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  • 201.
    de Rooy, Diederik P C
    et al.
    Leiden University.
    Kalvesten, Johan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Health Sciences.
    Huizinga, Tom W J
    Leiden University.
    van der Helm-van Mil, Annette H M
    Leiden University.
    Loss of metacarpal bone density predicts RA development in recent-onset arthritis2012In: Rheumatology, ISSN 1462-0324, E-ISSN 1462-0332, Vol. 51, no 6, p. 1037-1041Article in journal (Refereed)
    Abstract [en]

    Objective. Serum samples taken before the onset of RA suggest that one of the first features of RA is BMD loss. We determined the ability of radiographic BMD loss to predict RA development and arthritis persistency in patients with early undifferentiated arthritis (UA). less thanbrgreater than less thanbrgreater thanMethods. Five hundred and seventeen patients with early UA, included in the Leiden Early Arthritis Clinic, were assessed. Of these, 101 had hand radiographs made at first visit as well as after 6 months. BMD loss was measured using digital X-ray radiogrammetry (DXR) online. The outcome measures fulfilled the 1987 ACR criteria for RA after 1 year and arthritis persistency during a mean follow-up of 7 years. Additionally, it was assessed whether BMD measurements improved predictions compared with a validated prediction rule. less thanbrgreater than less thanbrgreater thanResults. A total of 53.8% of UA patients developed RA and 67.5% had persistent disease after 7 years follow-up. Highly elevated BMD loss (epsilon 2.5 mg/cm(2)/month) was present in 16.3% of patients and associated with RA development [odds ratio (OR) 6.1, 95% CI 1.2, 29.2, positive predictive value (PPV) 85%, negative predictive value (NPV) 52%, sensitivity 26%, specificity 95%]. BMD loss may have an independent effect of anti-CCP when tested in a logistic regression analysis (OR 4.1, 95% CI 0.8, 21.2), although the CI is large. All UA patients that were unclassified with the prediction rule and had highly elevated BMD loss progressed to RA. BMD loss was not significantly associated with arthritis persistency (HR = 0.56, 95% CI 0.14, 2.29). less thanbrgreater than less thanbrgreater thanConclusion. Present data suggest that BMD loss predicts RA development. These findings need to be verified in larger studies.

  • 202.
    deSouza, Nandita M.
    et al.
    Cancer Res UK Imaging Ctr, England; Royal Marsden Hosp, England.
    Achten, Eric
    Ghent Univ Hosp, Belgium.
    Alberich-Bayarri, Angel
    QUIBIM SL Fe Hlth Res Inst, Spain.
    Bamberg, Fabian
    Univ Freiburg, Germany.
    Boellaard, Ronald
    Vrije Univ Amsterdam, Netherlands.
    Clement, Olivier
    Hop Europeen Georges Pompidou, France.
    Fournier, Laure
    Hop Europeen Georges Pompidou, France.
    Gallagher, Ferdia
    Univ Cambridge, England.
    Golay, Xavier
    UCL Inst Neurol, England.
    Heussel, Claus Peter
    Heidelberg Univ, Germany.
    Jackson, Edward F.
    Univ Wisconsin, WI USA.
    Manniesing, Rashindra
    Radboud Univ Nijmegen, Netherlands.
    Mayerhofer, Marius E.
    Med Univ Vienna, Austria.
    Neri, Emanuele
    Univ Pisa, Italy.
    OConnor, James
    Univ Manchester, England.
    Oguz, Kader Karli
    Hacettepe Univ Hosp, Turkey.
    Persson, Anders
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Smits, Marion
    Erasmus MC, Netherlands.
    van Beek, Edwin J. R.
    Queens Med Res Inst, Scotland.
    Zech, Christoph J.
    Univ Basel, Switzerland.
    Validated imaging biomarkers as decision-making tools in clinical trials and routine practice: current status and recommendations from the EIBALL* subcommittee of the European Society of Radiology (ESR)2019In: Insight into Imaging, ISSN 1869-4101, E-ISSN 1869-4101, Vol. 10, no 1, article id UNSP 87Article in journal (Refereed)
    Abstract [en]

    Observer-driven pattern recognition is the standard for interpretation of medical images. To achieve global parity in interpretation, semi-quantitative scoring systems have been developed based on observer assessments; these are widely used in scoring coronary artery disease, the arthritides and neurological conditions and for indicating the likelihood of malignancy. However, in an era of machine learning and artificial intelligence, it is increasingly desirable that we extract quantitative biomarkers from medical images that inform on disease detection, characterisation, monitoring and assessment of response to treatment. Quantitation has the potential to provide objective decision-support tools in the management pathway of patients. Despite this, the quantitative potential of imaging remains under-exploited because of variability of the measurement, lack of harmonised systems for data acquisition and analysis, and crucially, a paucity of evidence on how such quantitation potentially affects clinical decision-making and patient outcome. This article reviews the current evidence for the use of semi-quantitative and quantitative biomarkers in clinical settings at various stages of the disease pathway including diagnosis, staging and prognosis, as well as predicting and detecting treatment response. It critically appraises current practice and sets out recommendations for using imaging objectively to drive patient management decisions.

  • 203.
    Diczfalusy, Elin
    et al.
    Linköping University, Department of Biomedical Engineering, Biomedical Instrumentation. Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Wårdell, Karin
    Linköping University, Department of Biomedical Engineering, Biomedical Instrumentation. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    A diffusion tensor-based finite element model of microdialysis in the deep brain2015In: Computer Methods in Biomechanics and Biomedical Engineering, ISSN 1025-5842, E-ISSN 1476-8259, Vol. 18, no 2, p. 201-212Article in journal (Refereed)
    Abstract [en]

    Microdialysis of the basal ganglia was recently used to study neurotransmitter levels in relation to deep brain stimulation. In order to estimate the anatomical origin of the obtained data, the maximum tissue volume of influence (TVImax) for a microdialysis catheter was simulated using the finite element method. This study investigates the impact of brain heterogeneity and anisotropy on the TVImax using diffusion tensor imaging (DTI) to create a second-order tensor model of the basal ganglia. Descriptive statistics showed that the maximum migration distance for neurotransmitters varied by up to 55% (n = 98,444) for DTI-based simulations compared with an isotropic reference model, and the anisotropy differed between different targets in accordance with theory. The size of the TVImax was relevant in relation to the size of the anatomical structures of interest, and local tissue properties should be accounted for when relating microdialysis data to their anatomical targets.

  • 204.
    Drissi, Natasha Morales
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Brain Networks and Dynamics in Narcolepsy2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Narcolepsy is a chronic sleep disorder, characterised by excessive daytime sleepiness with frequent uncontrollable sleep attacks. In addition to sleeprelated problems, changes in cognition have also been observed in patients with narcolepsy and has been linked to the loss of Orexin-A in a number of studies. Results from previous functional and structural neuroimaging studies would suggest that the loss of Orexin-A has numerous downstream effects in terms of both resting state glucose metabolism and perfusion and reduction in cortical grey matter.

    Specifically, studies investigating narcolepsy with positron emission tomography (PET) and single photon emission computed tomography (SPECT) have observed aberrant perfusion and glucose metabolism in the hypothalamus and thalamus, as well as in prefrontal cortex. A very recent PET study in a large cohort of adolescents with type 1 narcolepsy further observed that the hypoand hypermetabolism in many of these cortico-frontal and subcortical brain regions also exhibited significant correlations with performance on a number of neurocognitive tests. These findings parallel those found in structural neuroimaging studies, where a reduction of cortical grey matter in frontotemporal areas has been observed.

    The Aim of this thesis was to investigate mechanisms and aetiology behind the symptoms in narcolepsy through the application of different neuroimaging techniques. I present in this thesis evidence supporting that the complaints about subjective memory deficits in narcolepsy are related to a misallocation of resources.

    I further describe how this has its seat in defective default mode network activation, possibly involving alterations to GABA and Glutamate signaling. In addition to this, I present our findings of a structural deviation in an area of the brainstem previously not described in the aetiology of narcolepsy.

    This finding may have implications for further understanding the aetiology of the disease and the specific neuronal populations involved.

    In addition to this, I show evidence from adipose tissue measurements in specific compartments, confirming that weight gain in narcolepsy is characterized by centrally located weight gain and may be specifically related to OX changes, but maybe not brown adipose tissue volume.

    The findings presented in this thesis provides new insights to the pathophysiology of narcolepsy beyond the well-known depletion of OX producing neurons in the hypothalamus.

    List of papers
    1. Altered Brain Microstate Dynamics in Adolescents with Narcolepsy
    Open this publication in new window or tab >>Altered Brain Microstate Dynamics in Adolescents with Narcolepsy
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    2016 (English)In: Frontiers in Human Neuroscience, ISSN 1662-5161, E-ISSN 1662-5161, Vol. 10, no 369Article in journal (Refereed) Published
    Abstract [en]

    Narcolepsy is a chronic sleep disorder caused by a loss of hypocretin-1 producing neurons in the hypothalamus. Previous neuroimaging studies have investigated brain function in narcolepsy during rest using positron emission tomography (PET) and single photon emission computed tomography (SPECT). In addition to hypothalamic and thalamic dysfunction they showed aberrant prefrontal perfusion and glucose metabolism in narcolepsy. Given these findings in brain structure and metabolism in narcolepsy, we anticipated that changes in functional magnetic resonance imaging (fMRI) resting state network (RSN) dynamics might also be apparent in patients with narcolepsy. The objective of this study was to investigate and describe brain microstate activity in adolescents with narcolepsy and correlate these to RSNs using simultaneous fMRI and electroencephalography (EEG). Sixteen adolescents (ages 13-20) with a confirmed diagnosis of narcolepsy were recruited and compared to age-matched healthy controls. Simultaneous EEG and fMRI data were collected during 10 min of wakeful rest. EEG data were analyzed for microstates, which are discrete epochs of stable global brain states obtained from topographical EEG analysis. Functional fMRI data were analyzed for RSNs. Data showed that narcolepsy patients were less likely than controls to spend time in a microstate which we found to be related to the default mode network and may suggest a disruption of this network that is disease specific. We concluded that adolescents with narcolepsy have altered resting state brain dynamics.

    Place, publisher, year, edition, pages
    FRONTIERS MEDIA SA, 2016
    Keywords
    narcolepsy; default mode network; functional magnetic resonance imaging (fMRI); electroencephalography (EEG); microstates; resting state networks; orexin; sleep
    National Category
    Neurology
    Identifiers
    urn:nbn:se:liu:diva-131167 (URN)10.3389/fnhum.2016.00369 (DOI)000380989900001 ()27536225 (PubMedID)
    Note

    Funding Agencies|Research Council of South East Sweden (FORSS); Knut and Alice Wallenberg foundation (KAW); strategic research area of systems neurobiology at Linkoping University; Country council of Ostergotland Sweden

    Available from: 2016-09-20 Created: 2016-09-12 Last updated: 2019-01-04
    2. Evidence for cognitive resource imbalance in adolescents with narcolepsy
    Open this publication in new window or tab >>Evidence for cognitive resource imbalance in adolescents with narcolepsy
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    2018 (English)In: Brain Imaging and Behavior, ISSN 1931-7557, E-ISSN 1931-7565, Vol. 12, no 2, p. 411-424Article in journal (Refereed) Published
    Abstract [en]

    The study investigated brain activity changes during performance of a verbal working memory task in a population of adolescents with narcolepsy. Seventeen narcolepsy patients and twenty healthy controls performed a verbal working memory task during simultaneous fMRI and EEG acquisition. All subjects also underwent MRS to measure GABA and Glutamate concentrations in the medial prefrontal cortex. Activation levels in the default mode network and left middle frontal gyrus were examined to investigate whether narcolepsy is characterized by an imbalance in cognitive resources. Significantly increased deactivation within the default mode network during task performance was observed for the narcolepsy patients for both the encoding and recognition phases of the task. No evidence for task performance deficits or reduced activation within the left middle frontal gyrus was noted for the narcolepsy patients. Correlation analyses between the spectroscopy and fMRI data indicated that deactivation of the anterior aspect of the default mode in narcolepsy patients correlated more with increased concentrations of Glutamate and decreased concentrations of GABA. In contrast, deactivation in the default mode was correlated with increased concentrations of GABA and decreased concentrations of Glutamate in controls. The results suggested that narcolepsy is not characterized by a deficit in working memory but rather an imbalance of cognitive resources in favor of monitoring and maintaining attention over actual task performance. This points towards dysregulation within the sustained attention system being the origin behind self-reported cognitive difficulties in narcolepsy.

    Place, publisher, year, edition, pages
    Springer-Verlag New York, 2018
    Keywords
    EEG, GABA, MRS, Narcolepsy, Working memory, fMRI
    National Category
    Radiology, Nuclear Medicine and Medical Imaging
    Identifiers
    urn:nbn:se:liu:diva-145535 (URN)10.1007/s11682-017-9706-y (DOI)000429029000011 ()28321606 (PubMedID)2-s2.0-85015625386 (Scopus ID)
    Available from: 2018-03-05 Created: 2018-03-05 Last updated: 2019-05-01Bibliographically approved
    3. Unexpected Fat Distribution in Adolescents With Narcolepsy
    Open this publication in new window or tab >>Unexpected Fat Distribution in Adolescents With Narcolepsy
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    2018 (English)In: Frontiers in Endocrinology, ISSN 1664-2392, E-ISSN 1664-2392, Vol. 9, article id 728Article in journal (Refereed) Published
    Abstract [en]

    Narcolepsy type 1 is a chronic sleep disorder with significantly higher BMI reported in more than 50% of adolescent patients, putting them at a higher risk for metabolic syndrome in adulthood. Although well-documented, the body fat distribution and mechanisms behind weight gain in narcolepsy are still not fully understood but may be related to the loss of orexin associated with the disease. Orexin has been linked to the regulation of brown adipose tissue (BAT), a metabolically active fat involved in energy homeostasis. Previous studies have used BMI and waist circumference to characterize adipose tissue increases in narcolepsy but none have investigated its specific distribution. Here, we examine adipose tissue distribution in 19 adolescent patients with narcolepsy type 1 and compare them to 17 of their healthy peers using full body magnetic resonance imaging (MRI). In line with previous findings we saw that the narcolepsy patients had more overall fat than the healthy controls, but contrary to our expectations there were no group differences in supraclavicular BAT, suggesting that orexin may have no effect at all on BAT, at least under thermoneutral conditions. Also, in line with previous reports, we observed that patients had more total abdominal adipose tissue (TAAT), however, we found that they had a lower ratio between visceral adipose tissue (VAT) and TAAT indicating a relative increase of subcutaneous abdominal adipose tissue (ASAT). This relationship between VAT and ASAT has been associated with a lower risk for metabolic disease. We conclude that while weight gain in adolescents with narcolepsy matches that of central obesity, the lower VAT ratio may suggest a lower risk of developing metabolic disease.

    Place, publisher, year, edition, pages
    FRONTIERS MEDIA SA, 2018
    Keywords
    orexin; hypocretin; brown adipose tissue; visceral adipose tissue; subcutaneous adipose tissue; BMI; magnetic resonance imaging (MRI); obesity
    National Category
    Endocrinology and Diabetes
    Identifiers
    urn:nbn:se:liu:diva-153502 (URN)10.3389/fendo.2018.00728 (DOI)000452268600001 ()
    Note

    Funding Agencies|Research Council of South East Sweden [FORSS-480551]; Knut and Alice Wallenberg foundation [KAW 2013.0076]

    Available from: 2019-01-02 Created: 2019-01-02 Last updated: 2019-06-14
  • 205.
    Dudman, N. P. B.
    et al.
    Department of Medicine, University of New South Wales, Prince Henry Hospital, Uttle Bay, UK.
    Wilcken, D. E.
    Department of Medicine, University of New South Wales, Prince Henry Hospital, Uttle Bay, UK.
    Wang, J.
    Department of Medicine, University of New South Wales, Prince Henry Hospital, Uttle Bay, UK.
    Lynch, J. F.
    Department of Medicine, University of New South Wales, Prince Henry Hospital, Uttle Bay, UK.
    Macey, D.
    Department of Medicine, University of New South Wales, Prince Henry Hospital, Uttle Bay, UK.
    Lundberg, Peter
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Department of Biochemistry, University of Sydney, Sydney (P.L.), Australia.
    Disordered methionine/homocysteine metabolism in premature vascular disease. Its occurrence, cofactor therapy, and enzymology1993In: Arteriosclerosis, Thrombosis and Vascular Biology, ISSN 1079-5642, E-ISSN 1524-4636, Vol. 13, no 9, p. 1253-1260Article in journal (Refereed)
    Abstract [en]

    Mild homocysteinemia occurs surprisingly often in patients with premature vascular disease. We studied the possible enzymatic sources of this mild hyperhomocysteinemia and the control of homocysteine levels in plasma by treatment of patients with the cofactors and cosubstrates of homocysteine catabolism. We assessed homocysteine metabolism in 131 patients who had premature disease in their coronary, peripheral, or cerebrovascular circulation by using a standard oral methionine-load test. Impaired homocysteine metabolism occurred in 28 patients. We assayed levels of the primary enzymes of homocysteine catabolism in cultured skin fibroblast extracts from 15 of these 28 patients. The patients' cystathionine beta-synthase levels (3.68 +/- 2.52 nmol/h per milligram of cell protein, mean +/- SD) were markedly depressed compared with those from 31 healthy adult control subjects (7.61 +/- 4.49, P < .001). The patients' levels of 5-methyltetrahydrofolate: homocysteine methyltransferase were normal. While betaine: homocysteine methyltransferase was not expressed in skin fibroblasts, 24-hour urinary betaine and N,N-dimethylglycine measurements were consistent with normal or enhanced remethylation of homocysteine by betaine: homocysteine methyltransferase in the 13 patients tested. When treated daily with choline and betaine, pyridoxine, or folic acid, there was a normalization of the postmethionine plasma homocysteine level in 16 of 19 patients. Our results indicate that mild homocysteinemia in premature vascular disease may be caused by either a folate deficiency or deficiencies in cystathionine beta-synthase activity. It does not necessarily involve deficiencies of either 5-methyltetrahydrofolate:homocysteine methyltransferase or betaine:homocysteine methyltransferase. Effective treatment regimens are also defined.

  • 206.
    Dyverfeldt, Petter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Linköping University, Department of Management and Engineering, Applied Thermodynamics and Fluid Mechanics. Linköping University, The Institute of Technology.
    Extending MRI to the Quantification of Turbulence Intensity2010Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    In cardiovascular medicine, the assessment of blood flow is fundamental to the understanding and detection of disease. Many pharmaceutical, interventional, and surgical treatments impact the flow. The primary purpose of the cardiovascular system is to drive, control and maintain blood flow to all parts of the body. In the normal cardiovascular system, fluid transport is maintained at high efficiency and the blood flow is essentially laminar. Disturbed and turbulent blood flow, on the other hand, appears to be present in many cardiovascular diseases and may contribute to their initiation and progression. Despite strong indications of an important interrelationship between flow and cardiovascular disease, medical imaging has lacked a non-invasive tool for the in vivo assessment of disturbed and turbulent flow. As a result, the extent and role of turbulence in the blood flow of humans have not yet been fully investigated.

    Magnetic resonance imaging (MRI) is a versatile tool for the non-invasive assessment of flow and has several important clinical and research applications, but might not yet have reached its full potential. Conventional MRI techniques for the assessment of flow are based on measurements of the mean velocity within an image voxel. The mean velocity corresponds to the first raw moment of the distribution of velocities within a voxel. An MRI framework for the quantification of any moment (mean, standard deviation, skew, etc.) of arbitrary velocity distributions is presented in this thesis.

    Disturbed and turbulent flows are characterized by velocity fluctuations that are superimposed on the mean velocity. The intensity of these velocity fluctuations can be quantified by their standard deviation, which is a commonly used measure of turbulence intensity. This thesis focuses on the development of a novel MRI method for the quantification of turbulence intensity. This method is mathematically derived and experimentally validated. Limitations and sources of error are investigated and guidelines for adequate application of MRI measurements of turbulence intensity are outlined. Furthermore, the method is adapted to the quantification of turbulence intensity in the pulsatile blood flow of humans and applied to a wide range of cardiovascular diseases. In these applications, elevated turbulence intensity was consistently detected in regions where highly disturbed flow was anticipated, and the effects of potential sources of errors were small.

    Diseased heart valves are often replaced with prosthetic heart valves, which, in spite of improved benefits and durability, continue to fall short of matching native flow patterns. In an in vitro setting, MRI was used to visualize and quantify turbulence intensity in the flow downstream from four common designs of prosthetic heart valves. Marked differences in the extent and degree of turbulence intensity were detected between the different valves.

    Mitral valve regurgitation is a common valve lesion associated with progressive left atrial and left ventricular remodelling, which may often require surgical correction to avoid irreversible ventricular dysfunction. The spatiotemporal dynamics of flow disturbances in mitral regurgitation were assessed based on measurements of flow patterns and turbulence intensity in a group of patients with significant regurgitation arising from similar valve lesions. Peak turbulence intensity occurred at the same time in all patients and the total turbulence intensity in the left atrium appeared closely related to the severity of regurgitation.

    MRI quantification of turbulence intensity has the potential to become a valuable tool in investigating the extent, timing and role of disturbed blood flow in the human cardiovascular system, as well as in the assessment of the effects of different therapeutic options in patients with vascular or valvular disorders.

    List of papers
    1. Quantification of intravoxel velocity standard deviation and turbulence intensity by generalizing phase-contrast MRI
    Open this publication in new window or tab >>Quantification of intravoxel velocity standard deviation and turbulence intensity by generalizing phase-contrast MRI
    2006 (English)In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 56, no 4, p. 850-858Article in journal (Refereed) Published
    Abstract [en]

    Turbulent flow, characterized by velocity fluctuations, is a contributing factor to the pathogenesis of several cardiovascular diseases. A clinical noninvasive tool for assessing turbulence is lacking, however. It is well known that the occurrence of multiple spin velocities within a voxel during the influence of a magnetic gradient moment causes signal loss in phase-contrast magnetic resonance imaging (PC-MRI). In this paper a mathematical derivation of an expression for computing the standard deviation (SD) of the blood flow velocity distribution within a voxel is presented. The SD is obtained from the magnitude of PC-MRI signals acquired with different first gradient moments. By exploiting the relation between the SD and turbulence intensity (TI), this method allows for quantitative studies of turbulence. For validation, the TI in an in vitro flow phantom was quantified, and the results compared favorably with previously published laser Doppler anemometry (LDA) results. This method has the potential to become an important tool for the noninvasive assessment of turbulence in the arterial tree.

    Keywords
    phase-contrast magnetic resonance imaging, turbulent flow, intravoxel velocity distribution, turbulence intensity, atherosclerosis
    National Category
    Medical and Health Sciences Physiology Fluid Mechanics and Acoustics Medical Laboratory and Measurements Technologies
    Identifiers
    urn:nbn:se:liu:diva-37249 (URN)10.1002/mrm.21022 (DOI)000240897000017 ()34073 (Local ID)34073 (Archive number)34073 (OAI)
    Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2018-01-13
    2. On MRI turbulence quantification
    Open this publication in new window or tab >>On MRI turbulence quantification
    Show others...
    2009 (English)In: Magnetic Resonance Imaging, ISSN 0730-725X, E-ISSN 1873-5894, Vol. 27, no 7, p. 913-922Article in journal (Refereed) Published
    Abstract [en]

    Turbulent flow, characterized by velocity fluctuations, accompanies many forms of cardiovascular disease and may contribute to their progression and hemodynamic consequences. Several studies have investigated the effects of turbulence on the magnetic resonance imaging (MRI) signal. Quantitative MRI turbulence measurements have recently been shown to have great potential for application both in human cardiovascular flow and in engineering flow. In this article, potential pitfalls and sources of error in MRI turbulence measurements are theoretically and numerically investigated. Data acquisition strategies suitable for turbulence quantification are outlined. The results show that the sensitivity of MRI turbulence measurements to intravoxel mean velocity variations is negligible, but that noise may degrade the estimates if the turbulence encoding parameter is set improperly. Different approaches for utilizing a given amount of scan time were shown to influence the dynamic range and the uncertainty in the turbulence estimates due to noise. The findings reported in this work may be valuable for both in vitro and in vivo studies employing MRI methods for turbulence quantification.

    Keywords
    Turbulence quantification, Turbulent flow, Phase-contrast magnetic resonance imaging, Constriction, Numerical flow phantom
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-20746 (URN)10.1016/j.mri.2009.05.004 (DOI)000269613000004 ()
    Note

    Original Publication: Petter Dyverfeldt, Roland Gårdhagen, Andreas Sigfridsson, Matts Karlsson and Tino Ebbers, On MRI turbulence quantification, 2009, MAGNETIC RESONANCE IMAGING, (27), 7, 913-922. http://dx.doi.org/10.1016/j.mri.2009.05.004 Copyright: Elsevier Science B.V., Amsterdam. http://www.elsevier.com/

    Available from: 2009-09-18 Created: 2009-09-18 Last updated: 2017-12-13
    3. Assessment of fluctuating velocities in disturbed cardiovascular blood flow: in vivo feasibility of generalized phase-contrast MRI
    Open this publication in new window or tab >>Assessment of fluctuating velocities in disturbed cardiovascular blood flow: in vivo feasibility of generalized phase-contrast MRI
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    2008 (English)In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 28, no 3, p. 655-663Article in journal (Refereed) Published
    Abstract [en]

    Purpose

    To evaluate the feasibility of generalized phase-contrast magnetic resonance imaging (PC-MRI) for the noninvasive assessment of fluctuating velocities in cardiovascular blood flow.

    Materials and Methods

    Multidimensional PC-MRI was used in a generalized manner to map mean flow velocities and intravoxel velocity standard deviation (IVSD) values in one healthy aorta and in three patients with different cardiovascular diseases. The acquired data were used to assess the kinetic energy of both the mean (MKE) and the fluctuating (TKE) velocity field.

    Results

    In all of the subjects, both mean and fluctuating flow data were successfully acquired. The highest TKE values in the patients were found at sites characterized by abnormal flow conditions. No regional increase in TKE was found in the normal aorta.

    Conclusion

    PC-MRI IVSD mapping is able to detect flow abnormalities in a variety of human cardiovascular conditions and shows promise for the quantitative assessment of turbulence. This approach may assist in clarifying the role of disturbed hemodynamics in cardiovascular diseases.

    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-43135 (URN)10.1002/jmri.21475 (DOI)000259106900013 ()71980 (Local ID)71980 (Archive number)71980 (OAI)
    Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2017-12-13
    4. In Vitro Assessment of Flow Patterns and Turbulence Intensity in Prosthetic Heart Valves Using Generalized Phase-Contrast Magnetic Resonance Imaging
    Open this publication in new window or tab >>In Vitro Assessment of Flow Patterns and Turbulence Intensity in Prosthetic Heart Valves Using Generalized Phase-Contrast Magnetic Resonance Imaging
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    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    Purpose: To assess in vitro the three-dimensional mean velocity field and the extent and degree of turbulenceintensity in different prosthetic heart valves using a generalization of phase-contrast magnetic resonance imaging(PC-MRI).

    Material and Methods: Four 27 mm aortic valves (Björk-Shiley Monostrut tilting-disc, St. Jude MedicalStandard bileaflet, Medtronic Mosaic stented and Freestyle stentless porcine valve) were tested under steadyinflow conditions in a Plexiglas phantom. Three-dimensional PC-MRI data were acquired to measure the meanvelocity field and the turbulent kinetic energy (TKE), a direction-independent measure of turbulence intensity.

    Results: Velocity and turbulence intensity estimates could be obtained up and downstream of the valves, exceptwhere metallic structure in the valves caused signal void. Distinct differences in the location, extent and peakvalues of velocity and turbulence intensity were observed between the valves tested. The maximum values ofTKE varied between the different valves: tilting disc, 100 J/m3; bileaflet, 115 J/m3; stented, 200 J/m3; stentless,145 J/m3.

    Conclusion: The turbulence intensity downstream from a prosthetic heart valve is dependent on the specificvalve design. Generalized PC-MRI can be used to quantify velocity and turbulence intensity downstream fromprosthetic heart valves, which may allow assessment of these aspects of prosthetic valvular function inpostoperative patients.

    Keywords
    Turbulence intensity, prosthetic heart valves, phase-contrast magnetic resonance imaging
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-53189 (URN)
    Available from: 2010-01-19 Created: 2010-01-19 Last updated: 2013-09-03Bibliographically approved
    5. Hemodynamic aspects of mitral regurgitation assessed by generalized phase-contrast MRI
    Open this publication in new window or tab >>Hemodynamic aspects of mitral regurgitation assessed by generalized phase-contrast MRI
    Show others...
    2011 (English)In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 33, no 3, p. 582-588Article in journal (Refereed) Published
    Abstract [en]

    Purpose: Mitral regurgitation creates a high velocity jet into the left atrium (LA), contributing both volume andpressure; we hypothesized that the severity of regurgitation would be reflected in the degree of LA flowdistortion.

    Material and Methods: Three-dimensional cine PC-MRI was applied to determine LA flow patterns andturbulent kinetic energy (TKE) in seven subjects (five patients with posterior mitral leaflet prolapse, two normalsubjects). In addition, the regurgitant volume and the time-velocity profiles in the pulmonary veins weremeasured.

    Results: The LA flow in the mitral regurgitation patients was highly disturbed with elevated values of TKE.Peak TKE occurred consistently at late systole. The total LA TKE was closely related to the regurgitant volume.LA flow patterns were characterized by a pronounced vortex in proximity to the regurgitant jet. In some patients,pronounced discordances were observed between individual pulmonary venous inflows, but these could not berelated to the direction of the flow jet or parameters describing global LA hemodynamics.

    Conclusion: PC-MRI permits investigations of atrial and pulmonary vein flow patterns and TKE in significantmitral regurgitation, reflecting the impact of the highly disturbed blood flow that accompanies this importantvalve disease.

    Place, publisher, year, edition, pages
    John Wiley and Sons, 2011
    Keywords
    Hemodynamics, mitral valve insufficiency, turbulent flow, phase-contrast magnetic resonance imaging, pulmonary veins, blood flow velocity
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-53190 (URN)10.1002/jmri.22407 (DOI)000287951100009 ()
    Available from: 2010-01-19 Created: 2010-01-19 Last updated: 2017-12-12
  • 207.
    Dyverfeldt, Petter
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Bissell, Malenka
    University of Oxford, England.
    Barker, Alex J.
    Northwestern University, IL 60611 USA.
    Bolger, Ann F
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. University of Calif San Francisco, CA USA.
    Carlhäll, Carljohan
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ebbers, Tino
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Francios, Christopher J.
    University of Wisconsin, WI 53706 USA.
    Frydrychowicz, Alex
    University Hospital Schleswig Holstein, Germany.
    Geiger, Julia
    University of Childrens Hospital Zurich, Switzerland.
    Giese, Daniel
    University Hospital Cologne, Germany.
    Hope, Michael D.
    University of Calif San Francisco, CA USA.
    Kilner, Philip J.
    University of London Imperial Coll Science Technology and Med, England.
    Kozerke, Sebastian
    University of Zurich, Switzerland; ETH, Switzerland.
    Myerson, Saul
    University of Oxford, England.
    Neubauer, Stefan
    University of Oxford, England.
    Wieben, Oliver
    University of Wisconsin, WI 53706 USA.
    Markl, Michael
    Northwestern University, IL 60611 USA; Northwestern University, IL 60611 USA.
    4D flow cardiovascular magnetic resonance consensus statement2015In: Journal of Cardiovascular Magnetic Resonance, ISSN 1097-6647, E-ISSN 1532-429X, Vol. 17, no 72Article, review/survey (Refereed)
    Abstract [en]

    Pulsatile blood flow through the cavities of the heart and great vessels is time-varying and multidirectional. Access to all regions, phases and directions of cardiovascular flows has formerly been limited. Four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) has enabled more comprehensive access to such flows, with typical spatial resolution of 1.5x1.5x1.5 - 3x3x3 mm(3), typical temporal resolution of 30-40 ms, and acquisition times in the order of 5 to 25 min. This consensus paper is the work of physicists, physicians and biomedical engineers, active in the development and implementation of 4D Flow CMR, who have repeatedly met to share experience and ideas. The paper aims to assist understanding of acquisition and analysis methods, and their potential clinical applications with a focus on the heart and greater vessels. We describe that 4D Flow CMR can be clinically advantageous because placement of a single acquisition volume is straightforward and enables flow through any plane across it to be calculated retrospectively and with good accuracy. We also specify research and development goals that have yet to be satisfactorily achieved. Derived flow parameters, generally needing further development or validation for clinical use, include measurements of wall shear stress, pressure difference, turbulent kinetic energy, and intracardiac flow components. The dependence of measurement accuracy on acquisition parameters is considered, as are the uses of different visualization strategies for appropriate representation of time-varying multidirectional flow fields. Finally, we offer suggestions for more consistent, user-friendly implementation of 4D Flow CMR acquisition and data handling with a view to multicenter studies and more widespread adoption of the approach in routine clinical investigations.

  • 208.
    Dyverfeldt, Petter
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ebbers, Tino
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Comparison of Respiratory Motion Suppression Techniques for 4D Flow MRI2017In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 78, no 5, p. 1877-1882Article in journal (Refereed)
    Abstract [en]

    Purpose: The purpose of this work was to assess the impact of respiratory motion and to compare methods for suppression of respiratory motion artifacts in 4D Flow MRI. Methods: A numerical 3D aorta phantom was designed based on an aorta velocity field obtained by computational fluid mechanics. Motion-distorted 4D Flow MRI measurements were simulated and several different motion-suppression techniques were evaluated: Gating with fixed acceptance window size, gating with different window sizes in inner and outer kspace, and k-space reordering. Additionally, different spatial resolutions were simulated. Results: Respiratory motion reduced the image quality. All motion-suppression techniques improved the data quality. Flow rate errors of up to 30% without gating could be reduced to less than 2.5% with the most successful motion suppression methods. Weighted gating and gating combined with kspace reordering were advantageous compared with conventional fixed-window gating. Spatial resolutions finer than the amount of accepted motion did not lead to improved results. Conclusion: Respiratory motion affects 4D Flow MRI data. Several different motion suppression techniques exist that are capable of reducing the errors associated with respiratory motion. Spatial resolutions finer than the degree of accepted respiratory motion do not result in improved data quality. (C) 2017 International Society for Magnetic Resonance in Medicine.

  • 209.
    Dyverfeldt, Petter
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ebbers, Tino
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Letter by Dyverfeldt and Ebbers regarding article "Estimation of turbulent kinetic energy using 4D phase-contrast MRI: Effect of scan parameters and target vessel size"2016In: Magnetic Resonance Imaging, ISSN 0730-725X, E-ISSN 1873-5894, Vol. 34, no 8, p. 1226-1226Article in journal (Other academic)
    Abstract [en]

    n/a

  • 210.
    Dyverfeldt, Petter
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ebbers, Tino
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Länne, Toste
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Thoracic and Vascular Surgery. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Pulse wave velocity with 4D flow MRI: Systematic differences and age-related regional vascular stiffness2014In: Magnetic Resonance Imaging, ISSN 0730-725X, E-ISSN 1873-5894, Vol. 32, no 10, p. 1266-1271Article in journal (Refereed)
    Abstract [en]

    Purpose: The objective of this study was to compare multiple methods for estimation of PWV from 4D flow MRI velocity data and to investigate if 4D flow MRI-based PWV estimation with piecewise linear regression modeling of travel-distance vs. travel time is sufficient to discern age-related regional differences in PWV. Methods: 4D flow MRI velocity data were acquired in 8 young and Solder (age: 23 +/- 2 vs. 58 +/- 2 years old) normal volunteers. Travel-time and travel-distance were measured throughout the aorta and piecewise linear regression was used to measure global PWV in the descending aorta and regional PWV in three equally sized segments between the top of the aortic arch and the renal arteries. Six different methods for extracting travel-time were compared. Results: Methods for estimation of travel-time that use information about the whole flow waveform systematically overestimate PWV when compared to methods restricted to the upslope-portion of the waveforms (p less than 0.05). In terms of regional PWV, a significant interaction was found between age and location (p less than 0.05). The age-related differences in regional PWV were greater in the proximal compared to distal descending aorta. Conclusion: Care must be taken as different classes of methods for the estimation of travel-time produce different results. 4D flow MRI-based PWV estimation with piecewise linear regression modeling of travel-distance vs. travel time can discern age-related differences in regional PWV well in line with previously reported data.

  • 211.
    Dyverfeldt, Petter
    et al.
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Eriksson, Jonatan
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Sigfridsson, Andreas
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Escobar Kvitting, John-Peder
    Linköping University, Department of Medical and Health Sciences, Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Carlhäll, Carljohan
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Engvall, Jan
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Bolger, Ann F.
    University of California San Francisco, San Francisco, California, USA.
    Ebbers, Tino
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Extending 4D Flow Visualization to the Human Right Ventricle2009In: Proceedings of International Society for Magnetic Resonance in Medicine: 17th Scientific Meeting 2009, International Society for Magnetic Resonance in Medicine , 2009, p. 3860-3860Conference paper (Refereed)
    Abstract [en]

    The right ventricle has an important role in cardiovascular disease. However, because of the complex geometry and the sensitivity to the respiratory cycle, imaging of the right ventricle is challenging. We investigated whether 3D cine phase-contrast MRI can provide data with sufficient accuracy for visualizations of the 4D blood flow in the right ventricle. Whole-heart 4D flow measurements with optimized imaging parameters and post-processing tools were made in healthy volunteers. Pathlines emitted from the right atrium could be traced through the right ventricle to the pulmonary artery without leaving the blood pool and thereby met our criteria for sufficient accuracy.

  • 212.
    Dyverfeldt, Petter
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Escobar Kvitting, John Peder
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology. Linköping University, Department of Medicine and Health Sciences, Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Boano, G.
    Östergötlands Läns Landsting.
    Carlhäll, Carljohan
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Sigfridsson, Andreas
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Hermansson, Ulf
    Linköping University, Department of Medicine and Health Sciences, Thoracic Surgery. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Bolger, A.F.
    University of California, San Fransisco, San Franisco, California, United States.
    Engvall, Jan
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Ebbers, Tino
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Turbulence Mapping Extends the Utility of Phase-Contrast MRI in Mitral Valve Regurgitation2009In: Proc. Intl. Soc. Mag. Reson. Med., 2009, p. 3939-Conference paper (Refereed)
  • 213.
    Dyverfeldt, Petter
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Faculty of Health Sciences. Linköping University, Department of Management and Engineering, Applied Thermodynamics and Fluid Mechanics. Linköping University, The Institute of Technology. Linköping University, Department of Medical and Health Sciences, Physiology.
    Escobar Kvitting, John-Peder
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Carlhäll, Carl Johan
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Boano, Gabriella
    Östergötlands Läns Landsting, Heart Centre, Department of Cardiology.
    Sigfridsson, Andreas
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Hermansson, Ulf
    Linköping University, Department of Medical and Health Sciences, Thoracic Surgery. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Bolger, Ann F.
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Engvall, Jan
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Ebbers, Tino
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Faculty of Health Sciences. Linköping University, Department of Management and Engineering, Applied Thermodynamics and Fluid Mechanics. Linköping University, The Institute of Technology. Linköping University, Department of Medical and Health Sciences, Physiology.
    Hemodynamic aspects of mitral regurgitation assessed by generalized phase-contrast MRI2011In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 33, no 3, p. 582-588Article in journal (Refereed)
    Abstract [en]

    Purpose: Mitral regurgitation creates a high velocity jet into the left atrium (LA), contributing both volume andpressure; we hypothesized that the severity of regurgitation would be reflected in the degree of LA flowdistortion.

    Material and Methods: Three-dimensional cine PC-MRI was applied to determine LA flow patterns andturbulent kinetic energy (TKE) in seven subjects (five patients with posterior mitral leaflet prolapse, two normalsubjects). In addition, the regurgitant volume and the time-velocity profiles in the pulmonary veins weremeasured.

    Results: The LA flow in the mitral regurgitation patients was highly disturbed with elevated values of TKE.Peak TKE occurred consistently at late systole. The total LA TKE was closely related to the regurgitant volume.LA flow patterns were characterized by a pronounced vortex in proximity to the regurgitant jet. In some patients,pronounced discordances were observed between individual pulmonary venous inflows, but these could not berelated to the direction of the flow jet or parameters describing global LA hemodynamics.

    Conclusion: PC-MRI permits investigations of atrial and pulmonary vein flow patterns and TKE in significantmitral regurgitation, reflecting the impact of the highly disturbed blood flow that accompanies this importantvalve disease.

  • 214.
    Dyverfeldt, Petter
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Linköping University, Department of Management and Engineering, Applied Thermodynamics and Fluid Mechanics. Linköping University, The Institute of Technology.
    Escobar Kvitting, John-Peder
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Sigfridsson, Andreas
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Engvall, Jan
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Bolger, Ann F
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Ebbers, Tino
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Assessment of fluctuating velocities in disturbed cardiovascular blood flow: in vivo feasibility of generalized phase-contrast MRI2008In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 28, no 3, p. 655-663Article in journal (Refereed)
    Abstract [en]

    Purpose

    To evaluate the feasibility of generalized phase-contrast magnetic resonance imaging (PC-MRI) for the noninvasive assessment of fluctuating velocities in cardiovascular blood flow.

    Materials and Methods

    Multidimensional PC-MRI was used in a generalized manner to map mean flow velocities and intravoxel velocity standard deviation (IVSD) values in one healthy aorta and in three patients with different cardiovascular diseases. The acquired data were used to assess the kinetic energy of both the mean (MKE) and the fluctuating (TKE) velocity field.

    Results

    In all of the subjects, both mean and fluctuating flow data were successfully acquired. The highest TKE values in the patients were found at sites characterized by abnormal flow conditions. No regional increase in TKE was found in the normal aorta.

    Conclusion

    PC-MRI IVSD mapping is able to detect flow abnormalities in a variety of human cardiovascular conditions and shows promise for the quantitative assessment of turbulence. This approach may assist in clarifying the role of disturbed hemodynamics in cardiovascular diseases.

  • 215.
    Dyverfeldt, Petter
    et al.
    Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Management and Engineering, Applied Thermodynamics and Fluid Mechanics .
    Escobar Kvitting, John-Peder
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Health Sciences. Östergötlands Läns Landsting, Centre of Surgery and Oncology, Department of Surgery in Östergötland. Linköping University, Center for Medical Image Science and Visualization, CMIV.
    Sigfridsson, Andreas
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Engvall, Jan
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Bolger, Ann F
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Ebbers, Tino
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    In-vivo quantification of turbulent velocity fluctuations2007In: 15th Int Soc Magn Reson Med,2007, 2007Conference paper (Other academic)
  • 216.
    Dyverfeldt, Petter
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Escobar Kvitting, John-Peder
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Sigfridsson, Andreas
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Franzén, Stefan
    Linköping University, Department of Medicine and Health Sciences, Thoracic Surgery. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Bolger, Ann F.
    University of California San Fransisco, San Fransisco, California, United States.
    Ebbers, Tino
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    In-Vitro Turbulence Mapping in Prosthetic Heart Valves using Generalized Phase-Contrast MRI2009In: Proc. Intl. Soc. Mag. Reson. Med., 2009, p. 3941-Conference paper (Refereed)
  • 217.
    Dyverfeldt, Petter
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Gårdhagen, Roland
    Linköping University, Department of Management and Engineering, Applied Thermodynamics and Fluid Mechanics . Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology.
    Sigfridsson, Andreas
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Karlsson, Matts
    Linköping University, Department of Management and Engineering, Applied Thermodynamics and Fluid Mechanics . Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology.
    Ebbers, Tinno
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    MRI Turbulence Quantification2009In: Proc. Intl. Soc. Mag. Reson. Med., 2009, p. 1858-Conference paper (Refereed)
  • 218.
    Dyverfeldt, Petter
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Linköping University, Department of Management and Engineering, Applied Thermodynamics and Fluid Mechanics. Linköping University, The Institute of Technology.
    Gårdhagen, Roland
    Linköping University, Department of Management and Engineering, Applied Thermodynamics and Fluid Mechanics. Linköping University, The Institute of Technology.
    Sigfridsson, Andreas
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Karlsson, Matts
    Linköping University, Department of Management and Engineering, Applied Thermodynamics and Fluid Mechanics. Linköping University, The Institute of Technology.
    Ebbers, Tino
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Linköping University, Department of Management and Engineering, Applied Thermodynamics and Fluid Mechanics. Linköping University, The Institute of Technology.
    On MRI turbulence quantification2009In: Magnetic Resonance Imaging, ISSN 0730-725X, E-ISSN 1873-5894, Vol. 27, no 7, p. 913-922Article in journal (Refereed)
    Abstract [en]

    Turbulent flow, characterized by velocity fluctuations, accompanies many forms of cardiovascular disease and may contribute to their progression and hemodynamic consequences. Several studies have investigated the effects of turbulence on the magnetic resonance imaging (MRI) signal. Quantitative MRI turbulence measurements have recently been shown to have great potential for application both in human cardiovascular flow and in engineering flow. In this article, potential pitfalls and sources of error in MRI turbulence measurements are theoretically and numerically investigated. Data acquisition strategies suitable for turbulence quantification are outlined. The results show that the sensitivity of MRI turbulence measurements to intravoxel mean velocity variations is negligible, but that noise may degrade the estimates if the turbulence encoding parameter is set improperly. Different approaches for utilizing a given amount of scan time were shown to influence the dynamic range and the uncertainty in the turbulence estimates due to noise. The findings reported in this work may be valuable for both in vitro and in vivo studies employing MRI methods for turbulence quantification.

  • 219.
    Dyverfeldt, Petter
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Linköping University, Department of Management and Engineering, Applied Thermodynamics and Fluid Mechanics. Linköping University, The Institute of Technology.
    Sigfridsson, Andreas
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Escobar Kvitting, John-Peder
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Ebbers, Tino
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Linköping University, Department of Management and Engineering, Applied Thermodynamics and Fluid Mechanics. Linköping University, The Institute of Technology.
    Quantification of intravoxel velocity standard deviation and turbulence intensity by generalizing phase-contrast MRI2006In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 56, no 4, p. 850-858Article in journal (Refereed)
    Abstract [en]

    Turbulent flow, characterized by velocity fluctuations, is a contributing factor to the pathogenesis of several cardiovascular diseases. A clinical noninvasive tool for assessing turbulence is lacking, however. It is well known that the occurrence of multiple spin velocities within a voxel during the influence of a magnetic gradient moment causes signal loss in phase-contrast magnetic resonance imaging (PC-MRI). In this paper a mathematical derivation of an expression for computing the standard deviation (SD) of the blood flow velocity distribution within a voxel is presented. The SD is obtained from the magnitude of PC-MRI signals acquired with different first gradient moments. By exploiting the relation between the SD and turbulence intensity (TI), this method allows for quantitative studies of turbulence. For validation, the TI in an in vitro flow phantom was quantified, and the results compared favorably with previously published laser Doppler anemometry (LDA) results. This method has the potential to become an important tool for the noninvasive assessment of turbulence in the arterial tree.

  • 220.
    Dyverfeldt, Petter
    et al.
    Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Sigfridsson, Andreas
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Escobar Kvitting, John-Peder
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Ebbers, Tino
    Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Quantification of Turbulance Intensity by Generalizing Phase-Contrast MRI2006Conference paper (Refereed)
  • 221.
    Dyverfeldt, Petter
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Sigfridsson, Andreas
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Knutsson, Hans
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Ebbers, Tino
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    A Novel MRI Framework for the Quantification of Any Moment of Arbitrary Velocity Distributions.2010In: Proc. Intl. Soc. Mag. Reson. Med. 18 (2010), ISMRM , 2010, p. 1359-1359Conference paper (Other academic)
    Abstract [en]

    Under the assumption that the intravoxel velocity distribution is symmetric about its mean, the well-known MRI phase-difference method permits an estimation of the mean velocity of a voxel. The mean velocity corresponds to the first moment of the velocity distribution. Here, a novel framework for the quantification of any moment of arbitrary spin velocity distributions is presented. Simulations on realistic velocity distributions demonstrate its application. The presented moment framework may assist in improving the understanding of existing MRI methods for the quantification of flow and motion and serve as a basis for the development of new methods.

  • 222.
    Dyverfeldt, Petter
    et al.
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Sigfridsson, Andreas
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Ebbers, Tino
    Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology. Linköping University, Department of Medical and Health Sciences, Physiology. Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Management and Engineering, Applied Thermodynamics and Fluid Mechanics.
    A novel MRI framework for the quantification of any moment of arbitrary velocity distributions2011In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 65, no 3, p. 725-731Article in journal (Refereed)
    Abstract [en]

    MRI can measure several important hemodynamic parameters but might not yet have reached its full potential. The most common MRI method for the assessment of flow is phase-contrast MRI velocity mapping that estimates the mean velocity of a voxel. This estimation is precise only when the intravoxel velocity distribution is symmetric. The mean velocity corresponds to the first raw moment of the intravoxel velocity distribution. Here, a generalized MRI framework for the quantification of any moment of arbitrary velocity distributions is described. This framework is based on the fact that moments in the function domain (velocity space) correspond to differentials in the Fourier transform domain (kv-space). For proof-of-concept, moments of realistic velocity distributions were estimated using finite difference approximations of the derivatives of the MRI signal. In addition, the framework was applied to investigate the symmetry assumption underlying phase-contrast MRI velocity mapping; we found that this assumption can substantially affect phase-contrast MRI velocity estimates and that its significance can be reduced by increasing the velocity encoding range.

  • 223.
    Dyverfeldt, Petter
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Sigfridsson, Andreas
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Knutsson, Hans
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Ebbers, Tino
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    MR flow imaging beyond the mean velocity: Estimation of the skew  and kurtosis of intravoxel velocity distributions2011In: ISMRM 2011, International Society for Magnetic Resonance in Medicine ( ISMRM ) , 2011Conference paper (Other academic)
  • 224.
    Ebbers, Tino
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Physiology. Linköping University, Department of Management and Engineering, Applied Thermodynamics and Fluid Mechanics. Linköping University, The Institute of Technology. Östergötlands Läns Landsting, Heart and Medicine Centre, Department of Clinical Physiology UHL.
    Flow Imaging: Cardiac Applications of 3D Cine Phase-Contrast MRI2011In: Current Cardiovascular Imaging Reports, ISSN 1941-9074, Vol. 4, no 2, p. 127-133Article, review/survey (Refereed)
    Abstract [en]

    Global and regional blood flow dynamics are of pivotal importance to cardiac function. Fluid mechanical forces can affect hemolysis and platelet aggregation, as well as myocardial remodeling. In recent years, assessment of blood flow patterns based on time-resolved, three-dimensional, three-directional phase-contrast MRI (3D cine PC MRI) has become possible and rapidly gained popularity. Initially, this technique was mainly known for its intuitive and appealing visualizations of the cardiovascular blood flow. Most recently, the technique has begun to go beyond compelling images toward comprehensive and quantitative assessment of blood flow. In this article, cardiac applications of 3D cine PC MRI data are discussed, starting with a review of the acquisition and analysis techniques, and including descriptions of promising applications of cardiac 3D cine PC MRI for the clinical evaluation of myocardial, valvular, and vascular disorders.

  • 225.
    Ebbers, Tino
    et al.
    Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Dyverfeldt, Petter
    Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Sigfridsson, Andreas
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Escobar Kvitting, John-Peder
    Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences.
    Quantification of Mean and Fluctuating Flow2006Conference paper (Refereed)
  • 226.
    Ebbers, Tino
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Haraldsson, Henrik
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Dyverfeldt, Petter
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Sigfridsson, Andreas
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Warntjes, Marcel Jan Bertus
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Wigström, Lars
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences.
    Higher order weighted least-squares phase offset correction for improved accuracy in phase-contrast MRI2008Conference paper (Refereed)
    Abstract [en]

    Phase-contrast magnetic resonance imaging has the ability to accurately measure blood flow and myocardial velocities in the human body. Unwanted spatially varying phase offsets are, however, always present and may deteriorate the measurements significantly. Some of these phase offsets can be estimated based on the pulse sequence (1), but effects caused by eddy currents are more difficult to predict. A linear fit of the phase values is often estimated from either a number of manually defined areas containing stationary tissue or by semi-automatic detection of stationary tissue using the

  • 227.
    Eckerström, C.
    et al.
    Institute of Neuroscience and Physiology, Göteborg University, Sweden.
    Olsson, E.
    Department of Philosophy, Göteborg University, Sweden, Institute of Biomedicine, Göteborg University, Sweden.
    Borga, Magnus
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ekholm, S.
    Department of Radiology, University of Rochester Medical Center, United States.
    Ribbelin, S.
    Department of Radiology, Göteborg University, Sweden.
    Rolstad, S.
    Institute of Neuroscience and Physiology, Göteborg University, Sweden.
    Starck, G.
    Department of Radiology, Göteborg University, Sweden, Department of Radiation Physics, Göteborg University, Sweden.
    Edman, A.
    Edman, Å., Institute of Neuroscience and Physiology, Göteborg University, Sweden.
    Wallin, A.
    Institute of Neuroscience and Physiology, Göteborg University, Sweden.
    Malmgren, H.
    Department of Philosophy, Göteborg University, Sweden.
    Small baseline volume of left hippocampus is associated with subsequent conversion of MCI into dementia: The Göteborg MCI study2008In: Journal of the Neurological Sciences, ISSN 0022-510X, E-ISSN 1878-5883, Vol. 272, no 1-2, p. 48-59Article in journal (Refereed)
    Abstract [en]

    Background: Earlier studies have reported that hippocampal atrophy can to some extent predict which patients with mild cognitive impairment (MCI) will subsequently convert to dementia, and that converters have an enhanced rate of hippocampal volume loss. Objective: To further validate the hypothesis that hippocampal atrophy predicts conversion from MCI to dementia, to relate baseline hippocampal volume to different forms of dementia, and to investigate the role of hippocampal side differences and rate of volume loss over time. Patients: The subjects (N = 68) include patients with MCI at baseline and progression to dementia at the two-year follow-up (N = 21), stable MCI patients (N = 21), and controls (N = 26). Among the progressing patients, 13 were diagnosed as having AD. Methods: The Göteborg MCI study is a clinically based longitudinal study with biannual clinical assessments. Hippocampal volumetry was performed manually on the MRI investigations at baseline and at the two-year follow-up. Results: Hippocampal volumetry could predict conversion to dementia in both the AD and the non-AD subgroup of converters. Left hippocampal volume in particular discriminated between converting and stable MCI. Cut off points for individual discrimination were shown to be potentially useful. The converting MCI group had a significantly higher rate of hippocampal volume loss as compared to the stable MCI group. Conclusions: In MCI patients, hippocampal volumetry at baseline gives prognostic information about possible development of AD and non-AD dementia. Contrary to earlier studies, we found that left hippocampal volume has the best predictive power. Reliable predictions appear to be possible in many individual cases. © 2008 Elsevier B.V. All rights reserved.

  • 228. Edvardsson, Hannes
    et al.
    Smedby, Örjan
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Radiology. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology UHL. Linköping University, Center for Medical Image Science and Visualization, CMIV.
    Compact and efficient 3D shape description through radial function approximation2003In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 72, no 2, p. 89-97Article in journal (Refereed)
    Abstract [en]

    A fast and simple method for three-dimensional shape description is described. The method views a 3D object as a radial distance function on the unit sphere, and thus reduces the dimensionality of the description problem by one. The radial distance function is approximated by Fourier methods in the basis of the spherical harmonic polynomials. The necessary integration is carried out on the object boundary, rather than on the unit sphere. Consequently, there is no need of a parameterisation of the object surface. The description makes it possible to compare shapes in a computationally very simple way. Solutions on how to cope with translated and rotated objects are discussed. The method is developed for star-shaped objects, but is stable even if the input image is non-star-shaped. The method is tested in a data set from magnetic resonance imaging (MRI) of the brain. Potential medical applications are discussed. ⌐ 2002 Elsevier Science Ireland Ltd. All rights reserved.

  • 229.
    Ehsan Saffari, Seyed
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Sabzevar University of Medical Science, Iran.
    Love, Askell
    Lund University, Sweden; Landspitali University Hospital, Iceland; University of Iceland, Iceland.
    Fredrikson, Mats
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences.
    Smedby, Örjan
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV). KTH Royal Institute Technology, Sweden.
    Regression models for analyzing radiological visual grading studies - an empirical comparison2015In: BMC Medical Imaging, ISSN 1471-2342, E-ISSN 1471-2342, Vol. 15, no 49Article in journal (Refereed)
    Abstract [en]

    Background: For optimizing and evaluating image quality in medical imaging, one can use visual grading experiments, where observers rate some aspect of image quality on an ordinal scale. To analyze the grading data, several regression methods are available, and this study aimed at empirically comparing such techniques, in particular when including random effects in the models, which is appropriate for observers and patients. Methods: Data were taken from a previous study where 6 observers graded or ranked in 40 patients the image quality of four imaging protocols, differing in radiation dose and image reconstruction method. The models tested included linear regression, the proportional odds model for ordinal logistic regression, the partial proportional odds model, the stereotype logistic regression model and rank-order logistic regression (for ranking data). In the first two models, random effects as well as fixed effects could be included; in the remaining three, only fixed effects. Results: In general, the goodness of fit (AIC and McFaddens Pseudo R-2) showed small differences between the models with fixed effects only. For the mixed-effects models, higher AIC and lower Pseudo R-2 was obtained, which may be related to the different number of parameters in these models. The estimated potential for dose reduction by new image reconstruction methods varied only slightly between models. Conclusions: The authors suggest that the most suitable approach may be to use ordinal logistic regression, which can handle ordinal data and random effects appropriately.

  • 230. Eidenvall, Lars
    et al.
    Sjöberg, Birgitta Janero
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Ask, Per
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Loyd, Dan
    Linköping University, Department of Management and Engineering, Applied Thermodynamics and Fluid Mechanics. Linköping University, The Institute of Technology.
    Wranne, Bengt
    Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Two-dimensional color Doppler flow velocity profiles can be time corrected with an external ECG-delay device.1992In: Journal of the American Society of Echocardiography, ISSN 0894-7317, E-ISSN 1097-6795, Vol. 5, no 4, p. 405-413Article in journal (Refereed)
    Abstract [en]

    Although two-dimensional ultrasound color flow imaging is often considered to be a real-time technique, the acquisition time for two-dimensional color images may be up to 200 msec. Time correction is therefore necessary to obtain correct flow velocity profiles. We have developed a time-correction method in which a specially designed unit detects the QRS complex from the patient and creates a trig pulse that is delayed incrementally in relation to the QRS complex. This trig pulse controls the acquisition of the ultrasound images. A number of consecutively delayed images, with known incremental delay between the sweeps, can thus be stored in the memory of the echocardiograph and transferred digitally to a computer. The time-corrected flow velocity profile is obtained by interpolation of data from the time-delayed profiles. The system was evaluated in a Doppler string phantom test. With this technique it is possible to study time-corrected flow velocity profiles without the need to alter existing ultrasound Doppler equipment.

  • 231. Eidenvall, Lars
    et al.
    Sjöberg, Birgitta Janero
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Wranne, Bengt
    Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Loyd, Dan
    Linköping University, Department of Management and Engineering, Applied Thermodynamics and Fluid Mechanics. Linköping University, The Institute of Technology.
    Ask, Per
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    INFORMATION IN THE AORTIC BLOOD VELOCITY SIGNAL - A SIMULATION STUDY1991In: PROCEEDINGS OF THE ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOL 13, PTS 1-5, 1991, p. 2248-2249Conference paper (Refereed)
  • 232.
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Computational Medical Image Analysis: With a Focus on Real-Time fMRI and Non-Parametric Statistics2012Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Functional magnetic resonance imaging (fMRI) is a prime example of multi-disciplinary research. Without the beautiful physics of MRI, there wouldnot be any images to look at in the first place. To obtain images of goodquality, it is necessary to fully understand the concepts of the frequencydomain. The analysis of fMRI data requires understanding of signal pro-cessing, statistics and knowledge about the anatomy and function of thehuman brain. The resulting brain activity maps are used by physicians,neurologists, psychologists and behaviourists, in order to plan surgery andto increase their understanding of how the brain works.

    This thesis presents methods for real-time fMRI and non-parametric fMRIanalysis. Real-time fMRI places high demands on the signal processing,as all the calculations have to be made in real-time in complex situations.Real-time fMRI can, for example, be used for interactive brain mapping.Another possibility is to change the stimulus that is given to the subject, inreal-time, such that the brain and the computer can work together to solvea given task, yielding a brain computer interface (BCI). Non-parametricfMRI analysis, for example, concerns the problem of calculating signifi-cance thresholds and p-values for test statistics without a parametric nulldistribution.

    Two BCIs are presented in this thesis. In the first BCI, the subject wasable to balance a virtual inverted pendulum by thinking of activating theleft or right hand or resting. In the second BCI, the subject in the MRscanner was able to communicate with a person outside the MR scanner,through a virtual keyboard.

    A graphics processing unit (GPU) implementation of a random permuta-tion test for single subject fMRI analysis is also presented. The randompermutation test is used to calculate significance thresholds and p-values forfMRI analysis by canonical correlation analysis (CCA), and to investigatethe correctness of standard parametric approaches. The random permuta-tion test was verified by using 10 000 noise datasets and 1484 resting statefMRI datasets. The random permutation test is also used for a non-localCCA approach to fMRI analysis.

    List of papers
    1. Using Real-Time fMRI to Control a Dynamical System by Brain Activity Classification
    Open this publication in new window or tab >>Using Real-Time fMRI to Control a Dynamical System by Brain Activity Classification
    Show others...
    2009 (English)In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009: 12th International Conference, London, UK, September 20-24, 2009, Proceedings, Part I / [ed] Gerhard Goos, Juris Hartmanis and Jan van Leeuwen, Springer Berlin/Heidelberg, 2009, 1, p. 1000-1008Conference paper, Published paper (Refereed)
    Abstract [en]

    We present a method for controlling a dynamical system using real-time fMRI. The objective for the subject in the MR scanner is to balance an inverted pendulum by activating the left or right hand or resting. The brain activity is classified each second by a neural network and the classification is sent to a pendulum simulator to change the force applied to the pendulum. The state of the inverted pendulum is shown to the subject in a pair of VR goggles. The subject was able to balance the inverted pendulum during several minutes, both with real activity and imagined activity. In each classification 9000 brain voxels were used and the response time for the system to detect a change of activity was on average 2-4 seconds. The developments here have a potential to aid people with communication disabilities, such as locked in people. Another future potential application can be to serve as a tool for stroke and Parkinson patients to be able to train the damaged brain area and get real-time feedback for more efficient training.

    Place, publisher, year, edition, pages
    Springer Berlin/Heidelberg, 2009 Edition: 1
    Series
    Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 5761
    Keywords
    fMRI
    National Category
    Medical Image Processing
    Identifiers
    urn:nbn:se:liu:diva-54034 (URN)10.1007/978-3-642-04268-3_123 (DOI)000273617300123 ()978-3-642-04267-6 (ISBN)978-3-642-04268-3 (ISBN)
    Conference
    MICCAI 2009, 12th International Conference, London, UK, September 20-24, 2009
    Projects
    CADICS
    Note

    The original publication is available at www.springerlink.com: Anders Eklund, Henrik Ohlsson, Mats Andersson, Joakim Rydell, Anders Ynnerman and Hans Knutsson, Using Real-Time fMRI to Control a Dynamical System by Brain Activity Classification, 2009, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009, Lecture Notes in Computer Science, (5761/2009), 1000-1008. http://dx.doi.org/10.1007/978-3-642-04268-3_123 Copyright: Springer Science Business Media http://www.springerlink.com/

    Available from: 2010-02-19 Created: 2010-02-19 Last updated: 2018-01-25Bibliographically approved
    2. A Brain Computer Interface for Communication Using Real-Time fMRI
    Open this publication in new window or tab >>A Brain Computer Interface for Communication Using Real-Time fMRI
    Show others...
    2010 (English)In: Proceedings of the 20th International Conference on Pattern Recognition, Los Alamitos, CA, USA: IEEE Computer Society, 2010, p. 3665-3669Conference paper, Published paper (Refereed)
    Abstract [en]

    We present the first step towards a brain computer interface (BCI) for communication using real-time functional magnetic resonance imaging (fMRI). The subject in the MR scanner sees a virtual keyboard and steers a cursor to select different letters that can be combined to create words. The cursor is moved to the left by activating the left hand, to the right by activating the right hand, down by activating the left toes and up by activating the right toes. To select a letter, the subject simply rests for a number of seconds. We can thus communicate with the subject in the scanner by for example showing questions that the subject can answer. Similar BCI for communication have been made with electroencephalography (EEG). The subject then focuses on a letter while different rows and columns of the virtual keyboard are flashing and the system tries to detect if the correct letter is flashing or not. In our setup we instead classify the brain activity. Our system is neither limited to a communication interface, but can be used for any interface where five degrees of freedom is necessary.

    Place, publisher, year, edition, pages
    Los Alamitos, CA, USA: IEEE Computer Society, 2010
    Series
    International Conference on Pattern Recognition, ISSN 1051-4651
    Keywords
    Biomedical MRI, Medical image processing, Real-time systems
    National Category
    Biomedical Laboratory Science/Technology Control Engineering
    Identifiers
    urn:nbn:se:liu:diva-54038 (URN)10.1109/ICPR.2010.894 (DOI)978-1-4244-7542-1 (ISBN)
    Conference
    20th International Conference on Pattern Recognition, Istanbul, Turkey, 23-26 August 2010
    Note

    ©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Anders Eklund, Mats Andersson, Henrik Ohlsson, Anders Ynnerman and Hans Knutsson, A Brain Computer Interface for Communication Using Real-Time fMRI, 2010, Proceedings from the 20th International Conference on Pattern Recognition (ICPR), 3665-3669. http://dx.doi.org/10.1109/ICPR.2010.894

    Available from: 2010-02-19 Created: 2010-02-19 Last updated: 2015-09-22Bibliographically approved
    3. Using the Local Phase of the Magnitude of the Local Structure Tensor for Image Registration
    Open this publication in new window or tab >>Using the Local Phase of the Magnitude of the Local Structure Tensor for Image Registration
    2011 (English)In: Image Analysis: 17th Scandinavian Conference, SCIA 2011, Ystad, Sweden, May 2011. Proceedings / [ed] Anders Heyden, Fredrik Kahl, Springer Berlin/Heidelberg, 2011, Vol. 6688, p. 414-423Conference paper, Published paper (Refereed)
    Abstract [en]

    The need of image registration is increasing, especially in the medical image domain. The simplest kind of image registration is to match two images that have similar intensity. More advanced cases include the problem of registering images of different intensity, for which phase based algorithms have proven to be superior. In some cases the phase based registration will fail as well, for instance when the images to be registered do not only differ in intensity but also in local phase. This is the case if a dark circle in the reference image is a bright circle in the source image. While rigid registration algorithms can use other parts of the image to calculate the global transformation, this problem is harder to solve for non-rigid registration. The solution that we propose in this work is to use the local phase of the magnitude of the local structure tensor, instead of the local phase of the image intensity. By doing this, we achieve invariance both to the image intensity and to the local phase and thereby only use the structural information, i.e. the shapes of the objects, for registration.

    Place, publisher, year, edition, pages
    Springer Berlin/Heidelberg, 2011
    Series
    Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 6688/2011
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-69246 (URN)10.1007/978-3-642-21227-7_39 (DOI)000308543900039 ()978-3-642-21226-0 (ISBN)
    Conference
    Image Analysis 17th Scandinavian Conference, SCIA 2011, Ystad, Sweden, May 2011.
    Funder
    Swedish Research Council, 2007-4786
    Note

    The original publication is available at www.springerlink.com: Anders Eklund, Daniel Forsberg, Mats Andersson and Hans Knutsson, Using the Local Phase of the Magnitude of the Local Structure Tensor for Image Registration, 2011, Lecture Notes in Computer Science, (6688), 414-432. http://dx.doi.org/10.1007/978-3-642-21227-7_39 Copyright: Springer-verlag http://www.springerlink.com/

    Available from: 2011-06-20 Created: 2011-06-20 Last updated: 2018-02-08Bibliographically approved
    4. True 4D Image Denoising on the GPU
    Open this publication in new window or tab >>True 4D Image Denoising on the GPU
    2011 (English)In: International Journal of Biomedical Imaging, ISSN 1687-4188, E-ISSN 1687-4196, Vol. 2011Article in journal (Refereed) Published
    Abstract [en]

    The use of image denoising techniques is an important part of many medical imaging applications. One common application isto improve the image quality of low-dose, i.e. noisy, computed tomography (CT) data. The medical imaging domain has seen atremendous development during the last decades. It is now possible to collect time resolved volumes, i.e. 4D data, with a number ofmodalities (e.g. ultrasound (US), CT, magnetic resonance imaging (MRI)). While 3D image denoising previously has been appliedto several volumes independently, there has not been much work done on true 4D image denoising, where the algorithm considersseveral volumes at the same time (and not a single volume at a time). By using all the dimensions, it is for example possibleto remove some of the time varying reconstruction artefacts that exist in CT volumes. The problem with 4D image denoising,compared to 2D and 3D denoising, is that the computational complexity increases exponentially.In this paper we describe a novel algorithm for true 4D image denoising, based on local adaptive filtering, and how to implementit on the graphics processing unit (GPU). The algorithm was applied to a 4D CT heart dataset of the resolution 512 x 512 x 445 x 20.The result is that the GPU can complete the denoising in about 25 minutes if spatial filtering is used and in about 8 minutes if FFTbased filtering is used. The CPU implementation requires several days of processing time for spatial filtering and about 50 minutesfor FFT based filtering. Fast spatial filtering makes it possible to apply the denoising algorithm to larger datasets (compared to ifFFT based filtering is used). The short processing time increases the clinical value of true 4D image denoising significantly.

    Place, publisher, year, edition, pages
    Hindawi Publishing Corporation, 2011
    Keywords
    Image denoising, Graphics processing unit (GPU), 4D, Computed tomography (CT)
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-69678 (URN)10.1155/2011/952819 (DOI)
    Available from: 2011-07-13 Created: 2011-07-13 Last updated: 2017-12-08
    5. fMRI Analysis on the GPU - Possibilities and Challenges
    Open this publication in new window or tab >>fMRI Analysis on the GPU - Possibilities and Challenges
    2012 (English)In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 105, no 2, p. 145-161Article in journal (Refereed) Published
    Abstract [en]

    Functional magnetic resonance imaging (fMRI) makes it possible to non-invasively measure brain activity with high spatial resolution.There are however a number of issues that have to be addressed. One is the large amount of spatio-temporal data that needsto be processed. In addition to the statistical analysis itself, several preprocessing steps, such as slice timing correction and motioncompensation, are normally applied. The high computational power of modern graphic cards has already successfully been used forMRI and fMRI. Going beyond the first published demonstration of GPU-based analysis of fMRI data, all the preprocessing stepsand two statistical approaches, the general linear model (GLM) and canonical correlation analysis (CCA), have been implementedon a GPU. For an fMRI dataset of typical size (80 volumes with 64 x 64 x 22 voxels), all the preprocessing takes about 0.5 s on theGPU, compared to 5 s with an optimized CPU implementation and 120 s with the commonly used statistical parametric mapping(SPM) software. A random permutation test with 10 000 permutations, with smoothing in each permutation, takes about 50 s ifthree GPUs are used, compared to 0.5 - 2.5 h with an optimized CPU implementation. The presented work will save time forresearchers and clinicians in their daily work and enables the use of more advanced analysis, such as non-parametric statistics, bothfor conventional fMRI and for real-time fMRI.

    Place, publisher, year, edition, pages
    Elsevier, 2012
    Keywords
    Functional magnetic resonance imaging (fMRI), Graphics processing unit (GPU), CUDA, General linear model (GLM), Canonical correlation analysis (CCA), Random permutation test
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-69677 (URN)10.1016/j.cmpb.2011.07.007 (DOI)000300813600005 ()
    Note

    funding agencies|strategic research center MOVIII||Swedish foundation for strategic research (SSF)||Linnaeus center CADICS||Swedish research council||Linkoping University||

    Available from: 2011-07-13 Created: 2011-07-13 Last updated: 2017-12-08Bibliographically approved
    6. Fast Random Permutation Tests Enable Objective Evaluation of Methods for Single Subject fMRI Analysis
    Open this publication in new window or tab >>Fast Random Permutation Tests Enable Objective Evaluation of Methods for Single Subject fMRI Analysis
    2011 (English)In: International Journal of Biomedical Imaging, ISSN 1687-4188, E-ISSN 1687-4196Article in journal (Refereed) Published
    Abstract [en]

    Parametric statistical methods, such as Z-, t-, and F-values are traditionally employed in functional magnetic resonance imaging (fMRI) for identifying areas in the brain that are active with a certain degree of statistical significance. These parametric methods, however, have two major drawbacks. First, it is assumed that the observed data are Gaussian distributed and independent; assumptions that generally are not valid for fMRI data. Second, the statistical test distribution can be derived theoretically only for very simple linear detection statistics. With non-parametric statistical methods, the two limitations described above can be overcome. The major drawback of non-parametric methods is the computational burden with processing times ranging from hours to days, which so far have made them impractical for routine use in single subject fMRI analysis. In this work, it is shown how the computational power of cost-efficient Graphics Processing Units (GPUs) can be used to speed up random permutation tests. A test with 10 000 permutations takes less than a minute, making statistical analysis of advanced detection methods in fMRI practically feasible. To exemplify the permutation based approach, brain activity maps generated by the General Linear Model (GLM) and Canonical Correlation Analysis (CCA) are compared at the same significance level. During the development of the routines and writing of the paper, 3-4 years of processing time has been saved by using the GPU.

    Place, publisher, year, edition, pages
    Hindawi Publishing Corporation, 2011
    Keywords
    Functional magnetic resonance imaging (fMRI), Graphics processing unit (GPU), Non-parametric statistics, random permutation test, CUDA, General Linear Model (GLM), Canonical Correlation Analysis (CCA)
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-69680 (URN)10.1155/2011/627947 (DOI)
    Available from: 2011-07-14 Created: 2011-07-14 Last updated: 2017-12-08
    7. Does Parametric fMRI Analysis with SPM Yield Valid Results? - An Empirical Study of 1484 Rest Datasets
    Open this publication in new window or tab >>Does Parametric fMRI Analysis with SPM Yield Valid Results? - An Empirical Study of 1484 Rest Datasets
    Show others...
    2012 (English)In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 61, no 3, p. 565-578Article in journal (Refereed) Published
    Abstract [en]

    The validity of parametric functional magnetic resonance imaging (fMRI) analysis has only been reported for simulated data.Recent advances in computer science and data sharing make it possible to analyze large amounts of real fMRI data. In this study,1484 rest datasets have been analyzed in SPM8, to estimate true familywise error rates. For a familywise significance threshold of5%, significant activity was found in 1% - 70% of the 1484 rest datasets, depending on repetition time, paradigm and parametersettings. This means that parametric significance thresholds in SPM both can be conservative or very liberal. The main reason forthe high familywise error rates seems to be that the global AR(1) auto correlation correction in SPM fails to model the spectra ofthe residuals, especially for short repetition times. The findings that are reported in this study cannot be generalized to parametricfMRI analysis in general, other software packages may give different results. By using the computational power of the graphicsprocessing unit (GPU), the 1484 rest datasets were also analyzed with a random permutation test. Significant activity was thenfound in 1% - 19% of the datasets. These findings speak to the need for a better model of temporal correlations in fMRI timeseries.

    Place, publisher, year, edition, pages
    Elsevier, 2012
    Keywords
    Functional magnetic resonance imaging (fMRI), Familywise error rate, Random field theory, Non-parametric statistics, Random permutation test, Graphics processing unit (GPU)
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-76118 (URN)10.1016/j.neuroimage.2012.03.093 (DOI)000304729800006 ()22507229 (PubMedID)
    Note

    funding agencies|Linnaeus Center CADICS||Swedish Research Council||Neuroeconomic research group at Linkoping University||GPU hardware||

    Available from: 2012-03-28 Created: 2012-03-28 Last updated: 2017-12-07Bibliographically approved
    8. A Functional Connectivity Inspired Approach to Non-Local fMRI Analysis
    Open this publication in new window or tab >>A Functional Connectivity Inspired Approach to Non-Local fMRI Analysis
    2012 (English)In: Proceedings of the 19th IEEE International Conference on Image Processing (ICIP), 2012, IEEE conference proceedings, 2012, p. 1245-1248Conference paper, Published paper (Other academic)
    Abstract [en]

    We propose non-local analysis of functional magnetic resonanceimaging (fMRI) data in order to detect more brain activity.Our non-local approach combines the ideas of regularfMRI analysis with those of functional connectivity analysis,and was inspired by the non-local means algorithm thatcommonly is used for image denoising. We extend canonicalcorrelation analysis (CCA) based fMRI analysis to handlemore than one activity area, such that information fromdifferent parts of the brain can be combined. Our non-localapproach is compared to fMRI analysis by the general linearmodel (GLM) and local CCA, by using simulated as well asreal data.

    Place, publisher, year, edition, pages
    IEEE conference proceedings, 2012
    Series
    Image Processing, ISSN 1522-4880 ; 2012
    Keywords
    fMRI, non-local, CCA, functional connectivity, GPU
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-76119 (URN)10.1109/ICIP.2012.6467092 (DOI)978-1-4673-2532-5 (ISBN)978-1-4673-2534-9 (ISBN)
    Conference
    19th IEEE International Conference on Image Processing (ICIP), 2012, Sept. 30 2012-Oct. 3, Orlando, FL, USA
    Available from: 2012-03-28 Created: 2012-03-28 Last updated: 2013-08-28Bibliographically approved
  • 233.
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Repliker. ”Öppen vetenskap behöver inte kosta en enda krona”2016In: Dagens Nyheter, ISSN 1101-2447Article in journal (Other (popular science, discussion, etc.))
  • 234.
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Signal Processing for Robust and Real-Time fMRI With Application to Brain Computer Interfaces2010Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    It is hard to find another research field than functional magnetic resonance imaging (fMRI) that combines so many different areas of research. Without the beautiful physics of MRI we would not have any images to look at in the first place. To get images with good quality it is necessary to fully understand the concepts of the frequency domain. The analysis of fMRI data requires understanding of signal processing and statistics and also knowledge about the anatomy and function of the human brain. The resulting brain activity maps are used by physicians and neurologists in order to plan surgery and to increase their understanding of how the brain works.

    This thesis presents methods for signal processing of fMRI data in real-time situations. Real-time fMRI puts higher demands on the signal processing, than conventional fMRI, since all the calculations have to be made in realtime and in more complex situations. The result from the real-time fMRI analysis can for example be used to look at the subjects brain activity in real-time, for interactive planning of surgery or understanding of brain functions. Another possibility is to use the result in order to change the stimulus that is given to the subject, such that the brain and the computer can work together to solve a given task. These kind of setups are often called brain computer interfaces (BCI).

    Two BCI are presented in this thesis. In the first BCI the subject was able to balance a virtual inverted pendulum by thinking of activating the left or right hand or resting. In the second BCI the subject in the MR scanner was able to communicate with a person outside the MR scanner, through a communication interface.

    Since head motion is common during fMRI experiments it is necessary to apply image registration to align the collected volumes. To do image registration in real-time can be a challenging task, therefore how to implement a volume registration algorithm on a graphics card is presented. The power of modern graphic cards can also be used to save time in the daily clinical work, an example of this is also given in the thesis.

    Finally a method for calculating and incorporating a structural based certainty in the analysis of the fMRI data is proposed. The results show that the structural certainty helps to remove false activity that can occur due to head motion, especially at the edge of the brain.

    List of papers
    1. Using Real-Time fMRI to Control a Dynamical System by Brain Activity Classification
    Open this publication in new window or tab >>Using Real-Time fMRI to Control a Dynamical System by Brain Activity Classification
    Show others...
    2009 (English)In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009: 12th International Conference, London, UK, September 20-24, 2009, Proceedings, Part I / [ed] Gerhard Goos, Juris Hartmanis and Jan van Leeuwen, Springer Berlin/Heidelberg, 2009, 1, p. 1000-1008Conference paper, Published paper (Refereed)
    Abstract [en]

    We present a method for controlling a dynamical system using real-time fMRI. The objective for the subject in the MR scanner is to balance an inverted pendulum by activating the left or right hand or resting. The brain activity is classified each second by a neural network and the classification is sent to a pendulum simulator to change the force applied to the pendulum. The state of the inverted pendulum is shown to the subject in a pair of VR goggles. The subject was able to balance the inverted pendulum during several minutes, both with real activity and imagined activity. In each classification 9000 brain voxels were used and the response time for the system to detect a change of activity was on average 2-4 seconds. The developments here have a potential to aid people with communication disabilities, such as locked in people. Another future potential application can be to serve as a tool for stroke and Parkinson patients to be able to train the damaged brain area and get real-time feedback for more efficient training.

    Place, publisher, year, edition, pages
    Springer Berlin/Heidelberg, 2009 Edition: 1
    Series
    Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 5761
    Keywords
    fMRI
    National Category
    Medical Image Processing
    Identifiers
    urn:nbn:se:liu:diva-54034 (URN)10.1007/978-3-642-04268-3_123 (DOI)000273617300123 ()978-3-642-04267-6 (ISBN)978-3-642-04268-3 (ISBN)
    Conference
    MICCAI 2009, 12th International Conference, London, UK, September 20-24, 2009
    Projects
    CADICS
    Note

    The original publication is available at www.springerlink.com: Anders Eklund, Henrik Ohlsson, Mats Andersson, Joakim Rydell, Anders Ynnerman and Hans Knutsson, Using Real-Time fMRI to Control a Dynamical System by Brain Activity Classification, 2009, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009, Lecture Notes in Computer Science, (5761/2009), 1000-1008. http://dx.doi.org/10.1007/978-3-642-04268-3_123 Copyright: Springer Science Business Media http://www.springerlink.com/

    Available from: 2010-02-19 Created: 2010-02-19 Last updated: 2018-01-25Bibliographically approved
    2. Phase Based Volume Registration Using CUDA
    Open this publication in new window or tab >>Phase Based Volume Registration Using CUDA
    2010 (English)In: Acoustics Speech and Signal Processing (ICASSP), 2010, IEEE , 2010, p. 658-661Conference paper, Published paper (Refereed)
    Abstract [en]

    We present a method for fast phase based registration of volume data for medical applications. As the number of different modalities within medical imaging increases, it becomes more and more important with registration that works for a mixture of modalities. For these applications the phase based registration approach has proven to be superior. Today there seem to be two kinds of groups that work with medical image registration, one that works with refining of the registration algorithms and one that works with implementation of more simple algorithms on graphic cards for speeding up the algorithms. We put the work from these groups together and get the best from both worlds. We achieve a speedup of 10-30 compared to our CPU implementation, which makes fast phase based registration possible for large medical volumes.

    Place, publisher, year, edition, pages
    IEEE, 2010
    Series
    IEEE International Conference on Acoustics, Speech and Signal Processing. Proceedings, ISSN 1520-6149 ; 2010
    Keywords
    Image registration, local phase, CUDA, GPU
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-54035 (URN)10.1109/ICASSP.2010.5495134 (DOI)000287096000159 ()978-1-4244-4295-9 (ISBN)
    Conference
    The 35th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2010), March 14–19, Dallas, Texas, USA
    Note

    ©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Anders Eklund, Mats Andersson and Hans Knutsson, Phase Based Volume Registration Using CUDA, 2010, Proceedings of the 35th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2010), 658-661. http://dx.doi.org/10.1109/ICASSP.2010.5495134

    Available from: 2010-02-19 Created: 2010-02-19 Last updated: 2013-08-28Bibliographically approved
    3. Fast Phase Based Registration for Robust Quantitative MRI
    Open this publication in new window or tab >>Fast Phase Based Registration for Robust Quantitative MRI
    2010 (English)In: Proceedings of the annual meeting of the International Society for Magnetic Resonance in Medicine (ISMRM 2010), 2010Conference paper, Published paper (Other academic)
    Abstract [en]

    Quantitative magnetic resonance imaging has the major advantage that it handles absolute measurements of physical parameters. Quantitative MRI can for example be used to estimate the amount of different tissue types in the brain, but other applications are possible. Parameters such as relaxation rates R1 and R2 and proton density (PD) are independent of MR scanner settings and imperfections and hence are directly representative of the underlying tissue characteristics. Brain tissue quantification is an important aid for diagnosis of neurological diseases, such as multiple sclerosis (MS) and dementia. It is applied to estimate the volume of each tissue type, such as white tissue, grey tissue, myelin and cerebrospinal fluid (CSF). Tissue that deviates from normal values can be found automatically using computer aided diagnosis. In order for the quantification to have a clinical value, both the time in the MR scanner and the time for the data analysis have to be minimized. A challenge in MR quantification is to keep the scan time within clinically acceptable limits. The quantification method that we have used is based on the work by Warntjes et al.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-54037 (URN)
    Conference
    ISMRM Joint Annual Meeting, Stockholm, Sweden,1-7 May 2010
    Available from: 2010-02-19 Created: 2010-02-19 Last updated: 2013-08-28Bibliographically approved
    4. A Brain Computer Interface for Communication Using Real-Time fMRI
    Open this publication in new window or tab >>A Brain Computer Interface for Communication Using Real-Time fMRI
    Show others...
    2010 (English)In: Proceedings of the 20th International Conference on Pattern Recognition, Los Alamitos, CA, USA: IEEE Computer Society, 2010, p. 3665-3669Conference paper, Published paper (Refereed)
    Abstract [en]

    We present the first step towards a brain computer interface (BCI) for communication using real-time functional magnetic resonance imaging (fMRI). The subject in the MR scanner sees a virtual keyboard and steers a cursor to select different letters that can be combined to create words. The cursor is moved to the left by activating the left hand, to the right by activating the right hand, down by activating the left toes and up by activating the right toes. To select a letter, the subject simply rests for a number of seconds. We can thus communicate with the subject in the scanner by for example showing questions that the subject can answer. Similar BCI for communication have been made with electroencephalography (EEG). The subject then focuses on a letter while different rows and columns of the virtual keyboard are flashing and the system tries to detect if the correct letter is flashing or not. In our setup we instead classify the brain activity. Our system is neither limited to a communication interface, but can be used for any interface where five degrees of freedom is necessary.

    Place, publisher, year, edition, pages
    Los Alamitos, CA, USA: IEEE Computer Society, 2010
    Series
    International Conference on Pattern Recognition, ISSN 1051-4651
    Keywords
    Biomedical MRI, Medical image processing, Real-time systems
    National Category
    Biomedical Laboratory Science/Technology Control Engineering
    Identifiers
    urn:nbn:se:liu:diva-54038 (URN)10.1109/ICPR.2010.894 (DOI)978-1-4244-7542-1 (ISBN)
    Conference
    20th International Conference on Pattern Recognition, Istanbul, Turkey, 23-26 August 2010
    Note

    ©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Anders Eklund, Mats Andersson, Henrik Ohlsson, Anders Ynnerman and Hans Knutsson, A Brain Computer Interface for Communication Using Real-Time fMRI, 2010, Proceedings from the 20th International Conference on Pattern Recognition (ICPR), 3665-3669. http://dx.doi.org/10.1109/ICPR.2010.894

    Available from: 2010-02-19 Created: 2010-02-19 Last updated: 2015-09-22Bibliographically approved
    5. On Structural Based Certainty for Robust fMRI Analysis
    Open this publication in new window or tab >>On Structural Based Certainty for Robust fMRI Analysis
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    We present a method for obtaining and using a structural based certainty for robust functional magnetic resonance imaging (fMRI) analysis. In the area of fMRI it is common to see brain activity maps with activity at the edge of the brain. It is however a known fact that activity close to the edge of the brain can be due to head movement, since the voxels close to the edge will have a higher variance if they switch between being outside and inside the brain. To some extent this can be remedied by aligning each volume to a reference volume, by the means of volume registration. However, the problem with fMRI volumes is that the slices in the volume normally are taken at different timepoints, and motion between the slices can occur. We calculate a structural based certainty for each voxel, from a high resolution T1-weighted volume, and incorporate this certainty into the statistical analysis of the fMRI data. We show that our certainty approach removes a lot of false activity, both on simulated data and on real data.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-54039 (URN)
    Available from: 2010-02-19 Created: 2010-02-19 Last updated: 2013-08-28
  • 235.
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Öppen vetenskap behöver inte kosta en krona2017In: Svenska Dagbladet, ISSN 1101-2412Article in journal (Other (popular science, discussion, etc.))
  • 236.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Josephson, Camilla
    Linköping University, Department of Management and Engineering, Economics. Linköping University, Faculty of Arts and Sciences.
    Johannesson, Magnus
    Stockholm School of Economics.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Does Parametric fMRI Analysis with SPM Yield Valid Results? - An Empirical Study of 1484 Rest Datasets2012In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 61, no 3, p. 565-578Article in journal (Refereed)
    Abstract [en]

    The validity of parametric functional magnetic resonance imaging (fMRI) analysis has only been reported for simulated data.Recent advances in computer science and data sharing make it possible to analyze large amounts of real fMRI data. In this study,1484 rest datasets have been analyzed in SPM8, to estimate true familywise error rates. For a familywise significance threshold of5%, significant activity was found in 1% - 70% of the 1484 rest datasets, depending on repetition time, paradigm and parametersettings. This means that parametric significance thresholds in SPM both can be conservative or very liberal. The main reason forthe high familywise error rates seems to be that the global AR(1) auto correlation correction in SPM fails to model the spectra ofthe residuals, especially for short repetition times. The findings that are reported in this study cannot be generalized to parametricfMRI analysis in general, other software packages may give different results. By using the computational power of the graphicsprocessing unit (GPU), the 1484 rest datasets were also analyzed with a random permutation test. Significant activity was thenfound in 1% - 19% of the datasets. These findings speak to the need for a better model of temporal correlations in fMRI timeseries.

  • 237.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    4D Medical Image Processing with CUDA2012Conference paper (Other academic)
    Abstract [en]

    Learn how to do 4D image processing with CUDA, especially for medical imaging applications. In this session we will give a couple of examples of how 4D image processing can take advantage of the computational power of the GPU. We will present how to use the GPU for functional magnetic resonance imaging (fMRI) analysis and true 4D image denoising. Most of our examples use the GPU both to speedup the analysis and to visualize the results.

  • 238.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    A Functional Connectivity Inspired Approach to Non-Local fMRI Analysis2012In: Proceedings of the 19th IEEE International Conference on Image Processing (ICIP), 2012, IEEE conference proceedings, 2012, p. 1245-1248Conference paper (Other academic)
    Abstract [en]

    We propose non-local analysis of functional magnetic resonanceimaging (fMRI) data in order to detect more brain activity.Our non-local approach combines the ideas of regularfMRI analysis with those of functional connectivity analysis,and was inspired by the non-local means algorithm thatcommonly is used for image denoising. We extend canonicalcorrelation analysis (CCA) based fMRI analysis to handlemore than one activity area, such that information fromdifferent parts of the brain can be combined. Our non-localapproach is compared to fMRI analysis by the general linearmodel (GLM) and local CCA, by using simulated as well asreal data.

  • 239.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Fast Random Permutation Tests Enable Objective Evaluation of Methods for Single Subject fMRI Analysis2011In: International Journal of Biomedical Imaging, ISSN 1687-4188, E-ISSN 1687-4196Article in journal (Refereed)
    Abstract [en]

    Parametric statistical methods, such as Z-, t-, and F-values are traditionally employed in functional magnetic resonance imaging (fMRI) for identifying areas in the brain that are active with a certain degree of statistical significance. These parametric methods, however, have two major drawbacks. First, it is assumed that the observed data are Gaussian distributed and independent; assumptions that generally are not valid for fMRI data. Second, the statistical test distribution can be derived theoretically only for very simple linear detection statistics. With non-parametric statistical methods, the two limitations described above can be overcome. The major drawback of non-parametric methods is the computational burden with processing times ranging from hours to days, which so far have made them impractical for routine use in single subject fMRI analysis. In this work, it is shown how the computational power of cost-efficient Graphics Processing Units (GPUs) can be used to speed up random permutation tests. A test with 10 000 permutations takes less than a minute, making statistical analysis of advanced detection methods in fMRI practically feasible. To exemplify the permutation based approach, brain activity maps generated by the General Linear Model (GLM) and Canonical Correlation Analysis (CCA) are compared at the same significance level. During the development of the routines and writing of the paper, 3-4 years of processing time has been saved by using the GPU.

  • 240.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    fMRI Analysis on the GPU - Possibilities and Challenges2012In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 105, no 2, p. 145-161Article in journal (Refereed)
    Abstract [en]

    Functional magnetic resonance imaging (fMRI) makes it possible to non-invasively measure brain activity with high spatial resolution.There are however a number of issues that have to be addressed. One is the large amount of spatio-temporal data that needsto be processed. In addition to the statistical analysis itself, several preprocessing steps, such as slice timing correction and motioncompensation, are normally applied. The high computational power of modern graphic cards has already successfully been used forMRI and fMRI. Going beyond the first published demonstration of GPU-based analysis of fMRI data, all the preprocessing stepsand two statistical approaches, the general linear model (GLM) and canonical correlation analysis (CCA), have been implementedon a GPU. For an fMRI dataset of typical size (80 volumes with 64 x 64 x 22 voxels), all the preprocessing takes about 0.5 s on theGPU, compared to 5 s with an optimized CPU implementation and 120 s with the commonly used statistical parametric mapping(SPM) software. A random permutation test with 10 000 permutations, with smoothing in each permutation, takes about 50 s ifthree GPUs are used, compared to 0.5 - 2.5 h with an optimized CPU implementation. The presented work will save time forresearchers and clinicians in their daily work and enables the use of more advanced analysis, such as non-parametric statistics, bothfor conventional fMRI and for real-time fMRI.

  • 241.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Improving CCA based fMRI Analysis by Covariance Pooling - Using the GPU for Statistical Inference2011Conference paper (Other academic)
    Abstract [en]

    Canonical correlation analysis (CCA) is a statistical methodthat can be preferable to the general linear model (GLM) for analysisof functional magnetic resonance imaging (fMRI) data. There are,however, two problems with CCA based fMRI analysis. First, it is notfeasible to use a parametric approach to calculate an activity thresholdfor a certain signi cance level. Second, two covariance matrices need tobe estimated in each voxel, from a rather small number of time samples.We recently solved the rst problem by doing random permutation testson the graphics processing unit (GPU), such that the null distribution ofany maximum test statistics can be estimated in the order of minutes. Inthis paper we consider the second problem. We extend the idea of variancepooling, that previously has been used for the GLM, to covariancepooling to improve the estimates of the covariance matrices. Our GPUimplementation of random permutation tests is used to calculate signicance thresholds, which are needed to compare the di erent activitymaps in an objective way. The covariance pooling results in more robustestimates of the covariance matrices. The number of signi cantly activevoxels that are detected (thresholded at p = 0.05, corrected for multiplecomparisons) is increased with 40 - 120% (if 8 mm smoothing is appliedto the covariance estimates). Too much covariance pooling can howeverresult in a loss of small activity clusters, 7-10 mm of smoothing givesthe best results. The calculations that were made in order to generatethe results in this paper would have taken a total of about 65 days witha Matlab implementation and about 10 days with a multithreaded Cimplementation, with our multi-GPU implementation they took about 2hours. By using fast random permutation tests, suggested improvementsof existing methods for fMRI analysis can be evaluated in an objective way.

  • 242.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology.
    On Structural Based Certainty for Robust fMRI AnalysisManuscript (preprint) (Other academic)
    Abstract [en]

    We present a method for obtaining and using a structural based certainty for robust functional magnetic resonance imaging (fMRI) analysis. In the area of fMRI it is common to see brain activity maps with activity at the edge of the brain. It is however a known fact that activity close to the edge of the brain can be due to head movement, since the voxels close to the edge will have a higher variance if they switch between being outside and inside the brain. To some extent this can be remedied by aligning each volume to a reference volume, by the means of volume registration. However, the problem with fMRI volumes is that the slices in the volume normally are taken at different timepoints, and motion between the slices can occur. We calculate a structural based certainty for each voxel, from a high resolution T1-weighted volume, and incorporate this certainty into the statistical analysis of the fMRI data. We show that our certainty approach removes a lot of false activity, both on simulated data and on real data.

  • 243.
    Eklund, Anders
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Phase Based Volume Registration Using CUDA2010In: Acoustics Speech and Signal Processing (ICASSP), 2010, IEEE , 2010, p. 658-661Conference paper (Refereed)
    Abstract [en]

    We present a method for fast phase based registration of volume data for medical applications. As the number of different modalities within medical imaging increases, it becomes more and more important with registration that works for a mixture of modalities. For these applications the phase based registration approach has proven to be superior. Today there seem to be two kinds of groups that work with medical image registration, one that works with refining of the registration algorithms and one that works with implementation of more simple algorithms on graphic cards for speeding up the algorithms. We put the work from these groups together and get the best from both worlds. We achieve a speedup of 10-30 compared to our CPU implementation, which makes fast phase based registration possible for large medical volumes.

  • 244.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    True 4D Image Denoising on the GPU2011In: International Journal of Biomedical Imaging, ISSN 1687-4188, E-ISSN 1687-4196, Vol. 2011Article in journal (Refereed)
    Abstract [en]

    The use of image denoising techniques is an important part of many medical imaging applications. One common application isto improve the image quality of low-dose, i.e. noisy, computed tomography (CT) data. The medical imaging domain has seen atremendous development during the last decades. It is now possible to collect time resolved volumes, i.e. 4D data, with a number ofmodalities (e.g. ultrasound (US), CT, magnetic resonance imaging (MRI)). While 3D image denoising previously has been appliedto several volumes independently, there has not been much work done on true 4D image denoising, where the algorithm considersseveral volumes at the same time (and not a single volume at a time). By using all the dimensions, it is for example possibleto remove some of the time varying reconstruction artefacts that exist in CT volumes. The problem with 4D image denoising,compared to 2D and 3D denoising, is that the computational complexity increases exponentially.In this paper we describe a novel algorithm for true 4D image denoising, based on local adaptive filtering, and how to implementit on the graphics processing unit (GPU). The algorithm was applied to a 4D CT heart dataset of the resolution 512 x 512 x 445 x 20.The result is that the GPU can complete the denoising in about 25 minutes if spatial filtering is used and in about 8 minutes if FFTbased filtering is used. The CPU implementation requires several days of processing time for spatial filtering and about 50 minutesfor FFT based filtering. Fast spatial filtering makes it possible to apply the denoising algorithm to larger datasets (compared to ifFFT based filtering is used). The short processing time increases the clinical value of true 4D image denoising significantly.

  • 245.
    Eklund, Anders
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Ohlsson, Henrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ynnerman, Anders
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    A Brain Computer Interface for Communication Using Real-Time fMRI2010In: Proceedings of the 20th International Conference on Pattern Recognition, Los Alamitos, CA, USA: IEEE Computer Society, 2010, p. 3665-3669Conference paper (Refereed)
    Abstract [en]

    We present the first step towards a brain computer interface (BCI) for communication using real-time functional magnetic resonance imaging (fMRI). The subject in the MR scanner sees a virtual keyboard and steers a cursor to select different letters that can be combined to create words. The cursor is moved to the left by activating the left hand, to the right by activating the right hand, down by activating the left toes and up by activating the right toes. To select a letter, the subject simply rests for a number of seconds. We can thus communicate with the subject in the scanner by for example showing questions that the subject can answer. Similar BCI for communication have been made with electroencephalography (EEG). The subject then focuses on a letter while different rows and columns of the virtual keyboard are flashing and the system tries to detect if the correct letter is flashing or not. In our setup we instead classify the brain activity. Our system is neither limited to a communication interface, but can be used for any interface where five degrees of freedom is necessary.

  • 246.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Warntjes, Marcel
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Phase Based Volume Registration on the GPU with Application to Quantitative MRI2010Conference paper (Other academic)
    Abstract [en]

    We present a method for fast phase based registration of volume data for medical applications. As the number of different modalities within medical imaging increases, it becomes more and more important with registration that works for a mixture of modalities. For these applications the phase based registration approach has proven to be superior. Today there seem to be two kinds of groups that work with medical image registration, one that works with refining of the registration algorithms and one that works with implementation of more simple algorithms on graphic cards for speeding up the algorithms. We put the work from these groups together and get the best from both worlds. We achieve a speedup of 10-30 compared to our CPU implementation, which makes fast phase based registration possible for large medical volumes.

  • 247.
    Eklund, Anders
    et al.
    Virginia Tech Carilion Research Institute, Virginia Tech, Roanoke, USA.
    Dufort, Paul
    Department of Medical Imaging, University of Toronto, Toronto, Canada.
    Forsberg, Daniel
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    LaConte, Stephen
    Virginia Tech Carilion Research Institute, Virginia Tech, Roanoke, USA.
    Medical Image Processing on the GPU: Past, Present and Future2013In: Medical Image Analysis, ISSN 1361-8415, E-ISSN 1361-8423, Vol. 17, no 8, p. 1073-1094Article, review/survey (Refereed)
    Abstract [en]

    Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges.

  • 248.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Forsberg, Daniel
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Using the Local Phase of the Magnitude of the Local Structure Tensor for Image Registration2011In: Image Analysis: 17th Scandinavian Conference, SCIA 2011, Ystad, Sweden, May 2011. Proceedings / [ed] Anders Heyden, Fredrik Kahl, Springer Berlin/Heidelberg, 2011, Vol. 6688, p. 414-423Conference paper (Refereed)
    Abstract [en]

    The need of image registration is increasing, especially in the medical image domain. The simplest kind of image registration is to match two images that have similar intensity. More advanced cases include the problem of registering images of different intensity, for which phase based algorithms have proven to be superior. In some cases the phase based registration will fail as well, for instance when the images to be registered do not only differ in intensity but also in local phase. This is the case if a dark circle in the reference image is a bright circle in the source image. While rigid registration algorithms can use other parts of the image to calculate the global transformation, this problem is harder to solve for non-rigid registration. The solution that we propose in this work is to use the local phase of the magnitude of the local structure tensor, instead of the local phase of the image intensity. By doing this, we achieve invariance both to the image intensity and to the local phase and thereby only use the structural information, i.e. the shapes of the objects, for registration.

  • 249.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Friman, Ola
    Fraunhofer Mevis, Bremen, Germany.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    A GPU accelerated interactive interface for exploratory functional connectivity analysis of FMRI data2011In: Image Processing (ICIP), 2011, IEEE , 2011, p. 1589-1592Conference paper (Refereed)
    Abstract [en]

    Functional connectivity analysis is a way to investigate how different parts of the brain are connected and interact. A common measure of connectivity is the temporal correlation between a reference voxel time series and all the other time series in a functional MRI data set. An fMRI data set generally contains more than 20,000 within-brain voxels, making a complete correlation analysis between all possible combinations of voxels heavy to compute, store, visualize and explore. In this paper, a GPU-accelerated interactive tool for investigating functional connectivity in fMRI data is presented. A reference voxel can be moved by the user and the correlations to all other voxels are calculated in real-time using the graphics processing unit (GPU). The resulting correlation map is updated in real-time and visualized as a 3D volume rendering together with a high resolution anatomical volume. This tool greatly facilitates the search for interesting connectivity patterns in the brain.

  • 250.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Friman, Ola
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Comparing fMRI Activity Maps from GLM and CCA at the Same Significance Level by Fast Random Permutation Tests on the GPU2011Conference paper (Other academic)
    Abstract [en]

    Parametric statistical methods are traditionally employed in functional magnetic resonance imaging (fMRI) for identifying areas in the brain that are active with a certain degree of statistical significance. These parametric methods, however, have two major drawbacks. First, it isassumed that the observed data are Gaussian distributed and independent; assumptions that generally are not valid for fMRI data. Second, the statistical test distribution can be derived theoretically only for very simple linear detection statistics. In this work it is shown how the computational power of the Graphics Processing Unit (GPU) can be used to speedup non-parametric tests, such as random permutation tests. With random permutation tests it is possible to calculate significance thresholds for any test statistics. As an example, fMRI activity maps from the General Linear Model (GLM) and Canonical Correlation Analysis (CCA) are compared at the same significance level.

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