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  • 151.
    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.

  • 152.
    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.
    The effect of tissue heterogeneity and anisotropy on microdialysis of the deep brainManuscript (preprint) (Other academic)
    Abstract [en]

    Microdialysis of the basal ganglia was recently used to study changes of neurotransmitter levels in relation to deep brain stimulation (DBS). In order to estimate the anatomical origin of the microdialysis data, the maximum tissue volume of influence (TVImax) for a microdialysis catheter was simulated and visualized using the finite element method (FEM). In the current study the impact of brain heterogeneity and anisotropy on the TVImax was investigated, using diffusion tensor imaging (DTI) to create a second-order tensor model of the basal ganglia. The results were presented using descriptive statistics, indicating that the mean radius of the TVImax varied by up to 0.5 mm (n = 98444) for FEM simulations based on DTI compared to a homogeneous and isotropic reference model. The internal capsule and subthalamic area showed significantly higher anisotropy (p < 0.0001, n = 600) than the putamen and the globus pallidus, in accordance with theory. It was concluded that the size of the TVImax remained small enough to be relevant in relation to the anatomical structures of interest, and that local tissue properties should be accounted for when relating the microdialysis data to their anatomical targets.

  • 153.
    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.

  • 154.
    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.

  • 155.
    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.

  • 156.
    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.

  • 157.
    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)
  • 158.
    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.

  • 159.
    Edholm, Paul
    et al.
    n/a.
    Granlund, Gösta
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Petersson, C.
    n/a.
    Ectomography: A New Radiographic Method for Reproducing a Selected Slice of Varying Thickness1980In: Acta Radiologica, ISSN 0284-1851, E-ISSN 1600-0455, Vol. 21, no 4, p. 433-442Article in journal (Refereed)
    Abstract [en]

    The mathematical basis is described of a new radiographic method by which an arbitrarily thick layer of the patient may be reconstructed. The reconstruction is performed from at least 60 images of the volume under examination. Each of these images, which have to be in digital form, is subjected to a special filtration process of its spatial frequencies. The combination of all the images will form the resulting image of the layer--the ectomogram. The method has been analysed and tested in experiments simulated with a computer.

  • 160.
    Einarsson, Henrik
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Implementation and Performance Analysis of Filternets2006Independent thesis Basic level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Today Image acquisition equipment produces huge amounts of data that needs to be processed. Often the data describes signals with a dimensionality higher then 2, as with ordinary images. This introduce a problem when it comes to process this high dimensional data since ordinary signal processing tools are no longer suitable. New faster and more efficient tools need to be developed to fully exploit the advantages with e. g. a 3D CT-scan.

    One such tool is filternets, a layered networklike structure, which the signal propagates through. A filternet has three fundamental advantages which will decrease the filtering time. The network structure allows complex filter to be decomposed into simpler ones, intermediate result may be reused and filters may be implemented with very few nonzero coefficients (sparse filters).

    The aim of this study has been to create an implementation for filternets and optimize it with respect to execution time. Specially the possibility to use filternets that approximates a harmonic filterset for estimating orientation in 3D signals is investigated.

    Tests show that this method is up to about 30 times faster than a full filterset consisting of dense filters. They also show a slightly larger error in the estimated orientation compared with the dense filters, this error should however not limit the usability of the method.

  • 161.
    Ekdahl, Christer
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Infectious Diseases. Linköping University, Faculty of Health Sciences.
    Karlsson, Daniel
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Health Sciences.
    Wigertz, Ove
    Linköping University, Department of Molecular and Clinical Medicine, Infectious Diseases. Linköping University, Faculty of Health Sciences.
    Forsum, Urban
    Linköping University, Department of Clinical and Experimental Medicine, Clinical Microbiology. Linköping University, Faculty of Health Sciences.
    A study of the usage of a decision-support system for infective endocarditis2000In: Medical informatics and the Internet in medicine (Print), ISSN 1463-9238, E-ISSN 1464-5238, Vol. 25, no 1, p. 1-18Article in journal (Refereed)
    Abstract [en]

    The objective of this study was to examine a design for a World Wide Web-based decision-support system in use by clinically active physicians. A prototype implementation of the design concerned management of infective endocarditis patient cases. The design was based on an integration of hypertext and rule-based knowledge. In the study sessions, physicians in the field of internal medicine worked on managing authentic patient cases in a laboratory setting. Data was collected from interviews with the physicians using video recordings and stimulated recall technique. The qualitative data was analysed according to the constant comparative method in order to develop a model of the physicians' usage of the system. The resulting model describes perceived contributions and criteria for usefulness of the system. The ways the physicians used the system showed that it was able to provide patient-specific support for confirming clinical decisions, for higher-level patient management, and for preparing for and initiating expert consultations. Users also stated that new medical knowledge could be gained as a side effect of using the system.

  • 162.
    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
  • 163.
    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
  • 164.
    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.

  • 165.
    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.

  • 166.
    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.

  • 167.
    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.

  • 168.
    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.

  • 169.
    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.

  • 170.
    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.

  • 171.
    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.

  • 172.
    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.

  • 173.
    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.

  • 174.
    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.

  • 175.
    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.

  • 176.
    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.

  • 177.
    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.

  • 178.
    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.

  • 179.
    Eklund, Anders
    et al.
    Virginia Tech, Carilion Research Institute, USA.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Multivariate fMRI Analysis using Canonical Correlation Analysis instead of Classifiers, Comment on Todd et al2013In: figshare.comArticle in journal (Other academic)
    Abstract [en]

    Multivariate pattern analysis (MVPA) is a popular method for making inference about functional magnetic resonance imaging (fMRI) data. One approach is to train a classifier with voxels within a certain radius from the center voxel, to classify between different brain states. This approach is commonly known as the searchlight algorithm. As recently pointed out by Todd and colleagues, inference at the group level can however be confounded by the fact that the direction of the effect is lost if the per subject classification performance is used to generate group results. Here we show that canonical correlation analysis (CCA) can in some aspects be a better approach to multivariate fMRI analysis, than classification based analysis (CBA).

  • 180.
    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. Linköping University, Department of Computer and Information Science, Statistics.
    Nichols, Thomas
    Department of Statistics, University of Warwick, England.
    Andersson, Mats
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering.
    Knutsson, Hans
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering.
    Empirically Investigating the Statistical Validity of SPM, FSL and AFNI for Single Subject fMRI Analysis2015In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), IEEE conference proceedings, 2015, p. 1376-1380Conference paper (Refereed)
    Abstract [en]

    The software packages SPM, FSL and AFNI are the most widely used packages for the analysis of functional magnetic resonance imaging (fMRI) data. Despite this fact, the validity of the statistical methods has only been tested using simulated data. By analyzing resting state fMRI data (which should not contain specific forms of brain activity) from 396 healthy con- trols, we here show that all three software packages give in- flated false positive rates (4%-96% compared to the expected 5%). We isolate the sources of these problems and find that SPM mainly suffers from a too simple noise model, while FSL underestimates the spatial smoothness. These results highlight the need of validating the statistical methods being used for fMRI. 

  • 181.
    Eklund, Anders
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Science & Engineering.
    Nichols, Thomas
    University of Warwick, England.
    Knutsson, Hans
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering.
    Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates2016In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 113, no 28, p. 7900-7905Article in journal (Refereed)
    Abstract [en]

    The most widely used task functional magnetic resonance imaging (fMRI) analyses use parametric statistical methods that depend on a variety of assumptions. In this work, we use real resting-state data and a total of 3 million random task group analyses to compute empirical familywise error rates for the fMRI software packages SPM, FSL, and AFNI, as well as a nonparametric permutation method. For a nominal familywise error rate of 5%, the parametric statistical methods are shown to be conservative for voxelwise inference and invalid for clusterwise inference. Our results suggest that the principal cause of the invalid cluster inferences is spatial autocorrelation functions that do not follow the assumed Gaussian shape. By comparison, the nonparametric permutation test is found to produce nominal results for voxelwise as well as clusterwise inference. These findings speak to the need of validating the statistical methods being used in the field of neuroimaging.

  • 182.
    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.
    Ohlsson, Henrik
    Linköping University, Department of Electrical Engineering, Electronics System. 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.
    Rydell, Joakim
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Media and Information Technology. 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.
    Balancing an Inverted Pendulum by Thinking A Real-Time fMRI Approach2009Conference paper (Other academic)
    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 inverse 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 inverse pendulum is shown to the subject in a pair of VR goggles. The subject was able to balance the inverse pendulum both with real activity and imagined activity. The developments here have a potential to aid people with communication disabilities e.g., locked in people. It might also be a tool for stroke patients to be ableto train the damaged brain area and get real-time feedback of when they do it right.

  • 183.
    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.
    Ohlsson, Henrik
    Linköping University, Department of Electrical Engineering, Automatic Control. 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.
    Rydell, Joakim
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. 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.
    Using Real-Time fMRI to Control a Dynamical System2009In: ISMRM 17th Scientific Meeting & Exhibition, 2009Conference paper (Refereed)
    Abstract [en]

    We present e method for controlling a dynamical system using real-time fMRI. The objective for the subject in the MR scanner is to balance an inverse pendulum by activating the left or right hand or resting. The brain activity is clasified each second by a neural network and the classification is sent to a pendulum simulator to change the state of the pendulum. The state of the inverse pendulum is shown to the subject in a pair of VR goggles. The subject was able to balance the inverse pendulum during a 7 minute test run.

  • 184.
    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.
    Ohlsson, Henrik
    Linköping University, Department of Electrical Engineering, Automatic Control. 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.
    Rydell, Joakim
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. 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.
    Using Real-Time fMRI to Control a Dynamical System2009Report (Other academic)
    Abstract [en]

    We present e method for controlling a dynamical system using real-time fMRI. The objective for the subject in the MR scanner is to balance an inverse pendulum by activating the left or right hand or resting. The brain activity is clasified each second by a neural network and the classification is sent to a pendulum simulator to change the state of the pendulum. The state of the inverse pendulum is shown to the subject in a pair of VR goggles. The subject was able to balance the inverse pendulum during a 7 minute test run.

  • 185.
    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.
    Ohlsson, Henrik
    Linköping University, Department of Electrical Engineering, Automatic Control. 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.
    Rydell, Joakim
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Ynnerman, Anders
    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.
    Using Real-Time fMRI to Control a Dynamical System by Brain Activity Classification2010Report (Other academic)
    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.

  • 186.
    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).
    Ohlsson, Henrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    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).
    Rydell, Joakim
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    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, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Using Real-Time fMRI to Control a Dynamical System by Brain Activity Classification2009In: 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 (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.

  • 187.
    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.
    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 Centre, Department of Clinical Physiology.
    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 Phase Based Registration for Robust Quantitative MRI2010In: Proceedings of the annual meeting of the International Society for Magnetic Resonance in Medicine (ISMRM 2010), 2010Conference 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.

  • 188.
    Elahi, Pegah
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Application of Noise Invalidation Denoising in MRI2012Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Magnetic Resonance Imaging (MRI) is a common medical imaging tool that have beenused in clinical industry for diagnostic and research purposes. These images are subjectto noises while capturing the data that can eect the image quality and diagnostics.Therefore, improving the quality of the generated images from both resolution andsignal to noise ratio (SNR) perspective is critical. Wavelet based denoising technique isone of the common tools to remove the noise in the MRI images. The noise is eliminatedfrom the detailed coecients of the signal in the wavelet domain. This can be done byapplying thresholding methods. The main task here is to nd an optimal threshold andkeep all the coecients larger than this threshold as the noiseless ones. Noise InvalidationDenoising technique is a method in which the optimal threshold is found by comparingthe noisy signal to a noise signature (function of noise statistics). The original NIDeapproach is developed for one dimensional signals with additive Gaussian noise. In thiswork, the existing NIDe approach has been generalized for applications in MRI imageswith dierent noise distribution. The developed algorithm was tested on simulated datafrom the Brainweb database and compared with the well-known Non Local Mean lteringmethod for MRI. The results indicated better detailed structural preserving forthe NIDe approach on the magnitude data while the signal to noise ratio is compatible.The algorithm shows an important advantageous which is less computational complexitythan the NLM method. On the other hand, the Unbiased NLM technique is combinedwith the proposed technique, it can yield the same structural similarity while the signalto noise ratio is improved.

  • 189.
    Eriksson Bylund, Nina
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Ressner, Marcus
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    3D Wiener filtering to reduce reverberations in ultrasound image sequences2003In: Image Analysis: 13th Scandinavian Conference, SCIA 2003 Halmstad, Sweden, June 29 – July 2, 2003 Proceedings / [ed] Josef Bigun and Tomas Gustavsson, Springer, 2003, Vol. 2749, p. 579-586Chapter in book (Refereed)
    Abstract [en]

    One of the most frequently occuring artifacts in ultrasound imaging is reverberations. These are multiple reflection echoes that result in ghost echoes in the ultrasound image. A method for reducing these unwanted artifacts using a three-dimensional (3D) Wiener filter is presented. The Wiener filter is a global filter and produces an estimate of the uncorrupted signal by minimizing the mean square error between the estimate and the uncorrupted signal in a statistical sense. The procedure works as follows: In a graphic interface the operator is displayed an image sequence. The operator marks two areas in one of the images, one area which contains a typical reverberation artifact, and one area free from artifact. Using these areas to produce noise and signal estimates, a Wiener filter is created and applied to the sequence. The 3D Wiener filters display excellent selection capabilities, and the developed method significantly reduces the magnitude of the reverberation artifact in the tested sequences.

  • 190.
    Eriksson, Stefanie
    et al.
    Lund University, Sweden.
    Lasic, Samo
    CR Dev AB, Sweden.
    Nilsson, Markus
    Lund University, Sweden.
    Westin, Carl-Fredrik
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Harvard University, MA 02215 USA.
    Topgaard, Daniel
    Lund University, Sweden.
    NMR diffusion-encoding with axial symmetry and variable anisotropy: Distinguishing between prolate and oblate microscopic diffusion tensors with unknown orientation distribution2015In: Journal of Chemical Physics, ISSN 0021-9606, E-ISSN 1089-7690, Vol. 142, no 10, p. 104201-Article in journal (Refereed)
    Abstract [en]

    We introduce a nuclear magnetic resonance method for quantifying the shape of axially symmetric microscopic diffusion tensors in terms of a new diffusion anisotropy metric, D-Delta, which has unique values for oblate, spherical, and prolate tensor shapes. The pulse sequence includes a series of equal-amplitude magnetic field gradient pulse pairs, the directions of which are tailored to give an axially symmetric diffusion-encoding tensor b with variable anisotropy b(Delta). Averaging of data acquired for a range of orientations of the symmetry axis of the tensor b renders the method insensitive to the orientation distribution function of the microscopic diffusion tensors. Proof-of-principle experiments are performed on water in polydomain lyotropic liquid crystals with geometries that give rise to microscopic diffusion tensors with oblate, spherical, and prolate shapes. The method could be useful for characterizing the geometry of fluid-filled compartments in porous solids, soft matter, and biological tissues. (C) 2015 Author(s).

  • 191.
    Eriksson-Bylund, Nina
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    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).
    Detecting and reducing reverberation artifacts2004In: Proceedings of the Swedish Symposium on Image Analysis (2004), 2004, p. 54-57Conference paper (Other academic)
  • 192.
    Eriksson-Bylund, Nina
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    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).
    Interactive 3D filter design for ultrasound artifact reduction2005In: Image Processing, 2005. ICIP 2005. IEEE International Conference on  (Volume:3 ), 2005, p. 728-731Conference paper (Refereed)
    Abstract [en]

    A method for detecting and reducing reverberation artifacts in ultrasound image sequences is described. A reverberation artifact localization map is produced using local Rf-bandwidth estimation. To reduce the artifacts an ideal 3D (2D + time) Wiener filter function is computed by using the reverberation map to interactively produce an estimate of the noise and signal spectra. The Wiener filter kernel is optimized to obtain good locality properties. The optimized filter is then applied to the ultrasound image sequence. The test sequence used is from an open chest pig heart, corrupted by strong reverberation artifacts. The selective power of a 3D filter is far superior to that of ID and 2D filters and the reverberation artifacts are almost completely removed by the developed method.

  • 193.
    Eriksson-Bylund, Nina
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Andersson, Mats
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Knutsson, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Wide range frequensy estimation in ultrasound images2001In: SSAB Symposium on Image Analysis,2001, 2001Conference paper (Other academic)
  • 194.
    Eriksson-Bylund, Nina
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Ressner, Marcus
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Reverberation Reduction Using 3D Wiener Filtering2003Conference paper (Other academic)
    Abstract [en]

    One of the most common artifacts in ultrasound imaging is reverberations. These are multiple reflection echoes that register as coming from a deeper region than the depth of the interface that are causing them, and result in ghost echoes in the ultrasound image. A method to reduce these unwanted artifacts using a three dimensional (2D + time) Wiener filter has been developed. Two sequences of iq-data, the least processed signal possible to retrieve from the ultrasound system (Vingmed System Five), have been used to test the method: One sequence on a tissue-mimicking agar gel phantom in which bars of glass simulating ribs give rise to reverberations, and one sequence on an open-chest pig with a strong reverberation from a water-filled rubber glove used as a medium between the heart and the transducer. The procedure works as follows: In a graphic interface the operator is shown the image sequence. In one of the frames two areas must be marked out; One area which contains a typical reverberation artifact, and one area which will represent an artifact free signal. After creating the three dimensional Wiener filter post-processing of the sequence is performed. The developed method significantly reduced the magnitude of the reverberation artifact in the tested sequences.

  • 195.
    Erlandsson, Marcus
    et al.
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Burman, Lars G.
    Swedish Institute for Infectious Disease Control, Stockholm, Sweden.
    Cars, Otto
    Swedish Institute for Infectious Disease Control, Stockholm, Sweden.
    Gill, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Nilsson, Lennart E.
    Walther, Sten
    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.
    Hanberger, Håkan
    Linköping University, Department of Clinical and Experimental Medicine, Infectious Diseases . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medicine, Department of Infectious Diseases in Östergötland.
    ICU-STRAMA Study Group,
    Prescription of antibiotic agents in Swedish intensive care units is empiric and adequate2007In: Scandinavian Journal of Infectious Diseases, ISSN 0036-5548, E-ISSN 1651-1980, Vol. 39, no 1, p. 63-69Article in journal (Refereed)
    Abstract [en]

    Since the prescription of antibiotics in the hospital setting is often empiric, particularly in the critically ill, and therefore fraught with potential error, we analysed the use of antibiotic agents in Swedish intensive care units (ICUs). We examined indications for antibiotic treatment, agents and dosage prescribed among 393 patients admitted to 23 ICUs at 7 tertiary care centres, 11 secondary hospitals and 5 primary hospitals over a 2-week period in November 2000. Antibiotic consumption was higher among ICU patients in tertiary care centres with a median of 84% (range 58-87%) of patients on antibiotics compared to patients in secondary hospitals (67%, range 35-93%) and in primary hospitals (38%, range 24-80%). Altogether 68% of the patients received antibiotics during the ICU stay compared to 65% on admission. Cefuroxime was the most commonly prescribed antibiotic before and during admission (28% and 24% of prescriptions, respectively). A date for decision to continue or discontinue antibiotic therapy was set in 21% (6/29) of patients receiving prophylaxis, in 8% (16/205) receiving empirical treatment and in 3% (3/88) when culture-based therapy was given. No correlation between antibiotic prescription and laboratory parameters such as CRP levels, leukocyte and thrombocyte counts, was found. The treatment was empirical in 64% and prophylactic in 9% of cases. Microbiological data guided prescription more often in severe sepsis (median 50%, range 40-60% of prescriptions) than in other specified forms of infection (median 32%, range 21-50%). The empirically chosen antibiotic was found to be active in vitro against the pathogens found in 55 of 58 patients (95%) with a positive blood culture. This study showed that a high proportion of ICU patients receive antimicrobial agents and, as expected, empirical-based therapy is more common than culture-based therapy. Antibiotics given were usually active in vitro against the pathogen found in blood cultures. We ascribe this to a relatively modest antibiotic resistance problem in Swedish hospitals.

  • 196.
    Erlandsson, Marcus
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Infectious Diseases . Linköping University, Faculty of Health Sciences.
    Gill, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Nilsson, Lennart E.
    Linköping University, Department of Clinical and Experimental Medicine, Clinical Microbiology . Linköping University, Faculty of Health Sciences.
    Walther, Sten
    Department of Anaesthesiology, Ullevål University Hospital, University of Oslo, Oslo, Norway.
    Giske, Christian G.
    Division of Clinical Microbiology, Karolinska University Hospital, Stockholm, Sweden.
    Jonas, Daniel
    Institute of Environmental Medicine and Hospital Epidemiology, University Medical Centre, Freiburg, Germany.
    Hanberger, Håkan
    Linköping University, Department of Clinical and Experimental Medicine, Infectious Diseases . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medicine, Department of Infectious Diseases in Östergötland.
    Nordlinder, David
    Linköping University, Department of Clinical and Experimental Medicine, Infectious Diseases . Linköping University, Faculty of Health Sciences.
    Antibiotic susceptibility patterns and clones of Pseudomonas aeruginosa in Swedish ICUs2008In: Scandinavian Journal of Infectious Diseases, ISSN 0036-5548, E-ISSN 1651-1980, Vol. 40, no 6-7, p. 487-494Article in journal (Refereed)
    Abstract [en]

    Pseudomonas aeruginosa is 1 of the bacteria most adaptive to anti-bacterial treatment. Previous studies have shown nosocomial spread and transmission of clonal strains of P. aeruginosa in European hospitals. In this study we investigated antibiotic susceptibility and clonality in 101 P. aeruginosa isolates from 88 patients admitted to 8 Swedish ICUs during 2002. We also compared phenotypes and genotypes of P. aeruginosa and carried out cluster analysis to determine if phenotypic data can be used for surveillance of clonal spread. All isolates were collected on clinical indication as part of the NPRS II study in Sweden and were subjected to AFLP analysis for genotyping. 68 isolates with unique genotypes were found. Phenotyping was performed using MIC values for 5 anti-pseudomonal agents. Almost 6% of the isolates were multi-drug resistant (MDR), and this figure rose to almost 8% when intermediate isolates were also included. We found probable clonal spread in 9 cases, but none of them was found to be an MDR strain. Phenotypical cluster analysis produced 40 clusters. Comparing partitions did not demonstrate any significant concordance between the typing methods. The conclusion of our study is that cross-transmission and clonal spread of MDR P. aeruginosa does not present a clinical problem in Swedish ICUs, but probable cross-transmission of non-MDR clones indicate a need for improved hygiene routines bedside. The phenotype clusters were not concordant with genotype clusters, and genotyping is still recommended for epidemiological tracking.

  • 197.
    Erlingsson, Styrbjörn
    et al.
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences.
    Herard, Sebastian
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Lindström, Torbjörn
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medicine, Department of Endocrinology and Gastroenterology UHL.
    Länne, Toste
    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.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Nyström, Fredrik
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences.
    Men develop more intraabdominal obesity and signs of the metabolic syndrome after hyperalimentation than women2009In: Metabolism: Clinical and Experimental, ISSN 0026-0495, E-ISSN 1532-8600, Vol. 58, no 7, p. 995-1001Article in journal (Refereed)
    Abstract [en]

    We prospectively studied the effects of fast food-based hyperalimentation on insulin sensitivity and components of the metabolic syndrome and analyzed this with respect to sex. Twelve nonobese men and 6 nonobese women (26 +/- 6.6 years old), and an age-matched control group were recruited. Subjects in the intervention group aimed for 5% to 15% weight increase by doubling their regular caloric intake based on at least 2 fast food meals a day while also adopting a sedentary lifestyle for 4 weeks (andlt;5000 steps a day). Weight of Subjects in the intervention group increased from 67.6 +/- 9.1 to 74.0 +/- 11 kg (P andlt;.001), with no sex difference with regard to this or with respect to changes of total abdominal fat volumes or waist circumferences. Fasting insulin (men: before, 3.8 +/- 1.7 mu U/mL, after, 7.4 +/- 3.1 mu U/mL; P=.004; women: before, 4.9 +/- 2.3 mu U/mL; after, 5.9 +/- 2.8 mu U/mL; P =.17), systolic blood pressure (men: before, 117 +/- 13 mm Hg; after, 127 +/- 9.1 mm Hg; P =.002; women: before, 102 +/- 5.1 mm Hg; after, 98 +/- 5.4 mm Hg; P =.39), serum low-density lipoprotein cholesterol, and apolipoprotein B increased only in the men of the intervention group. The sex differences in the metabolic responses to the intervention were linked to a considerable difference in the fat accumulation pattern; 41.4% +/- 9.2% of the increase of the fat volume in the abdominal region was accumulated intraabdominally in men and 22.7 +/- 6.5% in women (P andlt;.0001). This Study thus showed that women are protected, compared with men, against developing intraabdominal obesity when adopting a standardized obesity-provoking lifestyle. Our findings suggest that it is not different lifestyles and/or behaviors that underlie the fact that men have a higher cardiovascular risk at the same level of percentage of body fat than women.

  • 198.
    Esmaeili, Morteza
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Enhancement and Visualization of VascularStructures in MRA Images Using Local Structure2010Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
    Abstract [en]

    The novel method of this thesis work is based on using quadrature filters to estimate an orientation tensor and to use the advantage of tensor information to control 3D adaptive filters. The adaptive filters are applied to enhance the Magnetic Resonance Angiography (MRA) images. The tubular structures are extracted from the volume dataset by using the quadrature filters. The idea of developing adaptive filtering in this thesis work is to enhance the volume dataset and suppress the image noise. Then the output of the adaptive filtering can be a clean dataset for segmentation of blood vessel structures to get appropriate volume visualization.

    The local tensors are used to create the control tensor which is used to control adaptive filters. By evaluation of the tensor eigenvalues combination, the local structures like tubular structures and stenosis structures are extracted from the dataset. The method has been evaluated with synthetic objects, which are vessel models (for segmentation), and onion like synthetic object (for enhancement). The experimental results are shown on clinical images to validate the proposed method as well.

  • 199.
    Falahati Asrami, Farshad
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Alzheimer's Disease Classification using K-OPLS and MRI2012Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In this thesis, we have used the kernel based orthogonal projection to latent structures (K-OPLS) method to discriminate between Alzheimer's Disease patients (AD) and healthy control subjects (CTL), and to predict conversion from mild cognitive impairment (MCI) to AD. In this regard three cohorts were used to create two different datasets; a small dataset including 63 subjects based on the Alzheimer’s Research Trust (ART) cohort and a large dataset including 1074 subjects combining the AddNeuroMed (ANM) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohorts.

    In the ART dataset, 34 regional cortical thickness measures and 21 volumetric measures from MRI in addition to 3 metabolite ratios from MRS, altogether 58 variables obtained for 28 AD and 35 CTL subjects. Three different K-OPLS models were created based on MRI and MRS measures and their combination. Combining the MRI and the MRS measures significantly improved the discriminant power resulting in a sensitivity of 96.4% and a specificity of 97.1%.

    In the combined dataset (ADNI and AddNeuroMed), the Freesurfer pipeline was utilized to extract 34 regional cortical thickness measures and 23 volumetric measures from MRI scans of 295 AD, 335 CTL and 444 MCI subjects. The classification of AD and CTL subjects using the K-OPLS model resulted in a high sensitivity of 85.8% and a specificity of 91.3%. Subsequently, the K-OPLS model was used to prospectively predict conversion from MCI to AD, according to the one year follow up diagnosis. As a result, 78.3% of the MCI converters were classified as AD-like and 57.5% of the MCI non-converters were classified as control-like.

    Furthermore, an age correction method was proposed to remove the effect of age as a confounding factor. The age correction method successfully removed the age-related changes of the data. Also, the age correction method slightly improved the performance regarding to classification and prediction. This resulted in that 82.1% of the MCI converters were correctly classified. All analyses were performed using 7-fold cross validation.

    The K-OPLS method shows strong potential for classification of AD and CTL, and for prediction of MCI conversion. 

  • 200.
    Farnebäck, Gunnar
    et al.
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    Rydell, Joakim
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Ebbers, Tino
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Centre, Department of Clinical Physiology UHL.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Efficient computation of the inverse gradient on irregular domains2007In: 2007 IEEE 11TH INTERNATIONAL CONFERENCE ON COMPUTER VISION, VOLS 1-6, IEEE , 2007, p. 2710-2717Conference paper (Other academic)
    Abstract [en]

    The inverse gradient problem, finding a scalar field f with a gradient near a given vector field g on some bounded and connected domain Omega epsilon R(n), can be solved by means of a Poisson equation with inhomogeneous Neumann boundary conditions. We present an elementary derivation of this partial differential equation and an efficient multigrid-based method to numerically compute the inverse gradient on non-rectangular domains. The utility of the method is demonstrated by a range of important medical applications such as phase unwrapping, pressure computation, inverse deformation fields, and fiber bundle tracking.

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