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  • 1.
    Ahlander, Britt-Marie
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Magnetic Resonance Imaging of the Heart: Image quality, measurement accuracy and patient experience2016Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Background: Non-invasive diagnostic imaging of atherosclerotic coronary artery disease (CAD) is frequently carried out with cardiovascular magnetic resonance imaging (CMR) or myocardial perfusion single photon emission computed tomography (MPS). CMR is the gold standard for the evaluation of scar after myocardial infarction and MPS the clinical gold standard for ischemia. Magnetic Resonance Imaging (MRI) is at times difficult for patients and may induce anxiety while patient experience of MPS is largely unknown.

    Aims: To evaluate image quality in CMR with respect to the sequences employed, the influence of atrial fibrillation, myocardial perfusion and the impact of patient information. Further, to study patient experience in relation to MRI with the goal of improving the care of these patients.

    Method: Four study designs have been used. In paper I, experimental cross-over, paper (II) experimental controlled clinical trial, paper (III) psychometric crosssectional study and paper (IV) prospective intervention study. A total of 475 patients ≥ 18 years with primarily cardiac problems (I-IV) except for those referred for MRI of the spine (III) were included in the four studies.

    Result: In patients (n=20) with atrial fibrillation, a single shot steady state free precession (SS-SSFP) sequence showed significantly better image quality than the standard segmented inversion recovery fast gradient echo (IR-FGRE) sequence (I). In first-pass perfusion imaging the gradient echo-echo planar imaging sequence (GREEPI) (n=30) had lower signal-to-noise and contrast–to-noise ratios than the steady state free precession sequence (SSFP) (n=30) but displayed a higher correlation with the MPS results, evaluated both qualitatively and quantitatively (II). The MRIAnxiety Questionnaire (MRI-AQ) was validated on patients, referred for MRI of either the spine (n=193) or the heart (n=54). The final instrument had 15 items divided in two factors regarding Anxiety and Relaxation. The instrument was found to have satisfactory psychometric properties (III). Patients who prior CMR viewed an information video scored significantly (lower) better in the factor Relaxation, than those who received standard information. Patients who underwent MPS scored lower on both factors, Anxiety and Relaxation. The extra video information had no effect on CMR image quality (IV).

    Conclusion: Single shot imaging in atrial fibrillation produced images with less artefact than a segmented sequence. In first-pass perfusion imaging, the sequence GRE-EPI was superior to SSFP. A questionnaire depicting anxiety during MRI showed that video information prior to imaging helped patients relax but did not result in an improvement in image quality.

    List of papers
    1. Image quality and myocardial scar size determined with magnetic resonance imaging in patients with permanent atrial fibrillation: a comparison of two imaging protocols
    Open this publication in new window or tab >>Image quality and myocardial scar size determined with magnetic resonance imaging in patients with permanent atrial fibrillation: a comparison of two imaging protocols
    Show others...
    2010 (English)In: CLINICAL PHYSIOLOGY AND FUNCTIONAL IMAGING, ISSN 1475-0961, Vol. 30, no 2, 122-129 p.Article in journal (Refereed) Published
    Abstract [en]

    Pandgt;Background: Magnetic resonance imaging (MRI) of the heart generally requires breath holding and a regular rhythm. Single shot 2D steady-state free precession (SS_SSFP) is a fast sequence insensitive to arrhythmia as well as breath holding. Our purpose was to determine image quality, signal-to-noise (SNR) and contrast-to-noise (CNR) ratios and infarct size with a fast single shot and a standard segmented MRI sequence in patients with permanent atrial fibrillation and chronic myocardial infarction. Methods: Twenty patients with chronic myocardial infarction and ongoing atrial fibrillation were examined with inversion recovery SS_SSFP and segmented inversion recovery 2D fast gradient echo (IR_FGRE). Image quality was assessed in four categories: delineation of infarcted and non-infarcted myocardium, occurrence of artefacts and overall image quality. SNR and CNR were calculated. Myocardial volume (ml) and infarct size, expressed as volume (ml) and extent (%), were calculated, and the methodological error was assessed. Results: SS_SSFP had significantly better quality scores in all categories (P = 0 center dot 037, P = 0 center dot 014, P = 0 center dot 021, P = 0 center dot 03). SNRinfarct and SNRblood were significantly better for IR_FGRE than for SS_SSFP (P = 0 center dot 048, P = 0 center dot 018). No significant difference was found in SNRmyocardium and CNR. The myocardial volume was significantly larger with SS_SSFP (170 center dot 7 versus 159 center dot 2 ml, P andlt; 0 center dot 001), but no significant difference was found in infarct volume and infarct extent. Conclusion: SS_SSFP displayed significantly better image quality than IR_FGRE. The infarct size and the error in its determination were equal for both sequences, and the examination time was shorter with SS_SSFP.

    Keyword
    atrial fibrillation, magnetic resonance imaging, myocardial infarction, segmented inversion recovery 2D fast gradient echo, single shot inversion recovery 2D steady-state free precession
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-54159 (URN)10.1111/j.1475-097X.2009.00914.x (DOI)000274438800006 ()
    Available from: 2010-02-26 Created: 2010-02-26 Last updated: 2016-08-24
    2. An echo-planar imaging sequence is superior to a steady-state free precession sequence for visual as well as quantitative assessment of cardiac magnetic resonance stress perfusion
    Open this publication in new window or tab >>An echo-planar imaging sequence is superior to a steady-state free precession sequence for visual as well as quantitative assessment of cardiac magnetic resonance stress perfusion
    Show others...
    2017 (English)In: Clinical Physiology and Functional Imaging, ISSN 1475-0961, E-ISSN 1475-097X, Vol. 37, no 1, 52-61 p.Article in journal (Refereed) Published
    Abstract [en]

    Background To assess myocardial perfusion, steady-state free precession cardiac magnetic resonance (SSFP, CMR) was compared with gradient-echo–echo-planar imaging (GRE-EPI) using myocardial perfusion scintigraphy (MPS) as reference. Methods Cardiac magnetic resonance perfusion was recorded in 30 patients with SSFP and in another 30 patients with GRE-EPI. Timing and extent of inflow delay to the myocardium was visually assessed. Signal-to-noise (SNR) and contrast-to-noise (CNR) ratios were calculated. Myocardial scar was visualized with a phase-sensitive inversion recovery sequence (PSIR). All scar positive segments were considered pathologic. In MPS, stress and rest images were used as in clinical reporting. The CMR contrast wash-in slope was calculated and compared with the stress score from the MPS examination. CMR scar, CMR perfusion and MPS were assessed separately by one expert for each method who was blinded to other aspects of the study. Results Visual assessment of CMR had a sensitivity for the detection of an abnormal MPS at 78% (SSFP) versus 91% (GRE-EPI) and a specificity of 58% (SSFP) versus 84% (GRE-EPI). Kappa statistics for SSFP and MPS was 0·29, for GRE-EPI and MPS 0·72. The ANOVA of CMR perfusion slopes for all segments versus MPS score (four levels based on MPS) had correlation r = 0·64 (SSFP) and r = 0·96 (GRE-EPI). SNR was for normal segments 35·63 ± 11·80 (SSFP) and 17·98 ± 8·31 (GRE-EPI), while CNR was 28·79 ± 10·43 (SSFP) and 13·06 ± 7·61 (GRE-EPI). Conclusion GRE-EPI displayed higher agreement with the MPS results than SSFP despite significantly lower signal intensity, SNR and CNR.

    Place, publisher, year, edition, pages
    John Wiley & Sons, 2017
    Keyword
    cardiac imaging techniques, coronary heart disease, Magnetic Resonance Imaging, nuclear medicine, perfusion
    National Category
    Radiology, Nuclear Medicine and Medical Imaging Medical Laboratory and Measurements Technologies Medical Image Processing Clinical Medicine
    Identifiers
    urn:nbn:se:liu:diva-130795 (URN)10.1111/cpf.12267 (DOI)000390688200008 ()26147785 (PubMedID)
    Note

    Funding agencies: Medical Research Council of Southeast Sweden [12437]; Futurum, the County council of Jonkoping [12440, 81851, 217261]; Linkoping University; County Council of Ostergotland [281281]; Swedish Heart-Lung Foundation [20120449]

    Available from: 2016-08-24 Created: 2016-08-24 Last updated: 2017-01-26Bibliographically approved
    3. Development and validation of a questionnaire evaluating patient anxiety during Magnetic Resonance Imaging: the Magnetic Resonance Imaging-Anxiety Questionnaire (MRI-AQ)
    Open this publication in new window or tab >>Development and validation of a questionnaire evaluating patient anxiety during Magnetic Resonance Imaging: the Magnetic Resonance Imaging-Anxiety Questionnaire (MRI-AQ)
    Show others...
    2016 (English)In: Journal of Advanced Nursing, ISSN 0309-2402, E-ISSN 1365-2648, Vol. 72, no 6, 1368-1380 p.Article in journal (Refereed) Published
    Abstract [en]

    Aim. To develop and validate a new instrument measuring patient anxiety during Magnetic Resonance Imaging examinations, Magnetic Resonance Imaging-Anxiety Questionnaire. Background. Questionnaires measuring patients anxiety during Magnetic Resonance Imaging examinations have been the same as used in a wide range of conditions. To learn about patients experience during examination and to evaluate interventions, a specific questionnaire measuring patient anxiety during Magnetic Resonance Imaging is needed. Design. Psychometric cross-sectional study with test-retest design. Methods. A new questionnaire, Magnetic Resonance Imaging-Anxiety Questionnaire, was designed from patient expressions of anxiety in Magnetic Resonance Imaging-scanners. The sample was recruited between October 2012-October 2014. Factor structure was evaluated with exploratory factor analysis and internal consistency with Cronbachs alpha. Criterion-related validity, known-group validity and test-retest was calculated. Results. Patients referred for Magnetic Resonance Imaging of either the spine or the heart, were invited to participate. The development and validation of Magnetic Resonance Imaging-Anxiety Questionnaire resulted in 15 items consisting of two factors. Cronbachs alpha was found to be high. Magnetic Resonance Imaging-Anxiety Questionnaire correlated higher with instruments measuring anxiety than with depression scales. Known-group validity demonstrated a higher level of anxiety for patients undergoing Magnetic Resonance Imaging scan of the heart than for those examining the spine. Test-retest reliability demonstrated acceptable level for the scale. Conclusion. Magnetic Resonance Imaging-Anxiety Questionnaire bridges a gap among existing questionnaires, making it a simple and useful tool for measuring patient anxiety during Magnetic Resonance Imaging examinations.

    Place, publisher, year, edition, pages
    WILEY-BLACKWELL, 2016
    Keyword
    anxiety; instrument development; magnetic resonance imaging; nurse; nursing; reliability; validity
    National Category
    Other Medical Sciences not elsewhere specified
    Identifiers
    urn:nbn:se:liu:diva-129145 (URN)10.1111/jan.12917 (DOI)000376007400014 ()26893007 (PubMedID)
    Note

    Funding Agencies|Swedish Heart and Lung Foundation; Futurum County Council of Jonkoping

    Available from: 2016-06-13 Created: 2016-06-13 Last updated: 2016-08-24
  • 2.
    Ahlander, Britt-Marie
    et al.
    Department of Radiology, Ryhov County Hospital, Jönköping.
    Maret, Eva
    Department of Radiology, Ryhov County Hospital, Jönköping / Department of Clinical Physiology, Karolinska University Hospital, Stockholm.
    Brudin, Lars
    Department of Clinical Physiology, Kalmar County Hospital, Kalmar.
    Starck, Sven-Åke
    Department of Natural Science and Biomedicine, School of Health Sciences, Jönköping University / Department of Oncology, Hospital Physics, Ryhov County Hospital, Jönköping.
    Engvall, Jan
    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 Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    An echo-planar imaging sequence is superior to a steady-state free precession sequence for visual as well as quantitative assessment of cardiac magnetic resonance stress perfusion2017In: Clinical Physiology and Functional Imaging, ISSN 1475-0961, E-ISSN 1475-097X, Vol. 37, no 1, 52-61 p.Article in journal (Refereed)
    Abstract [en]

    Background To assess myocardial perfusion, steady-state free precession cardiac magnetic resonance (SSFP, CMR) was compared with gradient-echo–echo-planar imaging (GRE-EPI) using myocardial perfusion scintigraphy (MPS) as reference. Methods Cardiac magnetic resonance perfusion was recorded in 30 patients with SSFP and in another 30 patients with GRE-EPI. Timing and extent of inflow delay to the myocardium was visually assessed. Signal-to-noise (SNR) and contrast-to-noise (CNR) ratios were calculated. Myocardial scar was visualized with a phase-sensitive inversion recovery sequence (PSIR). All scar positive segments were considered pathologic. In MPS, stress and rest images were used as in clinical reporting. The CMR contrast wash-in slope was calculated and compared with the stress score from the MPS examination. CMR scar, CMR perfusion and MPS were assessed separately by one expert for each method who was blinded to other aspects of the study. Results Visual assessment of CMR had a sensitivity for the detection of an abnormal MPS at 78% (SSFP) versus 91% (GRE-EPI) and a specificity of 58% (SSFP) versus 84% (GRE-EPI). Kappa statistics for SSFP and MPS was 0·29, for GRE-EPI and MPS 0·72. The ANOVA of CMR perfusion slopes for all segments versus MPS score (four levels based on MPS) had correlation r = 0·64 (SSFP) and r = 0·96 (GRE-EPI). SNR was for normal segments 35·63 ± 11·80 (SSFP) and 17·98 ± 8·31 (GRE-EPI), while CNR was 28·79 ± 10·43 (SSFP) and 13·06 ± 7·61 (GRE-EPI). Conclusion GRE-EPI displayed higher agreement with the MPS results than SSFP despite significantly lower signal intensity, SNR and CNR.

  • 3.
    Andersson, Mats
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Burdakov, Oleg
    Linköping University, Department of Mathematics, Optimization . Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Zikrin, Spartak
    Linköping University, Department of Mathematics. Linköping University, The Institute of Technology.
    Global Search Strategies for Solving Multilinear Least-squares Problems2011Report (Other academic)
    Abstract [en]

    The multilinear least-squares (MLLS) problem is an extension of the linear least-squares problem. The difference is that a multilinearoperator is used in place of a matrix-vector product. The MLLS istypically a large-scale problem characterized by a large number of local minimizers. It originates, for instance, from the design of filter networks. We present a global search strategy that allows formoving from one local minimizer to a better one. The efficiencyof this strategy isillustrated by results of numerical experiments performed forsome problems related to the design of filter networks.

  • 4.
    Andersson, Mats
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Burdakov, Oleg
    Linköping University, Department of Mathematics, Optimization . Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Zikrin, Spartak
    Linköping University, Department of Mathematics, Mathematics and Applied Mathematics. Linköping University, The Institute of Technology.
    Global search strategies for solving multilinear least-squares problems2012In: Sultan Qaboos University Journal for Science, ISSN 1027-524X, Vol. 17, no 1, 12-21 p.Article in journal (Refereed)
    Abstract [en]

    The multilinear least-squares (MLLS) problem is an extension of the linear leastsquares problem. The difference is that a multilinear operator is used in place of a matrix-vector product. The MLLS is typically a large-scale problem characterized by a large number of local minimizers. It originates, for instance, from the design of filter networks. We present a global search strategy that allows for moving from one local minimizer to a better one. The efficiency of this strategy is illustrated by results of numerical experiments performed for some problems related to the design of filter networks.

  • 5.
    Andersson, Mats
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Burdakov, Oleg
    Linköping University, Department of Mathematics, Optimization . Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Zikrin, Spartak
    Linköping University, Department of Mathematics. Linköping University, The Institute of Technology.
    Sparsity Optimization in Design of Multidimensional Filter Networks2013Report (Other academic)
    Abstract [en]

    Filter networks is a powerful tool used for reducing the image processing time, while maintaining its reasonably high quality.They are composed of sparse sub-filters whose low sparsity ensures fast image processing.The filter network design is related to solvinga sparse optimization problem where a cardinality constraint bounds above the sparsity level.In the case of sequentially connected sub-filters, which is the simplest network structure of those considered in this paper, a cardinality-constrained multilinear least-squares (MLLS) problem is to be solved. If to disregard the cardinality constraint, the MLLS is typically a large-scale problem characterized by a large number of local minimizers. Each of the local minimizers is singular and non-isolated.The cardinality constraint makes the problem even more difficult to solve.An approach for approximately solving the cardinality-constrained MLLS problem is presented.It is then applied to solving a bi-criteria optimization problem in which both thetime and quality of image processing are optimized. The developed approach is extended to designing filter networks of a more general structure. Its efficiency is demonstrated by designing certain 2D and 3D filter networks. It is also compared with the existing approaches.

  • 6.
    Andersson, Mats
    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.
    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.
    Adaptive Spatio-temporal Filtering of 4D CT-Heart2013In: Image Analyses: Image Processing, Computer Vision, Pattern Recognition, and Graphics / [ed] Joni-Kristian Kämäräinen, Markus Koskela, Berlin Heidelberg: Springer, 2013, 246-255 p.Conference paper (Refereed)
    Abstract [en]

    The aim of this project is to keep the x-ray exposure of the patient as low as reasonably achievable while improving the diagnostic image quality for the radiologist. The means to achieve these goals is to develop and evaluate an efficient adaptive filtering (denoising/image enhancement) method that fully explores true 4D image acquisition modes.

    The proposed prototype system uses a novel filter set having directional filter responses being monomials. The monomial filter concept is used both for estimation of local structure and for the anisotropic adaptive filtering. Initial tests on clinical 4D CT-heart data with ECG-gated exposure has resulted in a significant reduction of the noise level and an increased detail compared to 2D and 3D methods. Another promising feature is that the reconstruction induced streak artifacts which generally occur in low dose CT are remarkably reduced in 4D.

  • 7.
    Andersson, Thord
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Läthén, Gunnar
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Lenz, Reiner
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    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.
    Modified Gradient Search for Level Set Based Image Segmentation2013In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 22, no 2, 621-630 p.Article in journal (Refereed)
    Abstract [en]

    Level set methods are a popular way to solve the image segmentation problem. The solution contour is found by solving an optimization problem where a cost functional is minimized. Gradient descent methods are often used to solve this optimization problem since they are very easy to implement and applicable to general nonconvex functionals. They are, however, sensitive to local minima and often display slow convergence. Traditionally, cost functionals have been modified to avoid these problems. In this paper, we instead propose using two modified gradient descent methods, one using a momentum term and one based on resilient propagation. These methods are commonly used in the machine learning community. In a series of 2-D/3-D-experiments using real and synthetic data with ground truth, the modifications are shown to reduce the sensitivity for local optima and to increase the convergence rate. The parameter sensitivity is also investigated. The proposed methods are very simple modifications of the basic method, and are directly compatible with any type of level set implementation. Downloadable reference code with examples is available online.

  • 8.
    Andersson, Thord
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Romu, Thobias
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Karlsson, Anette
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Norén, Bengt
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Forsgren, Mikael
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Smedby, Örjan
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Kechagias, Stergios
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Gastroentorology.
    Almer, Sven
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Gastroentorology.
    Lundberg, Peter
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Consistent intensity inhomogeneity correction in water–fat MRI2015In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 42, no 2, 468-476 p.Article in journal (Refereed)
    Abstract [en]

    PURPOSE:

    To quantitatively and qualitatively evaluate the water-signal performance of the consistent intensity inhomogeneity correction (CIIC) method to correct for intensity inhomogeneities METHODS: Water-fat volumes were acquired using 1.5 Tesla (T) and 3.0T symmetrically sampled 2-point Dixon three-dimensional MRI. Two datasets: (i) 10 muscle tissue regions of interest (ROIs) from 10 subjects acquired with both 1.5T and 3.0T whole-body MRI. (ii) Seven liver tissue ROIs from 36 patients imaged using 1.5T MRI at six time points after Gd-EOB-DTPA injection. The performance of CIIC was evaluated quantitatively by analyzing its impact on the dispersion and bias of the water image ROI intensities, and qualitatively using side-by-side image comparisons.

    RESULTS:

    CIIC significantly ( P1.5T≤2.3×10-4,P3.0T≤1.0×10-6) decreased the nonphysiological intensity variance while preserving the average intensity levels. The side-by-side comparisons showed improved intensity consistency ( Pint⁡≤10-6) while not introducing artifacts ( Part=0.024) nor changed appearances ( Papp≤10-6).

    CONCLUSION:

    CIIC improves the spatiotemporal intensity consistency in regions of a homogenous tissue type. J. Magn. Reson. Imaging 2014.

  • 9.
    Andersson, Thord
    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.
    Romu, Thobias
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Norén, Bengt
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Forsgren, Mikael
    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. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Almer, Sven
    Linköping University, Department of Clinical and Experimental Medicine, Gastroenterology and Hepatology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Endocrinology.
    Lundberg, Peter
    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. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    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.
    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.
    Self-calibrated DCE MRI using Multi Scale Adaptive Normalized Averaging (MANA)2012In: Proceedings of the annual meeting of the International Society for Magnetic Resonance in Medicine (ISMRM 2012), 2012, 2012Conference paper (Other academic)
  • 10.
    Bae, Sang Won
    et al.
    Kyonggi University, Suwon, South Korea.
    Korman, Matias
    Tohoku University, Sendai, Japan.
    Mitchell, Joseph SB
    Stony Brook University, New York, USA.
    Okamoto, Yoshio
    The University of Electro-Communications, Tokyo, Japan.
    Polishchuk, Valentin
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Wang, Haitao
    Utah State University, Utah, USA.
    Computing the $ L_1 $ Geodesic Diameter and Center of a Polygonal Domain2016In: Leibniz International Proceedings in Informatics, ISSN 1868-8969, E-ISSN 1868-8969, Vol. 47, 14:1-14:14 p.Article in journal (Refereed)
    Abstract [en]

    For a polygonal domain with h holes and a total of n vertices, we present algorithms that compute the L1 geodesic diameter in O(n2+h4) time and the L1 geodesic center in O((n4+n2h4) (n)) time, where (·) denotes the inverse Ackermann function. No algorithms were known for these problems before. For the Euclidean counterpart, the best algorithms compute the geodesic diameter in O(n7.73) or O(n7(h+log n)) time, and compute the geodesic center in O(n12+) time. Therefore, our algorithms are much faster than the algorithms for the Euclidean problems. Our algorithms are based on several interesting observations on L1 shortest paths in polygonal domains.

  • 11.
    Baeza Ortega, José Antonio
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Combined Visualization of Intracardiac Blood Flow and Wall Motion Assessed by MRI2011Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    MRI is a well known and widely spread technique to characterize cardiac pathologies due to its high spatial resolution, its accessibility and its adjustable contrast among soft tissues.

    In attempt to close the gap between blood flow, myocardial movement and the cardiac fucntion, researching in the MRI field addresses the quantification of some of the most relevant blood and myocardial parameters.

    During this proyect a new tool that allows reading, postprocessing, quantifying and visualizing 2D motion sense MR data has been developed. In order to analyze intracardiac blood flow and wall motion, the new tool quantifies velocity, turbulent kinetic energy, pressure and strain.

    In the results section the final tool is presented, describing the visualization modes, which represent the quantified parameters both individually and combined; and detailing auxiliary tool features as masking, thresholding, zooming, and cursors.

    Finally, thecnical aspects as the convenience of two dimensional examinations to create compact tools, and the challenges of masking as part of the relative pressure calculation, among others, are discussed; ending up with the proposal of some future developments.

  • 12.
    Bagheri, Maryam
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Rezakhani, Arjang
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Roghani, Mehrdad
    Neurophysiology Research Center, Shahed University, Iran.
    Joghataei, Mohammad T.
    Iran University of Medical Science, Iran.
    Mohseni, Simin
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Protocol for Three-dimensional Confocal Morphometric Analysis of Astrocytes2015In: Journal of Visualized Experiments, ISSN 1940-087X, E-ISSN 1940-087X, no 106, e53113- p.Article in journal (Refereed)
    Abstract [en]

    As glial cells in the brain, astrocytes have diverse functional roles in the central nervous system. In the presence of harmful stimuli, astrocytes modify their functional and structural properties, a condition called reactive astrogliosis. Here, a protocol for assessment of the morphological properties of astrocytes is presented. This protocol includes quantification of 12 different parameters i.e. the surface area and volume of the tissue covered by an astrocyte (astrocyte territory), the entire astrocyte including branches, cell body, and nucleus, as well as total length and number of branches, the intensity of fluorescence immunoreactivity of antibodies used for astrocyte detection, and astrocyte density (number/1,000 mu m(2)). For this purpose three-dimensional (3D) confocal microscopic images were created, and 3D image analysis software such as Volocity 6.3 was used for measurements. Rat brain tissue exposed to amyloid beta(1-40) (A beta(1-40)) with or without a therapeutic intervention was used to present the method. This protocol can also be used for 3D morphometric analysis of other cells from either in vivo or in vitro conditions.

  • 13.
    Björnfot, Magnus
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Extension of DIRA (Dual-Energy Iterative Algorithm) to 3D Helical CT2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    There is a need for quantitative CT data in radiation therapy. Currently there are only few algorithms that address this issue, for instance the commercial DirectDensity algorithm. In scientific literature, an example of such an algorithm is DIRA. DIRA is an iterative model-based reconstruction method for dual-energy CT whose goal is to determine the material composition of the patient from accurate linear attenuation coefficients. It has been implemented in a two dimensional geometry, i.e., it could process axial scans only.  There was a need to extend DIRA so that it could process projection data generated in helical scanning geometries. The newly developed algorithm (DIRA-3D) implemented (i) polyenergetic semi-parallel projection generation, (ii) mono-energetic parallel projection generation and (iii) the PI-method for image reconstruction. The computation experiments showed that the accuracies of the resulting LAC and mass fractions were comparable to the ones of the original DIRA. The results converged after 10 iterations.

  • 14.
    Björnsdotter, Malin
    et al.
    Department of Physiology, Institute of Neuroscience and Physiology, University of Gothenburg, Sweden.
    Rylander, Karin
    Department of Physiology, Institute of Neuroscience and Physiology, University of Gothenburg, Sweden.
    Wessberg, Johan
    Department of Physiology, Institute of Neuroscience and Physiology, University of Gothenburg, Sweden.
    A Monte Carlo method for locally multivariate brain mapping.2011In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 56, no 2, 508-516 p.Article in journal (Refereed)
    Abstract [en]

    Locally multivariate approaches to functional brain mapping offer a highly appealing complement to conventional statistics, but require restrictive region-of-interest hypotheses, or, in exhaustive search forms (such as the "searchlight" algorithm; Kriegeskorte et al., 2006), are excessively computer intensive. We therefore propose a non-restrictive, comparatively fast yet highly sensitive method based on Monte Carlo approximation principles where locally multivariate maps are computed by averaging across voxelwise condition-discriminative information obtained from repeated stochastic sampling of fixed-size search volumes. On simulated data containing discriminative regions of varying size and contrast-to-noise ratio (CNR), the Monte Carlo method reduced the required computer resources by as much as 75% compared to the searchlight with no reduction in mapping performance. Notably, the Monte Carlo mapping approach not only outperformed the general linear method (GLM), but also produced higher discriminative voxel detection scores than the searchlight irrespective of classifier (linear or nonlinear support vector machine), discriminative region size or CNR. The improved performance was explained by the information-average procedure, and the Monte Carlo approach yielded mapping sensitivities of a few percent lower than an information-average exhaustive search. Finally, we demonstrate the utility of the algorithm on whole-brain, multi-subject functional magnetic resonance imaging (fMRI) data from a tactile study, revealing that the central representation of gentle touch is spatially distributed in somatosensory, insular and visual regions.

  • 15.
    Björnsdotter, Malin
    et al.
    Department of Physiology, Institute of Neuroscience and Physiology, University of Gothenburg, Göteborg, Sweden.
    Wessberg, Johan
    Department of Physiology, Institute of Neuroscience and Physiology, University of Gothenburg, Göteborg, Sweden.
    Clustered sampling improves random subspace brain mapping2012In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 45, no 6, 2035-2040 p.Article in journal (Refereed)
    Abstract [en]

    Intuitive and efficient, the random subspace ensemble approach provides an appealing solution to the problem of the vast dimensionality of functional magnetic resonance imaging (fMRI) data for maximal-accuracy brain state decoding. Recently, efforts to generate biologically plausible and interpretable maps of brain regions which contribute information to the ensemble decoding task have been made and two approaches have been introduced: globally multivariate random subsampling and locally multivariate Monte Carlo mapping. Both types of maps reflect voxel-wise decoding accuracies averaged across repeatedly randomly sampled voxel subsets, highlighting voxels which consistently participate in high-classification subsets. We compare the mapping sensitivities of the approaches on realistic simulated data containing both locally and globally multivariate information and demonstrate that utilizing the inherent volumetric nature of fMRI through clustered Monte Carlo mapping yields dramatically improved performances in terms of voxel detection sensitivity and efficiency. These results suggest that, unless a priori information specifically dictates a global search, variants of clustered sampling should be the priority for random subspace brain mapping.

  • 16.
    Borga, Magnus
    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, Thord
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Semi-Supervised Learning of Anatomical Manifolds for Atlas-Based Segmentation of Medical Images2016In: Proceedings of the 23rd International Conference on Pattern Recognition (ICPR), IEEE Computer Society, 2016, 3146-3149 p.Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel method for atlas-based segmentation of medical images. The method uses semi- supervised learning of a graph describing a manifold of anatom- ical variations of whole-body images, where unlabelled data are used to find a path with small deformations from the labelled atlas to the target image. The method is evaluated on 36 whole-body magnetic resonance images with manually segmented livers as ground truth. Significant improvement (p < 0.001) was obtained compared to direct atlas-based registration. 

  • 17.
    Borga, Magnus
    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.
    Thomas, E. Louise
    Department of Life Sciences Faculty of Science and Technology University of Westminster, London, United Kingdom.
    Romu, Thobias
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Rosander, Johannes
    Advanced MR Analytics AB, Linköping, Sweden.
    Fitzpatrick, Julie
    Department of Life Sciences Faculty of Science and Technology University of Westminster, London, United Kingdom.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Bell, Jimmy D
    Department of Life Sciences Faculty of Science and Technology University of Westminster, London, United Kingdom.
    Validation of a Fast Method for Quantification of Intra-abdominal and Subcutaneous Adipose Tissue for Large Scale Human Studies2015In: NMR in Biomedicine, ISSN 1099-1492, Vol. 28, no 12, 1747-1753 p.Article in journal (Refereed)
    Abstract [en]

    Central obesity is the hallmark of a number of non-inheritable disorders. The advent of imaging techniques such as magnetic resonance imaging (MRI) has allowed for a fast and accurate assessment of body fat content and distribution. However, image analysis continues to be one of the major obstacles for the use of MRI in large scale studies. In this study we assess the validity of the recently proposed fat-muscle-quantitation-system (AMRATM Profiler) for the quantification of intra-abdominal adipose tissue (IAAT) and abdominal subcutaneous adipose tissue (ASAT) from abdominal MR images.  Abdominal MR images were acquired from 23 volunteers with a broad range of BMIs and analysed using SliceOmatic, the current gold-standard, and the AMRATM Profiler based on a non-rigid image registration of a library of segmented atlases. The results show that there was a highly significant correlation between the fat volumes generated by both analysis methods, (Pearson correlation r = 0.97 p<0.001), with the AMRATM Profiler analysis being significantly faster (~3 mins) than the conventional SliceOmatic approach (~40 mins). There was also excellent agreement between the methods for the quantification of IAAT (AMRA 4.73 ± 1.99 vs SliceOmatic 4.73 ± 1.75 litres, p=0.97). For the AMRATM Profiler analysis, the intra-observer coefficient of variation was 1.6 % for IAAT and 1.1 % for ASAT, the inter-observer coefficient of variation was 1.4 % for IAAT and 1.2 % for ASAT, the intra-observer correlation was 0.998 for IAAT and 0.999 for ASAT, and the inter-observer correlation was 0.999 for both IAAT and ASAT. These results indicate that precise and accurate measures of body fat content and distribution can be obtained in a fast and reliable form by the AMRATM Profiler, opening up the possibility of large-scale human phenotypic studies.

  • 18.
    Borga, Magnus
    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.
    Virtanen, Kirsi A.
    Turku PET Centre, University of Turku, Finland.
    Romu, Thobias
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Persson, Anders
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Nuutila, Pirjo
    Turku PET Centre, University of Turku, Finland.
    Enerbäck, Sven
    Department of Biomedicine, University of Gothenburg, Sweden.
    Brown adipose tissue in humans: detection and functional analysis using PET (Positron Emission Tomography), MRI (Magnetic Resonance Imaging), and DECT (Dual Energy Computed Tomography)2014In: Methods in Enzymology: Methods of Adipose Tissue Biology / [ed] Ormond MacDougald, Elsevier, 2014, 1, 141-159 p.Chapter in book (Other academic)
    Abstract [en]

    Research with the aim to translate findings of the beneficial effects induced by brown adipose tissue (BAT) on metabolism, as seen in various non-human experimental systems to also include human metabolism requires tools that accurately measure how BAT influences human metabolism. This review sets out to discuss such techniques, how they can be used, what they can measure and also some of their limitations. The focus is on detection and functional analysis of human BAT and how this can be facilitated by applying advanced imaging technology such as:  PET (Positron Emission Tomography), MRI (Magnetic Resonance Imaging), and DECT (Dual Energy Computed Tomography).

  • 19.
    Brandl, Miriam B
    et al.
    School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia .
    Beck, Dominik
    School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia .
    Pham, Tuan D
    School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia .
    Application of Fuzzy c-Means and Joint-Feature-Clustering to Detect Redundancies of Image-Features in Drug Combinations Studies of Breast Cancer2011Conference paper (Refereed)
    Abstract [en]

    The high dimensionality of image-based dataset can be a drawback for classification accuracy. In this study, we propose the application of fuzzy c-means clustering, cluster validity indices and the notation of a joint-feature-clustering matrix to find redundancies of image-features. The introduced matrix indicates how frequently features are grouped in a mutual cluster. The resulting information can be used to find data-derived feature prototypes with a common biological meaning, reduce data storage as well as computation times and improve the classification accuracy.

  • 20.
    Bujack, Roxana
    et al.
    Leipzig University, Leipzig, Germany.
    Hotz, Ingrid
    Zuse Institute Berlin, Germany.
    Scheuermann, Gerik
    Leipzig University, Leipzig, Germany.
    Hitzer, Eckhard
    International Christian University, Tokyo, Japan.
    Moment Invariants for 2D Flow Fields Using Normalization in Detail2015In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 21, no 8, 916-929 p.Article in journal (Refereed)
    Abstract [en]

    The analysis of 2D flow data is often guided by the search for characteristic structures with semantic meaning. One way to approach this question is to identify structures of interest by a human observer, with the goal of finding similar structures in the same or other datasets. The major challenges related to this task are to specify the notion of similarity and define respective pattern descriptors. While the descriptors should be invariant to certain transformations, such as rotation and scaling, they should provide a similarity measure with respect to other transformations, such as deformations. In this paper, we propose to use moment invariants as pattern descriptors for flow fields. Moment invariants are one of the most popular techniques for the description of objects in the field of image recognition. They have recently also been applied to identify 2D vector patterns limited to the directional properties of flow fields. Moreover, we discuss which transformations should be considered for the application to flow analysis. In contrast to previous work, we follow the intuitive approach of moment normalization, which results in a complete and independent set of translation, rotation, and scaling invariant flow field descriptors. They also allow to distinguish flow features with different velocity profiles. We apply the moment invariants in a pattern recognition algorithm to a real world dataset and show that the theoretical results can be extended to discrete functions in a robust way.

  • 21.
    Cibis, Merih
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Bustamante, Mariana
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Eriksson, Jonatan
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Carlhäll, Carljohan
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ebbers, Tino
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Creating Hemodynamic Atlases of Cardiac 4D Flow MRI2017In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 46, no 5, 1389-1399 p.Article in journal (Refereed)
    Abstract [en]

    Purpose: Hemodynamic atlases can add to the pathophysiological understanding of cardiac diseases. This study proposes a method to create hemodynamic atlases using 4D Flow magnetic resonance imaging (MRI). The method is demonstrated for kinetic energy (KE) and helicity density (Hd). Materials and Methods: Thirteen healthy subjects underwent 4D Flow MRI at 3T. Phase-contrast magnetic resonance cardioangiographies (PC-MRCAs) and an average heart were created and segmented. The PC-MRCAs, KE, and Hd were nonrigidly registered to the average heart to create atlases. The method was compared with 1) rigid, 2) affine registration of the PC-MRCAs, and 3) affine registration of segmentations. The peak and mean KE and Hd before and after registration were calculated to evaluate interpolation error due to nonrigid registration. Results: The segmentations deformed using nonrigid registration overlapped (median: 92.3%) more than rigid (23.1%, P amp;lt; 0.001), and affine registration of PC-MRCAs (38.5%, P amp;lt; 0.001) and affine registration of segmentations (61.5%, P amp;lt; 0.001). The peak KE was 4.9 mJ using the proposed method and affine registration of segmentations (P50.91), 3.5 mJ using rigid registration (P amp;lt; 0.001), and 4.2 mJ using affine registration of the PC-MRCAs (P amp;lt; 0.001). The mean KE was 1.1 mJ using the proposed method, 0.8 mJ using rigid registration (P amp;lt; 0.001), 0.9 mJ using affine registration of the PC-MRCAs (P amp;lt; 0.001), and 1.0 mJ using affine registration of segmentations (P50.028). The interpolation error was 5.262.6% at mid-systole, 2.863.8% at early diastole for peak KE; 9.669.3% at mid-systole, 4.064.6% at early diastole, and 4.964.6% at late diastole for peak Hd. The mean KE and Hd were not affected by interpolation. Conclusion: Hemodynamic atlases can be obtained with minimal user interaction using nonrigid registration of 4D Flow MRI. Level of Evidence: 2 Technical Efficacy: Stage 1

  • 22.
    Cros, Olivier
    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.
    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.
    Pauwels, Elin
    University of Ghent, Belgium.
    Dirckx, Joris J. J.
    University of Antwerp, Belgium.
    Gaihede, Michael
    Aalborg University Hospital, Denmark.
    Micro-channels in the mastoid anatomy. Indications of a separate blood supply of the air cell system mucosa by micro-CT scanning2013In: Hearing Research, ISSN 0378-5955, E-ISSN 1878-5891, Vol. 301, 60-65 p.Article in journal (Refereed)
    Abstract [en]

    The mastoid air cell system has traditionally been considered to have a passive role in gas exchange and pressure regulation of the middle ear possibly with some acoustic function. However, more evidence has focused on the mucosa of the mastoid, which may play a more active role in regulation of middle ear pressure.

    In this study we have applied micro-CT scanning on a series of three human temporal bones. This approach greatly enhances the resolution (40–60 μm), so that we have discovered anatomical details, which has not been reported earlier. Thus, qualitative analysis using volume rendering has demonstrated notable micro-channels connecting the surface of the compact bone directly to the mastoid air cells as well as forming a network of connections between the air cells. Quantitative analysis on 2D slices was employed to determine the average diameter of these micro-channels (158 μm; range = 40–440 μm) as well as their density at a localized area (average = 75 cm−2; range = 64–97 cm−2).

    These channels are hypothesized to contain a separate vascular supply for the mastoid mucosa. However, future studies of the histological structure of the micro-channels are warranted to confirm the hypothesis. Studies on the mastoid mucosa and its blood supply may improve our knowledge of its physiological properties, which may have important implications for our understanding of the pressure regulation of the middle ear.

  • 23.
    Dahlqvist Leinhard, Olof
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Linge, Jennifer
    Advanced MR Analytics AB, Linköping, Sweden.
    West, Janne
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Bell, Jimmy
    Westminster University, London, UK.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering.
    Body Composition Profiling using MRI - Normative Data for Subjects with Cardiovascular Disease Extracted from the UK Biobank Imaging Cohort2016Conference paper (Other academic)
    Abstract [en]

    PURPOSE

    To describe the distribution of MRI-derived body composition measurements in subjects with cardiovascular disease (CVD) compared to subjects without any history of CVD.

    METHOD AND MATERIALS

    1864 males and 2036 females with an age range from 45 to 78 years from the UK Biobank imaging study were included in the study. Visceral adipose tissue volume normalized with height2 (VATi), total abdominal adipose tissue volume normalized with height2 (ATATi), total lean thigh muscle volume normalized with body weight (muscle ratio) and liver proton density fat fraction (PDFF) were measured with a 2-point Dixon imaging protocol covering neck to knee and a 10-point Dixon single slice protocol positioned within the liver using a 1.5T MR-scanner (Siemens, Germany). The MR-images were analyzed using AMRA® Profiler research (AMRA, Sweden). 213 subjects with history of cardiovascular events (angina, heart attack, or stroke) (event group) were age and gender matched to subjects with high blood pressure (HBP group), and subjects without CVD (controls).Kruskal-Wallis and Mann-Whitney U tests were used to test the observed differences for each measurement and group without correction for multiple comparisons.

    RESULTS

    VATi in the event group was 1.73 (1.13 - 2.32) l/m2 (median, 25%-75% percentile) compared to 1.68 (1.19 - 2.23) in the HBP group, and 1.30 (0.82-1.87) in the controls. ATATi in the event group was 4.31 (2.90-5.39) l/m2 compared to 4.05 (3.07-5.12) in the HBP group, and 3.48 (2.48-4.61) in the controls. Muscle ratio in the event group was 0.13 (0.12 - 0.15) l/kg as well as in the HBP group, compared to 0.14 (0.12 - 0.15) in the controls. Liver PDFF in the event group was 2.88 (1.77 - 7.72) % compared to 3.44 (2.04-6.18) in the HBP group, and 2.50 (1.58 - 5.15) in the controls. Kruskal-Wallis test showed significant differences for all variables and group comparisons (p<0.007). The post hoc test showed significant differences comparing the controls to both the event group and the HBP group. These were more significant for VATi and ATATi (p<10-4) than for muscle ratio and PDFF (p<0.03). No significant differences were detected between the event group and the HBP group.

    CONCLUSION

    Cardiovascular disease is strongly associated with high VATi, liver fat, and ATATi, and with low muscle ratio.

    CLINICAL RELEVANCE/APPLICATION

    The metabolic syndrome component in CVD can be effectively described using MRI-based body composition profiling.

  • 24.
    Dahlqvist Leinhard, Olof
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Linge, Jennifer
    Advanced MR Analytics AB, Linköping, Sweden.
    West, Janne
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Bell, Jimmy
    Westminster University, London, UK.
    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.
    Body Composition Profiling using MRI - Normative Data for Subjects with Diabetes Extracted from the UK Biobank Imaging Cohort2016Conference paper (Other academic)
    Abstract [en]

    PURPOSE

    To describe the distribution of MRI derived body composition measurements in subjects with diabetes mellitus (DM) compared to subjects without diabetes.

    METHOD AND MATERIALS

    3900 subjects (1864 males and 2036 females) from the UK Biobank imaging study were included in the study. The age range was 45 to 78 years. Visceral adipose tissue volume normalized with height2 (VATi), total abdominal adipose tissue volume normalized with height2 (ATATi), total lean thigh muscle volume normalized with body weight (muscle ratio) and liver proton density fat fraction (PDFF) were measured with a 6 minutes 2-point Dixon imaging protocol covering neck to knee and a 10-point Dixon single axial slice protocol positioned within the liver using a 1.5T MR-scanner (Siemens, Germany). The MR-images were analyzed using AMRA® Profiler research (AMRA, Sweden). 194 subjects with clinically diagnosed DM (DM group) were age and gender matched to subjects without DM (control group). For each variable and group, the median, 25%-percentile and 75%-percentile was calculated. Mann-Whitney U test was used to test the observed differences.

    RESULTS

    VATi in the DM group was 2.13 (1.43-2.62) l/m2 (median, 25% - 75% percentile) compared to 1.32 (0.86 - 1.79) l/m2 in the control group. ATATi in the DM group was 4.94 (3.86-6.19) l/m2 compared to 3.40 (2.56 - 4.70) l/m2 in the control group. Muscle ratio in the DM group was 0.13 (0.11 - 0.14) l/kg compared to 0.14 (0.12 - 0.15) l/kg in the control group. Liver PDFF in the DM group was 7.23 (2.68 - 13.26) % compared to 2.49 (1.53 - 4.73) % in the control group. Mann-Whitney U test detected significant differences between the DM group and the control group for all variables (p<10-5).

    CONCLUSION

    DM is strongly associated with high visceral fat, liver fat, and total abdominal fat, and low muscle ratio.

    CLINICAL RELEVANCE/APPLICATION

    Body composition profiling shows high potential to provide direct biomarkers to improve characterization and early diagnosis of DM.

  • 25.
    Dahlqvist Leinhard, Olof
    et al.
    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.
    Romu, Thobias
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Karlsson, Anette
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    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.
    High resolution isotropic whole-­‐body symmetrically sampled two point Dixon acquisition imaging at 3T2012In: ISMRM workshop on Fat-­‐Water Separation: Insights, Applications & Progress in MRI, 2012Conference paper (Other academic)
  • 26.
    Dahlqvist Leinhard, Olof
    et al.
    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.
    Romu, Thobias
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Kihlberg, Johan
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences.
    Gjellan, Solveig
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences.
    Zanjani, Sepehr
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping.
    Nyström, Fredrik
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Centre, Department of Endocrinology.
    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.
    Validation of whole-­‐body adipose tissue quantification using air displacement plethysmometry2012In: ISMRM workshop on Fat-­‐Water Separation: Insights, Applications & Progress in MRI, 2012Conference paper (Other academic)
  • 27.
    Danielis, Alessandro
    et al.
    CNR, Italy.
    Giorgi, Daniela
    CNR, Italy.
    Larsson, Marcus
    Linköping University, Department of Biomedical Engineering, Biomedical Instrumentation. Linköping University, Faculty of Science & Engineering.
    Strömberg, Tomas
    Linköping University, Department of Biomedical Engineering, Biomedical Instrumentation. Linköping University, Faculty of Science & Engineering.
    Colantonio, Sara
    CNR, Italy.
    Salvetti, Ovidio
    CNR, Italy.
    Lip segmentation based on Lambertian shadings and morphological operators for hyper-spectral images2017In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 63, 355-370 p.Article in journal (Refereed)
    Abstract [en]

    Lip segmentation is a non-trivial task because the colour difference between the lip and the skin regions maybe not so noticeable sometimes. We propose an automatic lip segmentation technique for hyper-spectral images from an imaging prototype with medical applications. Contrarily to many other existing lip segmentation methods, we do not use colour space transformations to localise the lip area. As input image, we use for the first time a parametric blood concentration map computed by using narrow spectral bands. Our method mainly consists of three phases: (i) for each subject generate a subset of face images enhanced by different simulated Lambertian illuminations, then (ii) perform lip segmentation on each enhanced image by using constrained morphological operations, and finally (iii) extract features from Fourier-based modeled lip boundaries for selecting the lip candidate. Experiments for testing our approach are performed under controlled conditions on volunteers and on a public hyper-spectral dataset. Results show the effectiveness of the algorithm against low spectral range, moustache, and noise.

  • 28.
    Davidsson, Anette
    et al.
    Östergötlands Läns Landsting, Heart and Medicine Centre, Department of Clinical Physiology UHL.
    Georgiopoulos, C
    Östergötlands Läns Landsting, Heart and Medicine Centre, Department of Clinical Physiology UHL.
    Gustafsson, Agnetha
    Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics UHL.
    Zachrisson, Helene
    Östergötlands Läns Landsting, Heart and Medicine Centre, Department of Clinical Physiology UHL.
    Evaluation and comparison of quantification tools for early diagnosis of Parkinson's disease with DaTSCAN SPECT.2011Conference paper (Refereed)
  • 29.
    Davidsson, Anette
    et al.
    Östergötlands Läns Landsting, Heart and Medicine Centre, Department of Clinical Physiology UHL.
    Georgiopoulos, C
    Östergötlands Läns Landsting, Heart and Medicine Centre, Department of Clinical Physiology UHL.
    Gustafsson, Agnetha
    Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics UHL.
    Zachrisson, Helene
    Östergötlands Läns Landsting, Heart and Medicine Centre, Department of Clinical Physiology UHL.
    Utvärdering och jämförelse av kvantifieringsverktyg för tidig diagnostik av Parkinsons sjukdom med DaTSCAN SPECT2011Conference paper (Other academic)
  • 30.
    De Geer, Jakob
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    On the use of computed tomography in cardiac imaging2016Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Background

    Cardiac Computed Tomography Angiography (CCTA) is becoming increasingly useful in the work‐up of coronary artery disease (CAD). Several potential methods for increasing the diagnostic yield of cardiac CT are available.

    Purpose

    Study I: To investigate whether the use of a 2‐D, non‐linear adaptive noise reduction filter can improve CCTA image quality.

    Study II: To evaluate the variation in adenosine stress dynamic CT perfusion (CTP) blood flow as compared to stress 99mTc SPECT. Secondly, to compare the perfusion results from manual and automatic myocardial CTP segmentation.

    Study III: To evaluate the accuracy of non‐invasive, CCTA‐derived Fractional Flow Reserve (cFFR).

    Study IV: To evaluate the prognostic value of CCTA in terms of major adverse cardiac events (MACE).

    Materials and methods

    Study I: Single images from 36 consecutive CCTA exams performed with two different dose levels were used. Image quality in full dose, low‐dose and noise‐reduced low‐dose images was graded using visual grading analysis. Image noise was measured.

    Study II: CTP and SPECT were performed in 17 patients, and the variation in per AHA‐segment blood flow was evaluated and compared. CTP results from manual and automated image segmentation were compared.

    Study III: CCTA datasets from 21 patients were processed using cFFR software and the results compared to the corresponding invasively measured FFR (invFFR).

    Study IV: 1205 consecutive patients with chest pain of unknown origin underwent CCTA. Baseline data and data on subsequent MACE were retrieved from relevant registries. Survival, hazard ratios and the three‐year incidence of cardiac events and readmissions were calculated.

    Results

    Study I: There was significant improvement in perceived image quality for all criteria when the filter was applied, and a significant decrease in image noise.

    Study II: The correlation coefficients for CTP vs. SPECT were 0.38 and 0.41 (p<0.001, for manual and automated segmentation respectively. Mean per patient CTP blood flow in normal segments varied between 94‐183 ml/100 ml tissue/min for manual segmentation, and 104‐196 ml/100 ml tissue/min for automated segmentation. The Spearman rank correlation coefficient for manual vs. automated segmentation CTP was ρ = 0.88 (p<0.001) and the Intraclass Correlation Coefficient (ICC) was 0.93 (p<0.001).

    Study III: The Spearman rank correlation coefficient for cFFR vs. invFFR was ρ = 0.77 (p<0.001) and the ICC was 0.73 (p<0.001). Sensitivity, specificity, positive predictive value and negative predictive value for significant stenosis (FFR<0.80, per vessel) were 0.83, 0.76, 0.56 and 0.93 respectively.

    Study IV: The hazard ratio for non‐obstructive CAD vs. normal coronary arteries was 5.13 (95% C.I 1.03‐25.43, p<0.05), and 151.40 (95% C.I 37.03‐619.08, p<0.001) for obstructive CAD vs. normal coronary arteries. The three‐year incidence of MACE was 1.1% for patients with normal vessels on CCTA, 2.5% for patients with non‐obstructive CAD and 42.7% for patients with obstructive CAD (p<0.001).

    Conclusions:

    Study I: Image quality and noise levels of low dose images were significantly improved with the filter, even though the improvement was small compared to the image quality of the corresponding diastolic full‐dose images.

    Study II: Correlation between dynamic CTP and SPECT was positive but weak. There were large variations in CTP blood flow in normal segments on SPECT, rendering the definition of an absolute cut‐off value for normal vs. ischemic myocardium difficult. Manual and automatic segmentation were equally useful.

    Study III: The correlation between cFFR and invFFR was good, indicating that noninvasively estimated cFFR performs on a similar level as invasively measure FFR.

    Study IV: The long‐term risk for MACE was very low in patients without obstructive CAD on CCTA, though there seemed to be a substantial increase in the risk for MACE even in patients with non‐obstructive CAD as compared to normal coronary arteries. In addition, even patients with normal coronary arteries or non‐obstructive CAD continued to have a substantial number of readmissions for chest pain or angina pectoris.

    List of papers
    1. The efficacy of 2D, non-linear noise reduction filtering in cardiac imaging: a pilot study
    Open this publication in new window or tab >>The efficacy of 2D, non-linear noise reduction filtering in cardiac imaging: a pilot study
    2011 (English)In: Acta Radiologica, ISSN 0284-1851, E-ISSN 1600-0455, Vol. 52, no 7, 716-722 p.Article in journal (Refereed) Published
    Abstract [en]

    Background: Computed tomography (CT) is becoming increasingly popular as a non-invasive method for visualizing the coronary arteries but patient radiation doses are still an issue. Postprocessing filters such as 2D adaptive non-linear filters might help to reduce the dose without loss of image quality. less thanbrgreater than less thanbrgreater thanPurpose: To investigate whether the use of a 2D, non-linear adaptive noise reduction filter can improve image quality in cardiac computed tomography angiography (CCTA). less thanbrgreater than less thanbrgreater thanMaterial and Methods: CCTA examinations were performed in 36 clinical patients on a dual source CT using two patient dose levels: maximum dose during diastole and reduced dose (20% of maximum dose) during systole. One full-dose and one reduced-dose image were selected from each of the examinations. The reduced-dose image was duplicated and one copy postprocessed using a 2D non-linear adaptive noise reduction filter, resulting in three images per patient. Image quality was assessed using visual grading with three criteria from the European guidelines for assessment of image quality and two additional criteria regarding the left main artery and the overall image quality. Also, the HU value and its standard deviation were measured in the ascending and descending aorta. Data were analyzed using Visual Grading Regression and paired t-test. less thanbrgreater than less thanbrgreater thanResult: For all five criteria, there was a significant (P andlt; 0.01 or better) improvement in perceived image quality when comparing postprocessed low-dose images with low-dose images without noise reduction. Comparing full dose images with postprocessed low-dose images resulted in a considerably larger, significant (P andlt; 0.001) difference. Also, there was a significant reduction of the standard deviation of the HU values in the ascending and descending aorta when comparing postprocessed low-dose images with low-dose images without postprocessing. less thanbrgreater than less thanbrgreater thanConclusion: Even with an 80% dose reduction, there was a significant improvement in the perceived image quality when using a 2D noise-reduction filter, though not approaching the quality of full-dose images. This indicates that cardiac CT examinations could benefit from noise-reducing postprocessing with 2D non-linear adaptive filters.

    Place, publisher, year, edition, pages
    Informa Healthcare / Wiley-Blackwell / Royal Society of Medicine Press, 2011
    Keyword
    Cardiac, CT angiography, heart, adults, image manipulation
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-71803 (URN)10.1258/ar.2011.100511 (DOI)000295759600007 ()
    Available from: 2011-11-04 Created: 2011-11-04 Last updated: 2016-05-24
    2. Large variation in blood flow between left ventricular segments, as detected by adenosine stress dynamic CT perfusion.
    Open this publication in new window or tab >>Large variation in blood flow between left ventricular segments, as detected by adenosine stress dynamic CT perfusion.
    Show others...
    2015 (English)In: Clinical Physiology and Functional Imaging, ISSN 1475-0961, E-ISSN 1475-097X, Vol. 35, no 4, 291-300 p.Article in journal (Refereed) Published
    Abstract [en]

    BACKGROUND: Dynamic cardiac CT perfusion (CTP) is based on repeated imaging during the first-pass contrast agent inflow. It is a relatively new method that still needs validation.

    PURPOSE: To evaluate the variation in adenosine stress dynamic CTP blood flow as compared to (99m) Tc SPECT. Secondarily, to compare manual and automatic segmentation.

    METHODS: Seventeen patients with manifest coronary artery disease were included. Nine were excluded from evaluation for various reasons. All patients were examined with dynamic stress CTP and stress/rest SPECT. CTP blood flow was compared with SPECT on a per segment basis. Results for manual and automated AHA segmentation were compared.

    RESULTS: CTP showed a positive correlation with SPECT, with correlation coefficients of 0·38 and 0·41 for manual and automatic segmentation, respectively (P<0·0001). There was no significant difference between the correlation coefficients of the manual and automated segmentation procedures (P = 0·75). The average per individual global CTP blood flow value for normal segments varied by a factor of 1·9 (manual and automatic segmentation). For the whole patient group, the CTP blood flow value in normal segments varied by a factor of 2·9/2·7 (manual/automatic segmentation). Within each patient, the average per segment blood flow in normal segments varied by a factor of 1·3-2·0/1·2-2·1 (manual/automatic segmentation).

    CONCLUSION: A positive but rather weak correlation was found between CTP and (99m) Tc SPECT. Large variations in CTP blood flow suggest that a cut-off value for stress myocardial blood flow is inadequate to detect ischaemic segments. Dynamic CTP is hampered by a limited coverage.

    National Category
    Clinical Medicine
    Identifiers
    urn:nbn:se:liu:diva-113400 (URN)10.1111/cpf.12163 (DOI)000356312800007 ()24842265 (PubMedID)
    Available from: 2015-01-17 Created: 2015-01-17 Last updated: 2016-05-24
    3. Software-based on-site estimation of fractional flow reserve using standard coronary CT angiography data.
    Open this publication in new window or tab >>Software-based on-site estimation of fractional flow reserve using standard coronary CT angiography data.
    Show others...
    2016 (English)In: Acta Radiologica, ISSN 0284-1851, E-ISSN 1600-0455, Vol. 57, no 10, 1186-1192 p.Article in journal (Refereed) Published
    Abstract [en]

    BACKGROUND: The significance of a coronary stenosis can be determined by measuring the fractional flow reserve (FFR) during invasive coronary angiography. Recently, methods have been developed which claim to be able to estimate FFR using image data from standard coronary computed tomography angiography (CCTA) exams.

    PURPOSE: To evaluate the accuracy of non-invasively computed fractional flow reserve (cFFR) from CCTA.

    MATERIAL AND METHODS: A total of 23 vessels in 21 patients who had undergone both CCTA and invasive angiography with FFR measurement were evaluated using a cFFR software prototype. The cFFR results were compared to the invasively obtained FFR values. Correlation was calculated using Spearman's rank correlation, and agreement using intraclass correlation coefficient (ICC). Sensitivity, specificity, accuracy, negative predictive value, and positive predictive value for significant stenosis (defined as both FFR ≤0.80 and FFR ≤0.75) were calculated.

    RESULTS: The mean cFFR value for the whole group was 0.81 and the corresponding mean invFFR value was 0.84. The cFFR sensitivity for significant stenosis (FFR ≤0.80/0.75) on a per-lesion basis was 0.83/0.80, specificity was 0.76/0.89, and accuracy 0.78/0.87. The positive predictive value was 0.56/0.67 and the negative predictive value was 0.93/0.94. The Spearman rank correlation coefficient was ρ = 0.77 (P < 0.001) and ICC = 0.73 (P < 0.001).

    CONCLUSION: This particular CCTA-based cFFR software prototype allows for a rapid, non-invasive on-site evaluation of cFFR. The results are encouraging and cFFR may in the future be of help in the triage to invasive coronary angiography.

    Place, publisher, year, edition, pages
    Sage Publications, 2016
    Keyword
    Cardiac; computed tomography angiography (CTA); heart; arteries; adults; computer applications – detection/diagnosis
    National Category
    Radiology, Nuclear Medicine and Medical Imaging
    Identifiers
    urn:nbn:se:liu:diva-123579 (URN)10.1177/0284185115622075 (DOI)000382967500007 ()26691914 (PubMedID)
    Note

    Funding agencies: Department of Radiology, Region Ostergotland; Swedish Heart-Lung-foundation [20120449]

    Available from: 2015-12-29 Created: 2015-12-29 Last updated: 2016-10-13Bibliographically approved
  • 31.
    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, 3860-3860 p.Conference 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.

  • 32.
    Dyverfeldt, Petter
    et al.
    University of California, San Francisco, USA.
    Hope, Michael D.
    University of California, San Francisco, USA.
    Tseng, Elaine E.
    University of California, San Francisco, USA.
    Saloner, David
    University of California, San Francisco, USA.
    Magnetic Resonance Measurement of Turbulent Kinetic Energy for the Estimation of Irreversible Pressure Loss in Aortic Stenosis2013In: JACC Cardiovascular Imaging, ISSN 1936-878X, Vol. 6, no 1, 64-71 p.Article in journal (Refereed)
    Abstract [en]

    Objectives

    The authors sought to measure the turbulent kinetic energy (TKE) in the ascending aorta of patients with aortic stenosis and to assess its relationship to irreversible pressure loss.

    Background

    Irreversible pressure loss caused by energy dissipation in post-stenotic flow is an important determinant of the hemodynamic significance of aortic stenosis. The simplified Bernoulli equation used to estimate pressure gradients often misclassifies the ventricular overload caused by aortic stenosis. The current gold standard for estimation of irreversible pressure loss is catheterization, but this method is rarely used due to its invasiveness. Post-stenotic pressure loss is largely caused by dissipation of turbulent kinetic energy into heat. Recent developments in magnetic resonance flow imaging permit noninvasive estimation of TKE.

    Methods

    The study was approved by the local ethics review board and all subjects gave written informed consent. Three-dimensional cine magnetic resonance flow imaging was used to measure TKE in 18 subjects (4 normal volunteers, 14 patients with aortic stenosis with and without dilation). For each subject, the peak total TKE in the ascending aorta was compared with a pressure loss index. The pressure loss index was based on a previously validated theory relating pressure loss to measures obtainable by echocardiography.

    Results

    The total TKE did not appear to be related to global flow patterns visualized based on magnetic resonance–measured velocity fields. The TKE was significantly higher in patients with aortic stenosis than in normal volunteers (p < 0.001). The peak total TKE in the ascending aorta was strongly correlated to index pressure loss (R2 = 0.91).

    Conclusions

    Peak total TKE in the ascending aorta correlated strongly with irreversible pressure loss estimated by a well-established method. Direct measurement of TKE by magnetic resonance flow imaging may, with further validation, be used to estimate irreversible pressure loss in aortic stenosis.

  • 33.
    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)
  • 34.
    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, 1073-1094 p.Article, 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.

  • 35.
    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).

  • 36.
    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, 7900-7905 p.Article 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.

  • 37.
    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.

  • 38.
    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, 1000-1008 p.Conference 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.

  • 39.
    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.

  • 40.
    Falk, Martin
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Hotz, Ingrid
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Ljung, Patric
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Treanor, Darren
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Leeds Teaching Hospitals NHS Trust, United Kingdom.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Lundström, Claes
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Transfer Function Design Toolbox for Full-Color Volume Datasets2017Conference paper (Refereed)
    Abstract [en]

    In this paper, we tackle the challenge of effective Transfer Function (TF) design for Direct Volume Rendering (DVR) of full-color datasets. We propose a novel TF design toolbox based on color similarity which is used to adjust opacity as well as replacing colors. We show that both CIE L*u*v* chromaticity and the chroma component of YCbCr are equally suited as underlying color space for the TF widgets. In order to maximize the area utilized in the TF editor, we renormalize the color space based on the histogram of the dataset. Thereby, colors representing a higher share of the dataset are depicted more prominently, thus providing a higher sensitivity for fine-tuning TF widgets. The applicability of our TF design toolbox is demonstrated by volume ray casting challenging full-color volume data including the visible male cryosection dataset and examples from 3D histology.

  • 41.
    Feng, Louis
    et al.
    University of California, Davis.
    Hotz, Ingrid
    Zuse Institue Berlin.
    Hamann, Bernd
    University of California, Davis, USA.
    Joy, Ken
    University of California, Davis, USA.
    Anisotropic Noise Samples2008In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 14, no 2, 342-354 p.Article in journal (Refereed)
    Abstract [en]

    We present a practical approach to generate stochastic anisotropic samples with Poisson-disk characteristic over a two-dimensional domain. In contrast to isotropic samples, we understand anisotropic samples as non-overlapping ellipses whose size and density match a given anisotropic metric. Anisotropic noise samples are useful for many visualization and graphics applications. The spot samples can be used as input for texture generation, e.g., line integral convolution (LIC), but can also be used directly for visualization. The definition of the spot samples using a metric tensor makes them especially suitable for the visualization of tensor fields that can be translated into a metric. Our work combines ideas from sampling theory and mesh generation. To generate these samples with the desired properties we construct a first set of non-overlapping ellipses whose distribution closely matches the underlying metric. This set of samples is used as input for a generalized anisotropic Lloyd relaxation to distribute noise samples more evenly. Instead of computing the Voronoi tessellation explicitly, we introduce a discrete approach which combines the Voronoi cell and centroid computation in one step. Our method supports automatic packing of the elliptical samples, resulting in textures similar to those generated by anisotropic reaction-diffusion methods. We use Fourier analysis tools for quality measurement of uniformly distributed samples. The resulting samples have nice sampling properties, for example, they satisfy a blue noise property where low frequencies in the power spectrum are reduced to a minimum.

  • 42.
    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).
    Robust Image Registration for Improved Clinical Efficiency: Using Local Structure Analysis and Model-Based Processing2013Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Medical imaging plays an increasingly important role in modern healthcare. In medical imaging, it is often relevant to relate different images to each other, something which can prove challenging, since there rarely exists a pre-defined mapping between the pixels in different images. Hence, there is a need to find such a mapping/transformation, a procedure known as image registration. Over the years, image registration has been proved useful in a number of clinical situations. Despite this, current use of image registration in clinical practice is rather limited, typically only used for image fusion. The limited use is, to a large extent, caused by excessive computation times, lack of established validation methods/metrics and a general skepticism toward the trustworthiness of the estimated transformations in deformable image registration.

    This thesis aims to overcome some of the issues limiting the use of image registration, by proposing a set of technical contributions and two clinical applications targeted at improved clinical efficiency. The contributions are made in the context of a generic framework for non-parametric image registration and using an image registration method known as the Morphon. 

    In image registration, regularization of the estimated transformation forms an integral part in controlling the registration process, and in this thesis, two regularizers are proposed and their applicability demonstrated. Although the regularizers are similar in that they rely on local structure analysis, they differ in regard to implementation, where one is implemented as applying a set of filter kernels, and where the other is implemented as solving a global optimization problem. Furthermore, it is proposed to use a set of quadrature filters with parallel scales when estimating the phase-difference, driving the registration. A proposal that brings both accuracy and robustness to the registration process, as shown on a set of challenging image sequences. Computational complexity, in general, is addressed by porting the employed Morphon algorithm to the GPU, by which a performance improvement of 38-44x is achieved, when compared to a single-threaded CPU implementation.

    The suggested clinical applications are based upon the concept paint on priors, which was formulated in conjunction with the initial presentation of the Morphon, and which denotes the notion of assigning a model a set of properties (local operators), guiding the registration process. In this thesis, this is taken one step further, in which properties of a model are assigned to the patient data after completed registration. Based upon this, an application using the concept of anatomical transfer functions is presented, in which different organs can be visualized with separate transfer functions. This has been implemented for both 2D slice visualization and 3D volume rendering. A second application is proposed, in which landmarks, relevant for determining various measures describing the anatomy, are transferred to the patient data. In particular, this is applied to idiopathic scoliosis and used to obtain various measures relevant for assessing spinal deformity. In addition, a data analysis scheme is proposed, useful for quantifying the linear dependence between the different measures used to describe spinal deformities.

    List of papers
    1. Adaptive anisotropic regularization of deformation fields for non-rigid registration using the Morphon framework
    Open this publication in new window or tab >>Adaptive anisotropic regularization of deformation fields for non-rigid registration using the Morphon framework
    2010 (English)In: IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE , 2010, 473-476 p.Conference paper, Published paper (Refereed)
    Abstract [en]

    Image registration is a crucial task in many applications and applied in a variety of different areas. In addition to the primary task of image alignment, the deformation field is valuable when studying structural/volumetric changes in the brain. In most applications a regularizing term is added to achieve a smoothly varying deformation field. This can sometimes cause conflicts in situations of local complex deformations. In this paper we present a new regularizer, which aims at handling local complex deformations while maintaining an overall smooth deformation field. It is based on an adaptive anisotropic regularizer and its usefulness is demonstrated by two examples, one synthetic and one with real MRI data from a pre- and post-op situation with normal pressure hydrocephalus.

    Place, publisher, year, edition, pages
    IEEE, 2010
    Series
    IEEE International Conference on Acoustics, Speech and Signal Processing. Proceedings, ISSN 1520-6149
    National Category
    Medical and Health Sciences Biomedical Laboratory Science/Technology
    Identifiers
    urn:nbn:se:liu:diva-56398 (URN)10.1109/ICASSP.2010.5495704 (DOI)000287096000116 ()978-1-4244-4296-6 (ISBN)978-1-4244-4295-9 (ISBN)
    Conference
    35th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2010), 14-19 March 2010, Dallas, Texas, USA
    Funder
    Swedish Research Council, 2007-4786
    Available from: 2010-05-10 Created: 2010-05-10 Last updated: 2014-09-25
    2. Globally Optimal Displacement Fields Using Local Tensor Metric
    Open this publication in new window or tab >>Globally Optimal Displacement Fields Using Local Tensor Metric
    2012 (English)In: Image Processing (ICIP), 2012 19th IEEE International Conference on, 2012, 2957-2960 p.Conference paper, Poster (with or without abstract) (Other academic)
    Abstract [en]

    In this paper, we propose a novel algorithm for regularizing displacement fields in image registration. The method uses the local structure tensor and gradients of the displacement field to impose a local metric, which is then used optimizing a global cost function. The method allows for linear operators, such as tensors and differential operators modeling the underlying physical anatomy of the human body in medical images. The algorithm is tested using output from the Morphon image registration algorithm on MRI data as well as synthetic test data and the result is compared to the initial displacement field. The results clearly demonstrate the power of the method and the unique features brought forth through the global optimization approach.

    Keyword
    Image Processing, Image Registration, Regularization, Optimization, Tensor
    National Category
    Medical Image Processing Signal Processing
    Identifiers
    urn:nbn:se:liu:diva-81947 (URN)10.1109/ICIP.2012.6467520 (DOI)978-1-4673-2534-9 (ISBN)
    Conference
    2012 IEEE International Conference on Image Processing, September 30 - October 3, 2012, Orlando, Florida, USA
    Projects
    Dynamic Context Atlases for Image Denoising and Patient Safety
    Funder
    Swedish Research Council, 2011-5176Swedish Research Council, 2007-4786
    Available from: 2012-09-26 Created: 2012-09-26 Last updated: 2015-04-17Bibliographically approved
    3. Parallel Scales for More Accurate Displacement Estimation in Phase-Based Image Registration
    Open this publication in new window or tab >>Parallel Scales for More Accurate Displacement Estimation in Phase-Based Image Registration
    2010 (English)In: Pattern Recognition (ICPR), 2010, IEEE Computer Society, 2010, 2329-2332 p.Conference paper, Published paper (Refereed)
    Abstract [en]

    Phase-based methods are commonly applied in image registration. When working with phase-difference methods only a single is employed, although the algorithms are normally iterated over multiple scales, whereas phase-congruency methods utilize the phase from multiple scales simultaneously. This paper presents an extension to phase-difference methods employing parallel scales to achieve more accurate displacements. Results are also presented clearly favouring the use of parallel scales over single scale in more than 95% of the 120 tested cases. 

    Place, publisher, year, edition, pages
    IEEE Computer Society, 2010
    Series
    International Conference on Pattern Recognition, ISSN 1051-4651
    National Category
    Medical Image Processing
    Identifiers
    urn:nbn:se:liu:diva-59332 (URN)10.1109/ICPR.2010.570 (DOI)
    Conference
    20th International Conference on pattern Recognition (ICPR 2010), 23-26 August 2010, Istanbul, Turkey
    Funder
    Swedish Research Council, 2007-4786
    Available from: 2012-06-27 Created: 2010-09-13 Last updated: 2013-09-12Bibliographically approved
    4. Phase-Based Non-Rigid 3D Image Registration - From Minutes to Seconds Using CUDA
    Open this publication in new window or tab >>Phase-Based Non-Rigid 3D Image Registration - From Minutes to Seconds Using CUDA
    2011 (English)Conference paper, Published paper (Other academic)
    Abstract [en]

    Image registration is a well-known concept within the medical image domain and has been shown to be useful in a number of dierent tasks. However, due to sometimes long processing times, image registration is not fully utilized in clinical workows, where time is an important factor. During the last couple of years, a number of signicant projects have been introduced to make the computational power of GPUs available to a wider audience, where the most well known is CUDA. In this paper we present, with the aid of CUDA, a speedup in the range of 38-44x (from 29 minutes to 40 seconds) when implementing a phasebased non-rigid image registration algorithm, known as the Morphon, on a single GPU. The achieved speedup is in the same magnitude as the speedups reported from other non-rigid registration algorithms fully ported to the GPU. Given the impressive speedups, both reported in this paper and other papers, we therefore consider that it is now feasible to eectively integrate image registration into various clinical workows, where time is a critical factor.

    National Category
    Medical Image Processing
    Identifiers
    urn:nbn:se:liu:diva-75387 (URN)
    Conference
    Joint MICCAI Workshop on High Performance and Distributed Computing for Medical Imaging, HP-MICCAI, September 22nd, Toronto, Canada
    Funder
    Swedish Research Council, 2007-4786
    Available from: 2012-03-01 Created: 2012-02-28 Last updated: 2013-09-12Bibliographically approved
    5. Model-Based Transfer Functions for Efficient Visualization of Medical Image Volumes
    Open this publication in new window or tab >>Model-Based Transfer Functions for Efficient Visualization of Medical Image Volumes
    2011 (English)In: Image Analysis: 17th Scandinavian Conference, SCIA 2011, Ystad, Sweden, May 2011. Proceedings, Springer Berlin/Heidelberg, 2011, Vol. 6688/2011, 592-603 p.Conference paper, Published paper (Refereed)
    Abstract [en]

    The visualization of images with a large dynamic range is a difficult task and this is especially the case for gray-level images. In radiology departments, this will force radiologists to review medical images several times, since the images need to be visualized with several different contrast windows (transfer functions) in order for the full information content of each image to be seen. Previously suggested methods for handling this situation include various approaches using histogram equalization and other methods for processing the image data. However, none of these utilize the underlying human anatomy in the images to control the visualization and the fact that different transfer functions are often only relevant for disjoint anatomical regions. In this paper, we propose a method for using model-based local transfer functions. It allows the reviewing radiologist to apply multiple transfer functions simultaneously to a medical image volume. This provides the radiologist with a tool for making the review process more efficient, by allowing him/her to review more of the information in a medical image volume with a single visualization. The transfer functions are automatically assigned to different anatomically relevant regions, based upon a model registered to the volume to be visualized. The transfer functions can be either pre-defined or interactively changed by the radiologist during the review process. All of this is achieved without adding any unfamiliar aspects to the radiologist’s normal work-flow, when reviewing medical image volumes.

    Place, publisher, year, edition, pages
    Springer Berlin/Heidelberg, 2011
    Series
    Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 6688
    National Category
    Medical Image Processing
    Identifiers
    urn:nbn:se:liu:diva-71679 (URN)10.1007/978-3-642-21227-7_55 (DOI)000308543900055 ()978-3-642-21226-0 (ISBN)
    Conference
    17th Scandinavian Conference on Image Analysis, SCIA 2011, Ystad, Sweden, May 2011.
    Funder
    Swedish Research Council, 2007-4786
    Available from: 2012-06-27 Created: 2011-10-31 Last updated: 2015-10-08Bibliographically approved
    6. Fully automatic measurements of axial vertebral rotation for assessment of spinal deformity in idiopathic scoliosis
    Open this publication in new window or tab >>Fully automatic measurements of axial vertebral rotation for assessment of spinal deformity in idiopathic scoliosis
    Show others...
    2013 (English)In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 58, no 6, 1775-1787 p.Article in journal (Refereed) Published
    Abstract [en]

    Reliable measurements of spinal deformities in idiopathic scoliosis are vital, since they are used for assessing the degree of scoliosis, deciding upon treatment and monitoring the progression of the disease. However, commonly used two dimensional methods (e.g. the Cobb angle) do not fully capture the three dimensional deformity at hand in scoliosis, of which axial vertebral rotation (AVR) is considered to be of great importance. There are manual methods for measuring the AVR, but they are often time-consuming and related with a high intra- and inter-observer variability. In this paper, we present a fully automatic method for estimating the AVR in images from computed tomography. The proposed method is evaluated on four scoliotic patients with 17 vertebrae each and compared with manual measurements performed by three observers using the standard method by Aaro-Dahlborn. The comparison shows that the difference in measured AVR between automatic and manual measurements are on the same level as the inter-observer difference. This is further supported by a high intraclass correlation coefficient (0.971-0.979), obtained when comparing the automatic measurements with the manual measurements of each observer. Hence, the provided results and the computational performance, only requiring approximately 10 to 15 s for processing an entire volume, demonstrate the potential clinical value of the proposed method.

    Place, publisher, year, edition, pages
    Institute of Physics (IOP), 2013
    National Category
    Medical Image Processing Orthopedics
    Identifiers
    urn:nbn:se:liu:diva-89619 (URN)10.1088/0031-9155/58/6/1775 (DOI)000315735400007 ()
    Funder
    Swedish Foundation for Strategic Research , SM10-0022
    Available from: 2013-02-28 Created: 2013-02-28 Last updated: 2014-10-08
    7. Model-based registration for assessment of spinal deformities in idiopathic scoliosis
    Open this publication in new window or tab >>Model-based registration for assessment of spinal deformities in idiopathic scoliosis
    2014 (English)In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 59, no 2, 311-326 p.Article in journal (Refereed) Published
    Abstract [en]

    Detailed analysis of spinal deformity is important within orthopaedic healthcare, in particular for assessment of idiopathic scoliosis. This paper addresses this challenge by proposing an image analysis method, capable of providing a full three-dimensional spine characterization. The proposed method is based on the registration of a highly detailed spine model to image data from computed tomography. The registration process provides an accurate segmentation of each individual vertebra and the ability to derive various measures describing the spinal deformity. The derived measures are estimated from landmarks attached to the spine model and transferred to the patient data according to the registration result. Evaluation of the method provides an average point-to-surface error of 0.9 mm ± 0.9 (comparing segmentations), and an average target registration error of 2.3 mm ± 1.7 (comparing landmarks). Comparing automatic and manual measurements of axial vertebral rotation provides a mean absolute difference of 2.5° ± 1.8, which is on a par with other computerized methods for assessing axial vertebral rotation. A significant advantage of our method, compared to other computerized methods for rotational measurements, is that it does not rely on vertebral symmetry for computing the rotational measures. The proposed method is fully automatic and computationally efficient, only requiring three to four minutes to process an entire image volume covering vertebrae L5 to T1. Given the use of landmarks, the method can be readily adapted to estimate other measures describing a spinal deformity by changing the set of employed landmarks. In addition, the method has the potential to be utilized for accurate segmentations of the vertebrae in routine computed tomography examinations, given the relatively low point-to-surface error.

    Place, publisher, year, edition, pages
    Institute of Physics and Engineering in Medicine, 2014
    National Category
    Medical Image Processing
    Identifiers
    urn:nbn:se:liu:diva-91233 (URN)10.1088/0031-9155/59/2/311 (DOI)000332842000005 ()
    Funder
    Swedish Research Council, 2007-4786Swedish Foundation for Strategic Research , SM10-0022
    Available from: 2013-04-17 Created: 2013-04-17 Last updated: 2014-10-08Bibliographically approved
  • 43.
    Forsberg, Daniel
    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.
    Extending Image Registration Using Polynomial Expansion To Diffeomorphic Deformations2012Conference paper (Other academic)
    Abstract [en]

    The use of polynomial expansion in image registration has previously been shown to be beneficial due to fast convergence and high accuracy. However, earlier work has only briefly out-lined how non-rigid image registration is handled, e.g. not discussing issues like regularization of the displacement field or how to accumulate the displacement field. In this work, it is shown how non-rigid image registration based upon polynomial expansion can be integrated into a generic framework for non-rigid image registration achieving diffeomorphic displacement fields. The proposed non-rigid image registration algorithm using diffeomorphic field accumulation has been evaluated on both synthetically deformed data and real image data and compared to traditional field accumulation. The results clearly demonstrate the power of the diffeomorphic field accumulation.

  • 44.
    Forsberg, Daniel
    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.
    Non-rigid Diffeomorphic Image Registration of Medical Images Using Polynomial Expansion2012In: Image Analysis and Recognition: 9th International Conference, ICIAR 2012, Aveiro, Portugal, June 25-27, 2012. Proceedings, Part II / [ed] Aurélio Campilho, Mohamed Kamel, Springer, 2012, Vol. 7325, 304-312 p.Conference paper (Refereed)
    Abstract [en]

    The use of polynomial expansion in image registration has previously been shown to be beneficial due to fast convergence and high accuracy. However, earlier work has only briefly out-lined how non-rigid image registration is handled, e.g. not discussing issues like regularization of the displacement field or how to accumulate the displacement field. In this work, it is shown how non-rigid image registration based upon polynomial expansion can be integrated into a generic framework for non-rigid image registration achieving diffeomorphic displacement fields. The proposed non-rigid image registration algorithm using diffeomorphic field accumulation is evaluated on both synthetically deformed data and real image data and compared to additive field accumulation. The results clearly demonstrate the power of the diffeomorphic field accumulation.

  • 45.
    Forsberg, Daniel
    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).
    Parallel Scales for More Accurate Displacement Estimation in Phase-Based Image Registration2010In: Pattern Recognition (ICPR), 2010, IEEE Computer Society, 2010, 2329-2332 p.Conference paper (Refereed)
    Abstract [en]

    Phase-based methods are commonly applied in image registration. When working with phase-difference methods only a single is employed, although the algorithms are normally iterated over multiple scales, whereas phase-congruency methods utilize the phase from multiple scales simultaneously. This paper presents an extension to phase-difference methods employing parallel scales to achieve more accurate displacements. Results are also presented clearly favouring the use of parallel scales over single scale in more than 95% of the 120 tested cases. 

  • 46.
    Forsberg, Daniel
    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.
    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.
    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.
    Non-Rigid Volume Registration - A CUDA-based GPU Implementation of the Morphon2011Conference paper (Other academic)
    Abstract [en]

    Image registration is frequently used within the medical image domain and where methods with high performance are required. The need for high accuracy coupled with high speed is especially important for applications such as adaptive radiation therapy and image-guided surgery. During the last years, a number of significant projects have been introduced to make the computational power of GPUs available to a wider audience. The most well known project is the introduction of CUDA (Compute Unified Device Architecture). In this paper, we present a CUDA based GPU implementation of a non-rigid image registration algorithm, known as the Morphon, and compare it with a CPU implementation of the Morphon. The achieved speedup, in the range of 51-54x, is also compared with speedups reported from other non-rigid registration methods mplemented on the GPU. These include the Demons algorithm and a mutual information based algorithm.

  • 47.
    Forsberg, Daniel
    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.
    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.
    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.
    Phase-Based Non-Rigid 3D Image Registration - From Minutes to Seconds Using CUDA2011Conference paper (Other academic)
    Abstract [en]

    Image registration is a well-known concept within the medical image domain and has been shown to be useful in a number of dierent tasks. However, due to sometimes long processing times, image registration is not fully utilized in clinical workows, where time is an important factor. During the last couple of years, a number of signicant projects have been introduced to make the computational power of GPUs available to a wider audience, where the most well known is CUDA. In this paper we present, with the aid of CUDA, a speedup in the range of 38-44x (from 29 minutes to 40 seconds) when implementing a phasebased non-rigid image registration algorithm, known as the Morphon, on a single GPU. The achieved speedup is in the same magnitude as the speedups reported from other non-rigid registration algorithms fully ported to the GPU. Given the impressive speedups, both reported in this paper and other papers, we therefore consider that it is now feasible to eectively integrate image registration into various clinical workows, where time is a critical factor.

  • 48.
    Forsberg, Daniel
    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.
    Farnebäck, Gunnar
    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, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Westin, Carl-Fredrik
    Harvard Medical School.
    Multi-modal Image Registration Using Polynomial Expansion and Mutual Information2012In: Biomedical Image Registration: Proceedings of the 5th International Workshop, WBIR 2012, Nashville, TN, USA, July 7-8, 2012 / [ed] Benoit M. Dawant, Gary E. Christensen, J.Michael Fitzpatrick and Daniel Rueckert, Springer Berlin/Heidelberg, 2012, 40-49 p.Chapter in book (Refereed)
    Abstract [en]

    This book constitutes the refereed proceedings of the 5th International Workshop on Biomedical Image Registration, WBIR 2012, held in Nashville, Tennessee, USA, in July 2012. The 20 full papers and 11 poster papers included in this volume were carefully reviewed and selected from 44 submitted papers. They full papers are organized in the following topical sections: multiple image sets; brain; non-rigid anatomy; and frameworks and similarity measures.

  • 49.
    Forsberg, Daniel
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Sectra AB, Linköping, Sweden.
    Lindblom, Maria
    Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Quick, Petter
    Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Gauffin, Håkan
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Orthopaedics in Linköping.
    Quantitative analysis of the patellofemoral motion pattern using semi-automatic processing of 4D CT data2016In: International Journal of Computer Assisted Radiology and Surgery, ISSN 1861-6410, E-ISSN 1861-6429, Vol. 11, no 9, 1731-1741 p.Article in journal (Refereed)
    Abstract [en]

    To present a semi-automatic method with minimal user interaction for quantitative analysis of the patellofemoral motion pattern. 4D CT data capturing the patellofemoral motion pattern of a continuous flexion and extension were collected for five patients prone to patellar luxation both pre- and post-surgically. For the proposed method, an observer would place landmarks in a single 3D volume, which then are automatically propagated to the other volumes in a time sequence. From the landmarks in each volume, the measures patellar displacement, patellar tilt and angle between femur and tibia were computed. Evaluation of the observer variability showed the proposed semi-automatic method to be favorable over a fully manual counterpart, with an observer variability of approximately 1.5 for the angle between femur and tibia, 1.5 mm for the patellar displacement, and 4.0-5.0 for the patellar tilt. The proposed method showed that surgery reduced the patellar displacement and tilt at maximum extension with approximately 10-15 mm and 15-20 for three patients but with less evident differences for two of the patients. A semi-automatic method suitable for quantification of the patellofemoral motion pattern as captured by 4D CT data has been presented. Its observer variability is on par with that of other methods but with the distinct advantage to support continuous motions during the image acquisition.

  • 50.
    Forsberg, Daniel
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Sectra, Linköping, Sweden .
    Lundström, Claes
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Sectra, Linköping, Sweden .
    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).
    Eigenspine: Eigenvector Analysis of Spinal Deformities in Idiopathic Scoliosis2014In: Computational Methods and Clinical Applications for Spine Imaging: Proceedings of the Workshop held at the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, September 22-26, 2013, Nagoya, Japan / [ed] Jianhua Yao,Tobias Klinder, Shuo Li, Springer, 2014, Vol. 17, 123-134 p.Conference paper (Refereed)
    Abstract [en]

    In this paper, we propose the concept of eigenspine, a data analysis schemeuseful for quantifying the linear correlation between different measures relevant fordescribing spinal deformities associated with spinal diseases, such as idiopathic scoliosis.The proposed concept builds upon the use of principal component analysis(PCA) and canonical correlation analysis (CCA), where PCA is used to reduce thenumber of dimensions in the measurement space, thereby providing a regularizationof the measurements, and where CCA is used to determine the linear dependence betweenpair-wise combinations of the different measures. To demonstrate the usefulnessof the eigenspine concept, the measures describing position and rotation of thelumbar and the thoracic vertebrae of 22 patients suffering from idiopathic scoliosiswere analyzed. The analysis showed that the strongest linear relationship is foundbetween the anterior-posterior displacement and the sagittal rotation of the vertebrae,and that a somewhat weaker but still strong correlation is found between thelateral displacement and the frontal rotation of the vertebrae. These results are wellin-line with the general understanding of idiopathic scoliosis. Noteworthy though isthat the obtained results from the analysis further proposes axial vertebral rotationas a differentiating measure when characterizing idiopathic scoliosis. Apart fromanalyzing pair-wise linear correlations between different measures, the method isbelieved to be suitable for finding a maximally descriptive low-dimensional combinationof measures describing spinal deformities in idiopathic scoliosis.

12345 1 - 50 of 230
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