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  • 1.
    Bolger, Ann F
    et al.
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Heiberg, Einar
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Dyverfeldt, Petter
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Carlsson, Mats
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Johansson, P
    Markenroth, K
    Sigfridsson, Andreas
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Engvall, Jan
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Ebbers, Tino
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Arheden, H
    Tredimensionellt MR-blodflöde och diastolisk kinetisk energi kvantiferat med magnetisk resonanstomografi efter kirurgisk vänsterkammarrekonstruktion. Ny teknik för utvärdering av kammarfunktion.2007In: Riksstämman,2007, 2007Conference paper (Other academic)
  • 2.
    Bolger, Ann F
    et al.
    Linköping University, Department of Medicine and Care, Center for Medical Image Science and Visualization. Linköping University, Faculty of Health Sciences.
    Heiberg, Einar
    Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Department of Medicine and Care, Center for Medical Image Science and Visualization. Linköping University, Faculty of Health Sciences.
    Karlsson, Matts
    Linköping University, Department of Biomedical Engineering. Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Faculty of Health Sciences.
    Wigström, Lars
    Linköping University, Department of Medicine and Care, Center for Medical Image Science and Visualization. Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Faculty of Health Sciences.
    Engvall, Jan
    Linköping University, Department of Medicine and Care, Center for Medical Image Science and Visualization. Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Faculty of Health Sciences.
    Sigfridsson, Andreas
    Linköping University, Department of Medicine and Care, Center for Medical Image Science and Visualization. Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Faculty of Health Sciences.
    Ebbers, Tino
    Linköping University, Department of Medicine and Care, Center for Medical Image Science and Visualization. Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Faculty of Health Sciences.
    Escobar Kvitting, John-Peder
    Linköping University, Department of Medicine and Care, Center for Medical Image Science and Visualization. Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Faculty of Health Sciences.
    Carlhäll, Carljohan
    Linköping University, Department of Medicine and Care, Center for Medical Image Science and Visualization. Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Faculty of Health Sciences.
    Wranne, Bengt
    Linköping University, Department of Medicine and Care, Center for Medical Image Science and Visualization. Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Faculty of Health Sciences.
    Transit of blood flow through thehuman left ventricle mapped by cardiovascular magnetic resonance2007In: Journal of Cardiovascular Magnetic Resonance, ISSN 1097-6647, E-ISSN 1532-429X, Vol. 9, no 5, p. 741-747Article in journal (Refereed)
    Abstract [en]

    BACKGROUND:

    The transit of blood through the beating heart is a basic aspect of cardiovascular physiology which remains incompletely studied. Quantification of the components of multidirectional flow in the normal left ventricle (LV) is lacking, making it difficult to put the changes observed with LV dysfunction and cardiac surgery into context.

    METHODS:

    Three dimensional, three directional, time resolved magnetic resonance phase-contrast velocity mapping was performed at 1.5 Tesla in 17 normal subjects, 6 female, aged 44+/-14 years (mean+/-SD). We visualized and measured the relative volumes of LV flow components and the diastolic changes in inflowing kinetic energy (KE). Of total diastolic inflow volume, 44+/-11% followed a direct, albeit curved route to systolic ejection (videos 1 and 2), in contrast to 11% in a subject with mildly dilated cardiomyopathy (DCM), who was included for preliminary comparison (video 3). In normals, 16+/-8% of the KE of inflow was conserved to the end of diastole, compared with 5% in the DCM patient. Blood following the direct route lost or transferred less of its KE during diastole than blood that was retained until the next beat (1.6+/-1.0 millijoules vs 8.2+/-1.9 millijoules, p<0.05); whereas, in the DCM patient, the reduction in KE of retained inflow was 18-fold greater than that of the blood tracing the direct route.

    CONCLUSION:

    Multidimensional flow mapping can measure the paths, compartmentalization and kinetic energy changes of blood flowing into the LV, demonstrating differences of KE loss between compartments, and potentially between the flows in normal and dilated left ventricles.

  • 3.
    Brandt, Einar
    et al.
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Clinical Physiology. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Ebbers, Tino
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Wigström, Lars
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Clinical Physiology. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Karlsson, Matts
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Automatisk flödeskaraktärisering av tredimensionella vektorfält.2001In: In proceedings of Svenska Mekanikdagarna,2001, 2001, p. 61-62Conference paper (Refereed)
  • 4.
    Brandt, Einar
    et al.
    Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Faculty of Health Sciences.
    Wigström, Lars
    Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Faculty of Health Sciences.
    Wranne, Bengt
    Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Faculty of Health Sciences.
    Segmentation of echo cardiographic image sequences using spatio-temporal information1999In: Medical Image Computing and Computer-Assisted Intervention – MICCAI’99: Second International Conference, Cambridge, UK, September 19-22, 1999. Proceedings / [ed] Chris Taylor, Alan Colchester, Berlin: Springer, 1999, Vol. 1679, p. 410-419Chapter in book (Refereed)
    Abstract [en]

    This paper describes a new method for improving border detection in image sequences by including both spatial and temporal information. The method is based on three dimensional quadrature filters for estimating local orientation. A simplification that gives a significant reduction in computational demand is also presented. The border detection framework is combined with a segmentation algorithm based on active contours or ’snakes’, implemented using a new optimization relaxation that can be solved to optimality using dynamical programming. The aim of the study was to compare segmentation performance using gradient based border detection and the proposed border detection algorithm using spatio-temporal information. Evaluation is performed both on a phantom and in-vivo data from five echocardiographic short axis image sequences. It could be concluded that when temporal information was included weak and incomplete boundaries could be found where gradient based border detection failed. Otherwise there was no significant difference in performance between the new proposed method and gradient based border detection.

  • 5.
    Brandt Heiberg, Einar
    Linköping University, Department of Medicine and Care. Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Health Sciences.
    Efficient representations in matlab made easy - a tensor array toolbox2001In: Proceedings of Nordic Matlab Conference, 2001, 2001, p. 213-216Conference paper (Refereed)
    Abstract [en]

    Tensors can be used to create efficient and intuitive representations for a wide variety of applications, including signal and image processing, mechanics and fluid dynamics. In order to achieve this in Matlab, a toolbox was developed designed to enhance Matlab's ability to store and manipulate arrays, such that each element in the array can be vectors or general tensors. This paper describes the implementation of the tool box and gives several examples on the usage of tensor representations for signal and image processing. Furthermore, the representation and processing of uncertain data using tensor representations is described as well.

  • 6. Carlhall, C.
    et al.
    Wigström, Lars
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Heiberg, Einar
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Karlsson, Matts
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Biomedical Modelling and Simulation .
    Bolger, A.F.
    Department of Medicine, Division of Cardiology, University of California, San Francisco, CA, United States.
    Nylander, Eva
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Reply [2]2006In: American Journal of Physiology. Heart and Circulatory Physiology, ISSN 0363-6135, E-ISSN 1522-1539, Vol. 291, no 5Other (Other academic)
    Abstract [en]

    [No abstract available]

  • 7.
    Carlhäll, Carljohan
    et al.
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Clinical Physiology. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Wigström, Lars
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Clinical Physiology. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Heiberg, Einar
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Clinical Physiology. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Karlsson, M.
    Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Biomedical Engineering in Östergötland. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Bolger, A. F.
    Department of Medicine, Division of Cardiology, University of California, San Francisco, California, USA.
    Nylander, E.
    Linköping University, Department of Medicine and Care. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Reply to article: Misinterpretation About the Contribution of the Left Ventricular Long-Axis Shortening to the Stroke Volume2006In: American Journal of Physiology. Heart and Circulatory Physiology, ISSN 0363-6135, E-ISSN 1522-1539, Vol. 291, no 5, p. 2551-2552Article in journal (Other academic)
    Abstract [en]

       

  • 8.
    Carlhäll, Carljohan
    et al.
    Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Department of Medicine and Care, Center for Medical Image Science and Visualization. Linköping University, Faculty of Health Sciences.
    Wigström, Lars
    Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Department of Medicine and Care, Center for Medical Image Science and Visualization. Linköping University, Faculty of Health Sciences.
    Heiberg, Einar
    Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Department of Medicine and Care, Center for Medical Image Science and Visualization. Linköping University, Faculty of Health Sciences.
    Karlsson, Matts
    Linköping University, Department of Biomedical Engineering. Linköping University, Department of Medicine and Care, Center for Medical Image Science and Visualization. Linköping University, Faculty of Health Sciences.
    Bolger, A. F.
    Department of Medicine/Cardiology, University of California, San Francisco, California.
    Nylander, Eva
    Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Department of Medicine and Care, Center for Medical Image Science and Visualization. Linköping University, Faculty of Health Sciences.
    Contribution of mitral annular excursion and shape dynamics to total left ventricular volume change2004In: American Journal of Physiology. Heart and Circulatory Physiology, ISSN 0363-6135, E-ISSN 1522-1539, Vol. 287, no 4, p. H1836-H1841Article in journal (Refereed)
    Abstract [en]

    The mitral annulus (MA) has a complex shape and motion, and its excursion has been correlated to left ventricular (LV) function. During the cardiac cycle the annulus’ excursion encompasses a volume that is part of the total LV volume change during both filling and emptying. Our objective was to evaluate the contribution of MA excursion and shape variation to total LV volume change. Nine healthy subjects aged 56 ± 11 (means ± SD) years underwent transesophageal echocardiography (TEE). The MA was outlined in all time frames, and a four-dimensional (4-D) Fourier series was fitted to the MA coordinates (3-D+time) and divided into segments. The annular excursion volume (AEV) was calculated based on the temporally integrated product of the segments’ area and their incremental excursion. The 3-D LV volumes were calculated by tracing the endocardial border in six coaxial planes. The AEV (10 ± 2 ml) represented 19 ± 3% of the total LV stroke volume (52 ± 12 ml). The AEV correlated strongly with LV stroke volume (r = 0.73; P < 0.05). Peak MA area occurred during middiastole, and 91 ± 7% of reduction in area from peak to minimum occurred before the onset of LV systole. The excursion of the MA accounts for an important portion of the total LV filling and emptying in humans. These data suggest an atriogenic influence on MA physiology and also a sphincter-like action of the MA that may facilitate ventricular filling and aid competent valve closure. This 4-D TEE method is the first to allow noninvasive measurement of AEV and may be used to investigate the impact of physiological and pathological conditions on this important aspect of LV performance.

  • 9.
    Escobar Kvitting, John-Peder
    et al.
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Clinical Physiology. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Brandt, Einar
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Clinical Physiology. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Wigström, Lars
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Clinical Physiology. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Engvall, Jan
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Clinical Physiology. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Visualization of flow in the aorta using time-resolved 3D phase contrast MRI2001In: Proc. Intl. Soc. Mag. Reson. Med.,2001, 2001, p. 378-378Conference paper (Refereed)
  • 10.
    Heiberg, Einar
    Linköping University, Department of Biomedical Engineering. Linköping University, Department of Medicine and Care, Center for Medical Image Science and Visualization. Linköping University, The Institute of Technology.
    Automated feature detection in multidimensional images2005Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Manual identification of structures and features in multidimensional images is at best time consuming and operator dependent. Feature identification need to be accurate, repeatable and quantitative.

    This thesis presents novel methods for automated feature detection in multidimensional images that are independent on imaging modality. Feature detection is described at two abstraction levels. At the first low level the image is regionally processed to find local or regional features. In the second medium level results are taken from the low level feature detection and grouped into objects or parts that can be quantified. A key to quantification of cardiac function is delineation of the cardiac walls which is a difficult task. Two different methods are described and evaluated for delineation of the left ventricular wall from anatomical images. The results show that semi-automatic delineation is a huge time saver compared to manual delineation. To obtain a robust results as much a priori and image information as possible should be used in the delineation process. Regional cardiac wall function is further studied by deriving and analyzing strain-rate tensors from velocity encoded images. For flow encoded images novel methods to find regional flow structures such as vortex cores, flow based delineation, and flow quantification are proposed. These methods are applied to study blood flow in the human heart, but the techniques outlined are general and can be applied to a wide array of flow conditions.

    List of papers
    1. Segmentation of echo cardiographic image sequences using spatio-temporal information
    Open this publication in new window or tab >>Segmentation of echo cardiographic image sequences using spatio-temporal information
    1999 (English)In: Medical Image Computing and Computer-Assisted Intervention – MICCAI’99: Second International Conference, Cambridge, UK, September 19-22, 1999. Proceedings / [ed] Chris Taylor, Alan Colchester, Berlin: Springer, 1999, Vol. 1679, p. 410-419Chapter in book (Refereed)
    Abstract [en]

    This paper describes a new method for improving border detection in image sequences by including both spatial and temporal information. The method is based on three dimensional quadrature filters for estimating local orientation. A simplification that gives a significant reduction in computational demand is also presented. The border detection framework is combined with a segmentation algorithm based on active contours or ’snakes’, implemented using a new optimization relaxation that can be solved to optimality using dynamical programming. The aim of the study was to compare segmentation performance using gradient based border detection and the proposed border detection algorithm using spatio-temporal information. Evaluation is performed both on a phantom and in-vivo data from five echocardiographic short axis image sequences. It could be concluded that when temporal information was included weak and incomplete boundaries could be found where gradient based border detection failed. Otherwise there was no significant difference in performance between the new proposed method and gradient based border detection.

    Place, publisher, year, edition, pages
    Berlin: Springer, 1999
    Series
    Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 1679
    Keywords
    medical image computing, computer-assisted intervention, data-driven image segmentation, structural models, image processing, feature detection, surfaces, shape, measurement, image interpretation, spatiotemporal analysis, diffusion tensor analysis, image registration, data fusion, data visualisation, image-guided intervention, robotic systems, biomechanics, simulation
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-49138 (URN)10.1007/10704282_45 (DOI)3-540-66503-X (ISBN)
    Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2018-01-30
    2. Three-dimensional flow characterization using vector pattern matching
    Open this publication in new window or tab >>Three-dimensional flow characterization using vector pattern matching
    2003 (English)In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 9, no 3, p. 313-319Article in journal (Refereed) Published
    Abstract [en]

    This paper describes a novel method for regional characterization of three-dimensional vector fields using a pattern matching approach. Given a three-dimensional vector field, the goal is to automatically locate, identify, and visualize a selected set of classes of structures or features. Rather than analytically defining the properties that must be fulfilled in a region in order to be classified as a specific structure, a set of idealized patterns for each structure type is constructed. Similarity to these patterns is then defined and calculated. Examples of structures of interest include vortices, swirling flow, diverging or converging flow, and parallel flow. Both medical and aerodynamic applications are presented in this paper.

    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-26709 (URN)10.1109/TVCG.2003.1207439 (DOI)11302 (Local ID)11302 (Archive number)11302 (OAI)
    Available from: 2009-10-08 Created: 2009-10-08 Last updated: 2017-12-13
    3. Kinematics of the heart: strain-rate imaging from time-resolved three-dimensional phase contrast MRI
    Open this publication in new window or tab >>Kinematics of the heart: strain-rate imaging from time-resolved three-dimensional phase contrast MRI
    Show others...
    2002 (English)In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 21, no 9, p. 1105-1109Article in journal (Refereed) Published
    Abstract [en]

    A four-dimensional mapping (three spatial dimensions + time) of myocardial strain-rate would help to describe the mechanical properties of the myocardium, which affect important physiological factors such as the pumping performance of the ventricles. Strain-rate represents the local instantaneous deformation of the myocardium and can be calculated from the spatial gradients of the velocity field. Strain-rate has previously been calculated using one-dimensional (ultrasound) or two-dimensional (2-D) magnetic resonance imaging techniques. However, this assumes that myocardial motion only occurs in one direction or in one plane, respectively. This paper presents a method for calculation of the time-resolved three-dimensional (3-D) strain-rate tensor using velocity vector information in a 3-D spatial grid during the whole cardiac cycle. The strain-rate tensor provides full information of both magnitude and direction of the instantaneous deformation of the myocardium. A method for visualization of the full 3-D tensor is also suggested. The tensors are visualized using ellipsoids, which display the principal directions of strain-rate and the ratio between strain-rate magnitude in each direction. The presented method reveals the principal strain-rate directions without a priori knowledge of myocardial motion directions.

    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-26711 (URN)10.1109/TMI.2002.804431 (DOI)11305 (Local ID)11305 (Archive number)11305 (OAI)
    Available from: 2009-10-08 Created: 2009-10-08 Last updated: 2018-07-03
    4. Time resolved three-dimensional segmentation of the left ventricle in multimodality cardiac imaging
    Open this publication in new window or tab >>Time resolved three-dimensional segmentation of the left ventricle in multimodality cardiac imaging
    Show others...
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    We propose a robust approach for multimodality segmentation of the cardiac left ventricle. The method is based on the concept of deformable models, but extended with an enhanced and fast edge detection scheme that includes temporal information, and anatomical a priori information. The algorithm is implemented with a fast numeric scheme for solving energy minimization, and efficient filter nets for fast edge detection. This allows clinically applicable time for a whole time resolved 3D cardiac data set to be acheived on a standard desktop computer. The algorithm is validated on images acquired using MRI Gradient echo, MRl (SSFP) images, and Cardiac CT, and tested for feasibility with three other imaging modalities, including gated blood pool SPECT, echocardiography and late enhancement MRL.

    Keywords
    Image Segmentation, Deformable models, Left Ventricle, 3D, time resolved, edge detection
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-86175 (URN)
    Available from: 2012-12-10 Created: 2012-12-10 Last updated: 2016-03-14
    5. Flow quantification from time resolved three dimensional phase contrast MRI
    Open this publication in new window or tab >>Flow quantification from time resolved three dimensional phase contrast MRI
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    This paper presents novel techniques for deriving meaningful quantitative parameters from three dimensional velocity measurements. By shifting perspective from the Eulerian grid of measured velocities to a Lagrange perspective that follows flow using particle traces, we are able to divide flow into different groups based on their behavior. Parameters from each group can then be extracted and quantified. The applicability of the method is demonstrated by extracting parameters of diastolic flow in the human left ventricle.

    Keywords
    Flow quantification, velocity measurements, particle trace, left ventricle
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-86176 (URN)
    Available from: 2012-12-10 Created: 2012-12-10 Last updated: 2016-03-14
  • 11.
    Heiberg, Einar
    Linköping University, Department of Biomedical Engineering, Biomedical Modelling and Simulation. Linköping University, The Institute of Technology.
    Automated feature detection in multidimensional images: a unified tensor approach2001Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Manual identification of structures and features in multidimensional images is at best time consuming and operator dependent. Feature identification need to be accurate, repeatable and quantitative. This thesis presents a unified approach for automatic feature detection in multidimensional scalar and vector fields. The basis for the feature detection is a tensor representation of multidimensional local neighborhoods, constructed from a filter response controlled linear combination of basis tensors. Using eigenvalue and eigenvector decomposition, local topology and local orientation can be estimated. With different filter sets the tensor representation can be used to find specific features in multidimensional images, such as planar structures in scalar fields or flow structures as vortices or parallel flow in vector fields.

    The motivation for the unified approach for feature detection in both scalar and vector fields is to build a foundation to tackle the challenge of understanding the complex interaction between the cardiac walls and the blood flow in the human heart.

    List of papers
    1. Segmentation of echo cardiographic image sequences using spatio-temporal information
    Open this publication in new window or tab >>Segmentation of echo cardiographic image sequences using spatio-temporal information
    1999 (English)In: Medical Image Computing and Computer-Assisted Intervention – MICCAI’99: Second International Conference, Cambridge, UK, September 19-22, 1999. Proceedings / [ed] Chris Taylor, Alan Colchester, Berlin: Springer, 1999, Vol. 1679, p. 410-419Chapter in book (Refereed)
    Abstract [en]

    This paper describes a new method for improving border detection in image sequences by including both spatial and temporal information. The method is based on three dimensional quadrature filters for estimating local orientation. A simplification that gives a significant reduction in computational demand is also presented. The border detection framework is combined with a segmentation algorithm based on active contours or ’snakes’, implemented using a new optimization relaxation that can be solved to optimality using dynamical programming. The aim of the study was to compare segmentation performance using gradient based border detection and the proposed border detection algorithm using spatio-temporal information. Evaluation is performed both on a phantom and in-vivo data from five echocardiographic short axis image sequences. It could be concluded that when temporal information was included weak and incomplete boundaries could be found where gradient based border detection failed. Otherwise there was no significant difference in performance between the new proposed method and gradient based border detection.

    Place, publisher, year, edition, pages
    Berlin: Springer, 1999
    Series
    Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 1679
    Keywords
    medical image computing, computer-assisted intervention, data-driven image segmentation, structural models, image processing, feature detection, surfaces, shape, measurement, image interpretation, spatiotemporal analysis, diffusion tensor analysis, image registration, data fusion, data visualisation, image-guided intervention, robotic systems, biomechanics, simulation
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-49138 (URN)10.1007/10704282_45 (DOI)3-540-66503-X (ISBN)
    Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2018-01-30
    2. Three-dimensional flow characterization using vector pattern matching
    Open this publication in new window or tab >>Three-dimensional flow characterization using vector pattern matching
    2003 (English)In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 9, no 3, p. 313-319Article in journal (Refereed) Published
    Abstract [en]

    This paper describes a novel method for regional characterization of three-dimensional vector fields using a pattern matching approach. Given a three-dimensional vector field, the goal is to automatically locate, identify, and visualize a selected set of classes of structures or features. Rather than analytically defining the properties that must be fulfilled in a region in order to be classified as a specific structure, a set of idealized patterns for each structure type is constructed. Similarity to these patterns is then defined and calculated. Examples of structures of interest include vortices, swirling flow, diverging or converging flow, and parallel flow. Both medical and aerodynamic applications are presented in this paper.

    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-26709 (URN)10.1109/TVCG.2003.1207439 (DOI)11302 (Local ID)11302 (Archive number)11302 (OAI)
    Available from: 2009-10-08 Created: 2009-10-08 Last updated: 2017-12-13
    3. Efficient representations in matlab made easy - a tensor array toolbox
    Open this publication in new window or tab >>Efficient representations in matlab made easy - a tensor array toolbox
    2001 (English)In: Proceedings of Nordic Matlab Conference, 2001, 2001, p. 213-216Conference paper, Published paper (Refereed)
    Abstract [en]

    Tensors can be used to create efficient and intuitive representations for a wide variety of applications, including signal and image processing, mechanics and fluid dynamics. In order to achieve this in Matlab, a toolbox was developed designed to enhance Matlab's ability to store and manipulate arrays, such that each element in the array can be vectors or general tensors. This paper describes the implementation of the tool box and gives several examples on the usage of tensor representations for signal and image processing. Furthermore, the representation and processing of uncertain data using tensor representations is described as well.

    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-27346 (URN)11998 (Local ID)11998 (Archive number)11998 (OAI)
    Conference
    Nordic Matlab Conference. 17-18 Oct, Oslo, Norway, 2001
    Available from: 2009-10-08 Created: 2009-10-08 Last updated: 2013-11-07
  • 12.
    Heiberg, Einar
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Clinical Physiology. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Hur kan höger kammares globala och regionala funktion värderas med magnetresonanstomografi (den bortglömda kammaren Hur och varför skall högerkammarfunktionen bedömas?)2007In: IX Svenska Kardiovaskulära vårmötet,2007, 2007Conference paper (Other academic)
  • 13.
    Heiberg, Einar
    et al.
    Linköping University, Department of Medicine and Care, Center for Medical Image Science and Visualization. Linköping University, Faculty of Health Sciences.
    Bolger, Ann F.
    Department of Medicine, University of California, San Francisco, USA.
    Karlsson, Matts
    Linköping University, Department of Medicine and Care. Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Health Sciences.
    Flow quantification from time resolved three dimensional phase contrast MRIManuscript (preprint) (Other academic)
    Abstract [en]

    This paper presents novel techniques for deriving meaningful quantitative parameters from three dimensional velocity measurements. By shifting perspective from the Eulerian grid of measured velocities to a Lagrange perspective that follows flow using particle traces, we are able to divide flow into different groups based on their behavior. Parameters from each group can then be extracted and quantified. The applicability of the method is demonstrated by extracting parameters of diastolic flow in the human left ventricle.

  • 14.
    Heiberg, Einar
    et al.
    Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Faculty of Health Sciences.
    Ebbers, Tino
    Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Faculty of Health Sciences.
    Wigström, Lars
    Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Faculty of Health Sciences.
    Karlsson, Matts
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Three-dimensional flow characterization using vector pattern matching2003In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 9, no 3, p. 313-319Article in journal (Refereed)
    Abstract [en]

    This paper describes a novel method for regional characterization of three-dimensional vector fields using a pattern matching approach. Given a three-dimensional vector field, the goal is to automatically locate, identify, and visualize a selected set of classes of structures or features. Rather than analytically defining the properties that must be fulfilled in a region in order to be classified as a specific structure, a set of idealized patterns for each structure type is constructed. Similarity to these patterns is then defined and calculated. Examples of structures of interest include vortices, swirling flow, diverging or converging flow, and parallel flow. Both medical and aerodynamic applications are presented in this paper.

  • 15.
    Heiberg, Einar
    et al.
    Linköping University, Department of Medicine and Care, Center for Medical Image Science and Visualization. Linköping University, Faculty of Health Sciences.
    Wigström, Lars
    Linköping University, Department of Medicine and Care, Center for Medical Image Science and Visualization. Linköping University, Faculty of Health Sciences.
    Carlsson, Marcus
    Department of Clinical Physiology, Lund University, Sweden.
    Bolger, Ann F.
    Department of Medicine/Cardiology, University of California, San Francisco, USA.
    Karlsson, Matts
    Linköping University, Department of Biomedical Engineering. Linköping University, Department of Medicine and Care, Center for Medical Image Science and Visualization. Linköping University, Faculty of Health Sciences.
    Time resolved three-dimensional segmentation of the left ventricle in multimodality cardiac imagingManuscript (preprint) (Other academic)
    Abstract [en]

    We propose a robust approach for multimodality segmentation of the cardiac left ventricle. The method is based on the concept of deformable models, but extended with an enhanced and fast edge detection scheme that includes temporal information, and anatomical a priori information. The algorithm is implemented with a fast numeric scheme for solving energy minimization, and efficient filter nets for fast edge detection. This allows clinically applicable time for a whole time resolved 3D cardiac data set to be acheived on a standard desktop computer. The algorithm is validated on images acquired using MRI Gradient echo, MRl (SSFP) images, and Cardiac CT, and tested for feasibility with three other imaging modalities, including gated blood pool SPECT, echocardiography and late enhancement MRL.

  • 16.
    Janerot-Sjöberg, Birgitta
    et al.
    Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    von Schmalensee, Niklas
    Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Schreckenberger, Anja
    Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Richter, Arina
    Östergötlands Läns Landsting, Heart Centre, Department of Cardiology.
    Brandt, Einar
    Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Kirkhorn, Johan
    Norwegian University of Science and Technology, Trondheim, Norway.
    Wilkenshoff, Ursula
    Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Influence of respiration on myocardial signal intensity2001In: Ultrasound in Medicine and Biology, ISSN 0301-5629, E-ISSN 1879-291X, Vol. 27, no 4, p. 473-479Article in journal (Refereed)
    Abstract [en]

    Echocardiographic quantification of myocardial perfusion after IV contrast is possible, based on the intensity of the received intermittent second harmonic signal. To investigate the influence of respiration on the intensity of myocardial signals, we examined nine patients with normal coronary angiograms. At baseline, end-expiratory and end-inspiratory images were obtained in broadband radiofrequency (RF) and intermittent second harmonic modes, the latter repeated during IV contrast at rest and at peak stress. In mid-septum at baseline, end-inspiratory integrated backscatter intensity was 4 dB higher (p < 0.05, both in second harmonic and fundamental domains) than end-expiratory intensity. In second harmonic imaging, contrast increased signal intensity by 4 dB (p < 0.05) in six examined segments, but the increase in the midseptal region (2 dB) was not significant. Contrast-enhanced intensity at end-inspiration was higher (3 dB, p < 0.01) than baseline intensity at end-expiration. We conclude that the increase in myocardial signal intensity during inspiration may resemble the contrast effect in intermittent second harmonic mode. The respiratory variation persists after contrast and may mask or exaggerate the effect of myocardial contrast.

  • 17.
    Pislaru, Cristina
    et al.
    Departments of Cardiology University of Leuven, Leuven, Belgium.
    D'hooge, Jan
    Department of Electrical Engineering, Medical Image Computing, University of Leuven, Leuven, Belgium.
    Pislaru, Sorin V
    Departments of Cardiology University of Leuven, Leuven, Belgium.
    Brandt, Einar
    Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Cipic, Roberg
    Department of Cardiology, Klinikum Innenstadt, University of Munich, Munich, Germany.
    Angermann, Christiane E
    Department of Cardiology, Klinikum Innenstadt, University of Munich, Munich, Germany.
    Van de Werf, Frans
    Departments of Cardiology University of Leuven, Leuven, Belgium.
    Bijnens, Bart
    Departments of Cardiology University of Leuven, Leuven, Belgium.
    Herregods, Marie-Christine
    Departments of Cardiology University of Leuven, Leuven, Belgium.
    Sutherland, George R
    Departments of Cardiology University of Leuven, Leuven, Belgium.
    Is there a change in myocardial nonlinearity during the cardiac cycle?2001In: Ultrasound in Medicine and Biology, ISSN 0301-5629, E-ISSN 1879-291X, Vol. 27, no 3, p. 389-398Article in journal (Refereed)
    Abstract [en]

    The distortion of a sound wave during propagation results in progressive transfer of the energy from fundamental to higher harmonics, and is dependent on the nonlinearity of the medium. We studied if relative changes in acoustical nonlinearity occur in healthy myocardium during the cardiac cycle. Radiofrequency data were acquired from transthoracic echocardiography (2.5 and 3.5 MHz), parasternal long axis view, from five dogs and nine healthy volunteers. Integrated backscatter was calculated after filtering for fundamental (FIB) and second harmonic frequencies (SHIB), from a region in the posterior myocardial wall. The results suggest that there is little difference between the SHIB and FIB, although there were large variations between individuals. The maximal changes in nonlinearity, as estimated by SHIB/FIB ratio, mostly occurred during systole. SHIB presented similar cyclic variation with FIB (p = NS). Further studies are necessary to separate the role of myocardial nonlinearity, attenuation, propagating distance, or acoustical properties of the blood. The results are important in further tissue characterization studies employing second harmonic data.

  • 18.
    Renner, Johan
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Mechanical Engineering, Applied Thermodynamics and Fluid Mechanics.
    Gårdhagen, Roland
    Linköping University, The Institute of Technology. Linköping University, Department of Mechanical Engineering, Applied Thermodynamics and Fluid Mechanics.
    Heiberg, Einar
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Clinical Physiology.
    Ebbers, Tino
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care.
    Länne, Toste
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Physiology. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Karlsson, Matts
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Biomedical Modelling and Simulation .
    Validation of Simulated Velocity of Blood in Patient Specific Aorta2006In: VIII Svenska Kardiovaskulära Vårmöte,2006, Linköping, Sweden: Linköpings universitet , 2006Conference paper (Refereed)
  • 19.
    Selskog, Pernilla
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Brandt, Einar
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Clinical Physiology. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Wigström, Lars
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Clinical Physiology. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Karlsson, Matts
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Quantification and Visualization of myocardial strain-rate tensors from time-resolved 3D cine phase contrast MRI.2001In: Proc. Intl. Soc. Mag. Reson. Med.,2001, 2001, p. 1870-1870Conference paper (Refereed)
  • 20.
    Selskog, Pernilla
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Biomedical Modelling and Simulation .
    Heiberg, Einar
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Clinical Physiology.
    Quantification of myocardial strain-rate from 3D Cine phase contrast2000In: ESMRMB,2000, 2000Conference paper (Other academic)
  • 21.
    Selskog, Pernilla
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Heiberg, Einar
    Linköping University, Department of Biomedical Engineering. Linköping University, Department of Medicine and Care.
    Ebbers, Tino
    Linköping University, Department of Medicine and Care. Linköping University, Faculty of Health Sciences.
    Wigström, Lars
    Linköping University, Department of Biomedical Engineering. Linköping University, Department of Medicine and Care. Linköping University, Faculty of Health Sciences.
    Karlsson, Matts
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Kinematics of the heart: strain-rate imaging from time-resolved three-dimensional phase contrast MRI2002In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 21, no 9, p. 1105-1109Article in journal (Refereed)
    Abstract [en]

    A four-dimensional mapping (three spatial dimensions + time) of myocardial strain-rate would help to describe the mechanical properties of the myocardium, which affect important physiological factors such as the pumping performance of the ventricles. Strain-rate represents the local instantaneous deformation of the myocardium and can be calculated from the spatial gradients of the velocity field. Strain-rate has previously been calculated using one-dimensional (ultrasound) or two-dimensional (2-D) magnetic resonance imaging techniques. However, this assumes that myocardial motion only occurs in one direction or in one plane, respectively. This paper presents a method for calculation of the time-resolved three-dimensional (3-D) strain-rate tensor using velocity vector information in a 3-D spatial grid during the whole cardiac cycle. The strain-rate tensor provides full information of both magnitude and direction of the instantaneous deformation of the myocardium. A method for visualization of the full 3-D tensor is also suggested. The tensors are visualized using ellipsoids, which display the principal directions of strain-rate and the ratio between strain-rate magnitude in each direction. The presented method reveals the principal strain-rate directions without a priori knowledge of myocardial motion directions.

  • 22.
    Sigfridsson, Andreas
    et al.
    Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Ebbers, Tino
    Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Heiberg, Einar
    Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Wigström, Lars
    Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Tensor Field Visualisation using Adaptive Filtering of Noise Fields combined with Glyph Rendering2002In: IEEE Visualization 2002 Conference, IEEE , 2002, p. 371-378Conference paper (Refereed)
    Abstract [en]

    While many methods exist for visualising scalar and vector data, visualisation of tensor data is still troublesome. We present a method for visualising second order tensors in three dimensions using a hybrid between direct volume rendering and glyph rendering.

    An overview scalar field is created by using three-dimensional adaptive filtering of a scalar field containing noise. The filtering process is controlled by the tensor field to be visualised, creating patterns that characterise the tensor field. By combining direct volume rendering of the scalar field with standard glyph rendering methods for detailed tensor visualisation, a hybrid solution is created.

    A combined volume and glyph renderer was implemented and tested with both synthetic tensors and strain-rate tensors from the human heart muscle, calculated from phase contrast magnetic resonance image data. A comprehensible result could be obtained, giving both an overview of the tensor field as well as detailed information on individual tensors.

  • 23.
    Sutherland, George
    et al.
    Department of Cardiology, University Hospital, Gasthuisberg Leuven, Belgium.
    Kukulsi, Tomasz
    Department of Cardiology, University Hospital, Gasthuisberg Leuven, Belgium.
    Escobar Kvitting, John-Peder
    Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    D'hooge, Jan
    Department of Cardiology, University Hospital, Gasthuisberg Leuven, Belgium.
    Arnold, Martina
    Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Brandt, Einar
    Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Hatle, Liv
    Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Wranne, Bengt
    Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Quantitation of left-ventricular asynergy by cardiac ultrasound2000In: American Journal of Cardiology, ISSN 0002-9149, E-ISSN 1879-1913, Vol. 86, no 4, p. 4-9Article in journal (Refereed)
    Abstract [en]

    The clinical evaluation of regional delays in myocardial motion (myocardial asynchrony) has proved problematic, yet it remains an important functional parameter to evaluate. Prior attempts to quantify regional asynergy have met with limited success, often thwarted by the low temporal resolution of imaging-system data acquisition. If a delay in onset of motion of 30–40 msec is clinically important to measure, then data acquisition at frame rates of 50–100 per second is required. This is out of the current temporal resolution of angiographic, nuclear, or magnetic resonance studies. Only cardiac ultrasound can currently achieve the necessary frame rates. Furthermore, quantitative studies into the accuracy with which a trained observer can identify computed regional myocardial asynchrony in a left-ventricular 2-dimensional (2-D) image have shown that regional delays of <80 msec are not normally recognized in a moving image. This may be improved to 60 msec when either training is undertaken or comparative image review is used. However, this is still out of the temporal resolution required in clinical practice. Thus, visual interpretation of asynchrony is not sufficiently accurate. Two ultrasound data sets based on either integrated backscatter or Doppler myocardial imaging data may provide the solution. Doppler myocardial imaging is a new ultrasound technique which, in either its pulsed or color Doppler format, can achieve the required temporal resolution (with temporal resolutions of 8 msec and 16 msec, respectively). In contrast, color Doppler myocardial imaging, in its curved M-mode format, can display the timing of events during the cardiac cycle for all in-plane myocardial segments. This should allow the quantitation of regional delay for all systolic and diastolic events. Potentially, asynchrony due to regional ischemia, bundle branch block, ventricular premature beats, and ventricular preexcitation could all be identified and the degree of delay quantified. This overview will aim to establish the potential role of these new ultrasound methodologies in the recognition and quantitation of left-ventricular asynergy and how they might best be introduced into clinical practice.

  • 24.
    Svensson (Renner), Johan
    et al.
    Linköping University, Department of Mechanical Engineering, Applied Thermodynamics and Fluid Mechanics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Gårdhagen, Roland
    Linköping University, Department of Mechanical Engineering, Applied Thermodynamics and Fluid Mechanics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Heiberg, Einar
    Department of Clinical Physiology, Lund University, Sweden.
    Ebbers, Tino
    Linköping University, Department of Medicine and Care. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Loyd, Dan
    Linköping University, Department of Mechanical Engineering, Applied Thermodynamics and Fluid Mechanics. Linköping University, The Institute of Technology.
    Länne, Toste
    Linköping University, Department of Medicine and Care, Physiology. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Karlsson, Matts
    Linköping University, Department of Biomedical Engineering, Biomedical Modelling and Simulation. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Feasibility of Patient Specific Aortic Blood Flow CFD Simulation2006In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006: 9th International Conference, Copenhagen, Denmark, October 1-6, 2006. Proceedings, Part I / [ed] Rasmus Larsen, Mads Nielsen and Jon Sporring, Springer Berlin/Heidelberg, 2006, 1, Vol. 4190, p. 257-263Conference paper (Refereed)
    Abstract [en]

    Patient specific modelling of the blood flow through the human aorta is performed using computational fluid dynamics (CFD) and magnetic resonance imaging (MRI). Velocity patterns are compared between computer simulations and measurements. The workflow includes several steps: MRI measurement to obtain both geometry and velocity, an automatic levelset segmentation followed by meshing of the geometrical model and CFD setup to perform the simulations follwed by the actual simulations. The computational results agree well with the measured data.

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