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  • 1.
    Bustamante, Mariana
    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).
    Gupta, Vikas
    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).
    Forsberg, Daniel
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Sectra, Linköping, Sweden.
    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.
    Engvall, Jan
    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.
    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).
    Automated multi-atlas segmentation of cardiac 4D flow MRI2018In: Medical Image Analysis, ISSN 1361-8415, E-ISSN 1361-8423, Vol. 49, p. 128-140Article in journal (Refereed)
    Abstract [en]

    Four-dimensional (4D) flow magnetic resonance imaging (4D Flow MRI) enables acquisition of time-resolved three-directional velocity data in the entire heart and all major thoracic vessels. The segmentation of these tissues is typically performed using semi-automatic methods. Some of which primarily rely on the velocity data and result in a segmentation of the vessels only during the systolic phases. Other methods, mostly applied on the heart, rely on separately acquired balanced Steady State Free Precession (b-SSFP) MR images, after which the segmentations are superimposed on the 4D Flow MRI. While b-SSFP images typically cover the whole cardiac cycle and have good contrast, they suffer from a number of problems, such as large slice thickness, limited coverage of the cardiac anatomy, and being prone to displacement errors caused by respiratory motion. To address these limitations we propose a multi-atlas segmentation method, which relies only on 4D Flow MRI data, to automatically generate four-dimensional segmentations that include the entire thoracic cardiovascular system present in these datasets. The approach was evaluated on 4D Flow MR datasets from a cohort of 27 healthy volunteers and 83 patients with mildly impaired systolic left-ventricular function. Comparison of manual and automatic segmentations of the cardiac chambers at end-systolic and end-diastolic timeframes showed agreements comparable to those previously reported for automatic segmentation methods of b-SSFP MR images. Furthermore, automatic segmentation of the entire thoracic cardiovascular system improves visualization of 4D Flow MRI and facilitates computation of hemodynamic parameters.

    The full text will be freely available from 2020-08-13 11:32
  • 2.
    Eklund, Anders
    et al.
    Virginia Tech Carilion Research Institute, Virginia Tech, Roanoke, USA.
    Dufort, Paul
    Department of Medical Imaging, University of Toronto, Toronto, Canada.
    Forsberg, Daniel
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    LaConte, Stephen
    Virginia Tech Carilion Research Institute, Virginia Tech, Roanoke, USA.
    Medical Image Processing on the GPU: Past, Present and Future2013In: Medical Image Analysis, ISSN 1361-8415, E-ISSN 1361-8423, Vol. 17, no 8, p. 1073-1094Article, review/survey (Refereed)
    Abstract [en]

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

  • 3.
    Kirişli, Hortense
    et al.
    Erasmus MC, Rotterdam, The Netherlands.
    Schaap, M.
    Erasmus MC, Rotterdam, The Netherlands.
    Metz, C. T.
    Erasmus MC, Rotterdam, The Netherlands.
    Dharampal, A. S.
    Erasmus MC, Rotterdam, The Netherlands.
    Meijboom, W. B.
    Erasmus MC, Rotterdam, The Netherlands.
    Papadopoulou, S. L.
    Erasmus MC, Rotterdam, The Netherlands.
    Dedic, A.
    Erasmus MC, Rotterdam, The Netherlands.
    Nieman, K.
    Erasmus MC, Rotterdam, The Netherlands.
    de Graaf, M. A.
    Leiden UMC, The Netherlands.
    Meijs, M. F. L.
    UMC Utrecht, The Netherlands.
    Cramer, M. J.
    UMC Utrecht, The Netherlands.
    Broersen, A.
    Leiden UMC, The Netherlands.
    Cetin, S.
    SabancıUniversity, Turkey.
    Eslami, A.
    Technical University of Munich, Germany.
    Flórez-Valencia, L.
    Pontificia Universidad Javeriana, Bogotá, Colombia.
    Lor, K.L.
    National Taiwan University, Taipei, Taiwan.
    Matuszewski, B.
    University of Central Lancashire, Preston, UK.
    Melki, I.
    Université Paris-Est, France.
    Mohr, B.
    Toshiba Medical Visualization Systems, Edinburgh, UK.
    Öksüz, I.
    Bahçeşehir University, Istanbul, Turkey.
    Shahzad, R.
    Erasmus MC, Rotterdam The Netherlands.
    Wang, Chunliang
    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.
    Kitslaar, P. H.
    Leiden UMC, The Netherlands.
    Unal, G.
    Sabancı University, Turkey.
    Katouzian, A.
    Technical University of Munich, Germany.
    Orkisz, M.
    Université de Lyon, France.
    Chen, C.M.
    National Taiwan University, Taipei, Taiwan.
    Precioso, F.
    University Nice-Sophia Antipolis, France.
    Najman, L.
    Université Paris-Est, France.
    Masood, S.
    Toshiba Medical Visualization Systems, Edinburgh, UK.
    Ünay, D.
    Bahçeşehir University, Istanbul, Turkey.
    van Vliet, L.
    Delft University of Technology, The Netherlands.
    Moreno, Rodrigo
    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.
    Goldenberg, R.
    Rcadia Medical Imaging, Haïfa, Israel.
    Vuçini, E.
    VRVis Research Center for Virtual Reality and Visualization, Vienna, Austria.
    Krestin, G. P.
    Erasmus MC, Rotterdam, The Netherlands.
    Niessen, W. J.
    Erasmus MC, Rotterdam, The Netherlands.
    van Walsum, T.
    Erasmus MC, Rotterdam, The Netherlands.
    Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography2013In: Medical Image Analysis, ISSN 1361-8415, E-ISSN 1361-8423, Vol. 17, no 8, p. 859-876Article in journal (Refereed)
    Abstract [en]

    Though conventional coronary angiography (CCA) has been the standard of reference for diagnosing coronary artery disease in the past decades, computed tomography angiography (CIA) has rapidly emerged, and is nowadays widely used in clinical practice. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms devised to detect and quantify the coronary artery stenoses, and to segment the coronary artery lumen in CIA data. The objective of this evaluation framework is to demonstrate the feasibility of dedicated algorithms to: (I) (semi-)automatically detect and quantify stenosis on CIA, in comparison with quantitative coronary angiography (QCA) and CIA consensus reading, and (2) (semi-)automatically segment the coronary lumen on CIA, in comparison with expert's manual annotation. A database consisting of 48 multicenter multivendor cardiac CIA datasets with corresponding reference standards are described and made available. The algorithms from 11 research groups were quantitatively evaluated and compared. The results show that (1) some of the current stenosis detection/quantification algorithms may be used for triage or as a second-reader in clinical practice, and that (2) automatic lumen segmentation is possible with a precision similar to that obtained by experts. The framework is open for new submissions through the website, at http://coronary.bigr.nl/stenoses/.

  • 4.
    Moreno, Rodrigo
    et al.
    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). KTH Royal Institute of Technology.
    Smedby, Örjan
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Faculty of Medicine and Health Sciences. KTH Royal Institute of Technology.
    Gradient-Based Enhancement of Tubular Structures in Medical Images2015In: Medical Image Analysis, ISSN 1361-8415, E-ISSN 1361-8423, Vol. 26, no 1, p. 19-29Article in journal (Refereed)
    Abstract [en]

    Vesselness filters aim at enhancing tubular structures in medical images. The most popular vesselness filters are based on eigenanalyses of the Hessian matrix computed at different scales. However, Hessian-based methods have well-known limitations, most of them related to the use of second order derivatives. In this paper, we propose an alternative strategy in which ring-like patterns are sought in the local orientation distribution of the gradient. The method takes advantage of symmetry properties of ring-like patterns in the spherical harmonics domain. For bright vessels, gradients not pointing towards the center are filtered out from every local neighborhood in a first step. The opposite criterion is used for dark vessels. Afterwards, structuredness, evenness and uniformness measurements are computed from the power spectrum in spherical harmonics of both the original and the half-zeroed orientation distribution of the gradient. Finally, the features are combined into a single vesselness measurement. Alternatively, a structure tensor that is suitable for vesselness can be estimated before the analysis in spherical harmonics. The two proposed methods are called Ring Pattern Detector (RPD) and Filtered Structure Tensor (FST) respectively. Experimental results with computed tomography angiography data show that the proposed filters perform better compared to the state-of-the-art.

  • 5.
    Schaap, M.
    et al.
    Biomedical Imaging Group Rotterdam, The Netherlands.
    Metz, C.T.
    Biomedical Imaging Group Rotterdam, The Netherlands.
    van Walsum, T.
    Biomedical Imaging Group Rotterdam, The Netherlands.
    van der Giessen, A.G.
    Biomedical Imaging Group Rotterdam, The Netherlands.
    Weustink, A.C.
    Erasmus MC, Rotterdam, The Netherlands.
    Mollet, N.R.
    Erasmus MC, Rotterdam, The Netherlands.
    Bauer, C.
    Graz University of Technology, Austria.
    Bogunovic, H.
    Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Barcelona, Spain.
    Castro, C.
    Universidad Politécnica de Madrid, Spain.
    Deng, X.
    Siemens Corporate Research, Princeton, NJ, USA.
    Dikici, E.
    University of Florida College of Medicine, Jacksonville, FL, USA.
    O'Donnell, T.
    Siemens Corporate Research, Princeton, NJ, USA.
    Frenay, M.
    Leiden University Medical Center, The Netherlands.
    Friman, O.
    MeVis Research, Bremen, Germany.
    Hoyos, M.H.
    Universidad de los Andes, Bogota, Colombia.
    Kitslaar, P.H.
    Leiden University Medical Center, The Netherlands.
    Krissian, K.
    University of Las Palmas of Gran Canaria, Spain.
    Kuhnel, C.
    MeVis Research, Bremen, Germany.
    Luengo-Oroz, M.A.
    Universidad Politécnica de Madrid, Spain.
    Orkisz, M.
    Université de Lyon, France.
    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 Medical Imaging, Department of Radiology in Linköping.
    Styner, M.
    University of North Carolina, Chapel Hill, NC, USA.
    Szymczak, A.
    Colorado School of Mines, Golden, CO, USA.
    Tek, H.
    Siemens Corporate Research, Princeton, NJ, USA.
    Wang, Chunliang
    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.
    Warfield, S.K.
    Childrens Hospital Boston, MA, USA.
    Zambal, S.
    VRVis Research Center for Virtual Reality and Visualization, Vienna, Austria.
    Zhang, Y.
    The Methodist Hospital Research Institute, Houston, TX, USA.
    Krestin, G.P.
    Erasmus MC, Rotterdam, The Netherlands.
    Niessen, W.J.
    Erasmus MC, Rotterdam, The Netherlands.
    Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms2009In: Medical Image Analysis, ISSN 1361-8415, E-ISSN 1361-8423, Vol. 13, no 5, p. 701-714Article in journal (Refereed)
    Abstract [en]

    Efficiently obtaining a reliable coronary artery centerline from computed tomography angiography data is relevant in clinical practice. Whereas numerous methods have been presented for this purpose, up to now no standardized evaluation methodology has been published to reliably evaluate and compare the performance of the existing or newly developed coronary artery centerline extraction algorithms. This paper describes a standardized evaluation methodology and reference database for the quantitative evaluation of coronary artery centerline extraction algorithms. The contribution of this work is fourfold: (1) a method is described to create a consensus centerline with multiple observers, (2) well-defined measures are presented for the evaluation of coronary artery centerline extraction algorithms, (3) a database containing 32 cardiac CTA datasets with corresponding reference standard is described and made available, and (4) 13 coronary artery centerline extraction algorithms, implemented by different research groups, are quantitatively evaluated and compared. The presented evaluation framework is made available to the medical imaging community for benchmarking existing or newly developed coronary centerline extraction algorithms.

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