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  • 1.
    Bernard, Olivier
    et al.
    University of Lyon 1, France.
    Bosch, Johan G.
    Erasmus MC, Netherlands.
    Heyde, Brecht
    Katholieke University of Leuven, Belgium.
    Alessandrini, Martino
    Katholieke University of Leuven, Belgium.
    Barbosa, Daniel
    University of Minho, Portugal.
    Camarasu-Pop, Sorina
    University of Lyon 1, France.
    Cervenansky, Frederic
    University of Lyon 1, France.
    Valette, Sebastien
    University of Lyon 1, France.
    Mirea, Oana
    Katholieke University of Leuven, Belgium.
    Bernier, Michel
    University of Sherbrooke, Canada.
    Jodoin, Pierre-Marc
    University of Sherbrooke, Canada.
    Santo Domingos, Jaime
    University of Oxford, England.
    Stebbing, Richard V.
    University of Oxford, England.
    Keraudren, Kevin
    University of London Imperial Coll Science Technology and Med, England.
    Oktay, Ozan
    University of London Imperial Coll Science Technology and Med, England.
    Caballero, Jose
    University of London Imperial Coll Science Technology and Med, England.
    Shi, Wei
    University of London Imperial Coll Science Technology and Med, England.
    Rueckert, Daniel
    University of London Imperial Coll Science Technology and Med, England.
    Milletari, Fausto
    Technical University of Munich, Germany.
    Ahmadi, Seyed-Ahmad
    University of Munich, Germany.
    Smistad, Erik
    Norwegian University of Science and Technology, Norway.
    Lindseth, Frank
    Norwegian University of Science and Technology, Norway.
    van Stralen, Maartje
    University of Medical Centre Utrecht, Netherlands.
    Wang, Chen
    Not Found:Linkoping Univ, Dept Med and Hlth Sci IMH, Ctr Med Imaging Sci and Visualizat CMIV, SE-58185 Linkoping, Sweden.
    Smedby, Örjan
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Donal, Erwan
    University of Rennes 1, France; University of Rennes 1, France; University of Rennes 1, France.
    Monaghan, Mark
    Kings Coll Hospital NHS Fdn Trust, England.
    Papachristidis, Alex
    Kings Coll Hospital NHS Fdn Trust, England.
    Geleijnse, Marcel L.
    Erasmus MC, Netherlands.
    Galli, Elena
    University of Rennes 1, France; University of Rennes 1, France; University of Rennes 1, France.
    Dhooge, Jan
    Katholieke University of Leuven, Belgium.
    Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography2016In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 35, no 4, p. 967-977Article in journal (Refereed)
    Abstract [en]

    Real-time 3D Echocardiography (RT3DE) has been proven to be an accurate tool for left ventricular (LV) volume assessment. However, identification of the LV endocardium remains a challenging task, mainly because of the low tissue/blood contrast of the images combined with typical artifacts. Several semi and fully automatic algorithms have been proposed for segmenting the endocardium in RT3DE data in order to extract relevant clinical indices, but a systematic and fair comparison between such methods has so far been impossible due to the lack of a publicly available common database. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms developed to segment the LV border in RT3DE. A database consisting of 45 multivendor cardiac ultrasound recordings acquired at different centers with corresponding reference measurements from three experts are made available. The algorithms from nine research groups were quantitatively evaluated and compared using the proposed online platform. The results showed that the best methods produce promising results with respect to the experts measurements for the extraction of clinical indices, and that they offer good segmentation precision in terms of mean distance error in the context of the experts variability range. The platform remains open for new submissions.

  • 2.
    Hemmendorff, Magnus
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Kronander, Torbjörn
    SECTRA AB, Linköping, Sweden.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Phase-based multidimensional volume registration2002In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 21, no 12, p. 1536-1543Article in journal (Refereed)
    Abstract [en]

    We present a method for accurate image registration and motion compensation in multidimensional signals, such as two-dimensional (2-D) X-ray images and three-dimensional (3-D) computed tomography/magnetic resonance imaging volumes. The method is based on phase from quadrature filters, which makes it robust to noise and temporal intensity variations. The method is equally applicable to signals of two, three or higher number of dimensions. We use parametric models, e.g., affine models, finite elements or local affine models with global regularization. Experimental results show high accuracy for 2-D and 3-D motion compensation.

  • 3.
    Lauritsch, Günter
    et al.
    Tyskland.
    Boese, Jan
    Tyskland.
    Wigström, Lars
    Stanford University.
    Kemeth, Herbert
    Tyskland.
    Fahrig, Rebecca
    Stanford, USA.
    Towards cardiac C-arm computed tomography2006In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 7, p. 922-934Article in journal (Refereed)
    Abstract [en]

      Cardiac interventional procedures would benefit tremendously from sophisticated three-dimensional image guidance. Such procedures are typically performed with C-arm angiography systems, and tomographic imaging is currently available only by using preprocedural computed tomography (CT) or magnetic resonance imaging (MRI) scans. Recent developments in C-arm CT (Angiographic CT) allow three-dimensional (3-D) imaging of low contrast details with angiography imaging systems for noncardiac applications. We propose a new approach for cardiac imaging that takes advantage of this improved contrast resolution and is based on intravenous contrast injection. The method is an analogue to multisegment reconstruction in cardiac CT adapted to the much slower rotational speed of C-arm CT. Motion of the heart is considered in the reconstruction process by retrospective electrocardiogram (ECG)-gating, using only projections acquired at a similar heart phase. A series of N almost identical rotational acquisitions is performed at different heart phases to obtain a complete data set at a minimum temporal resolution of 1/N of the heart cycle time. First results in simulation, using an experimental phantom, and in preclinical in vivo studies showed that excellent image quality can be achieved

  • 4.
    Magnusson, Maria
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Danielsson, Per-Erik
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Sunnegårdh, Johan
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Engineering.
    Handling of Long Objects in Iterative Improvement of Non-Exact Reconstruction in Helical Cone-Beam CT2006In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 25, no 7, p. 935-940Article in journal (Refereed)
    Abstract [en]

     In medical helical cone-beam CT, it is common that the region-of-interest (ROI) is contained inside the helix cylinder, while the complete object is long and extends outside the top and the bottom of the cylinder. This is the Long Object Problem. Analytical reconstruction methods for helical cone-beam CT have been designed to handle this problem. It has been shown that a moderate amount of over-scanning is sufficient for reconstruction of a certain ROI. The over-scanning projection rays travel both through the ROI as well as outside the ROI. This is unfortunate for iterative methods since it seems impossible to compute accurate values for the projection rays which travel partly inside and partly outside the ROI. Therefore, it seems that the useful ROI will diminish for every iteration step. We propose the following solution to the problem. Firstly, we reconstruct volume regions also outside the ROI. These volume regions will certainly be incompletely reconstructed, but our experimental results show that they serve well for projection generation. This is rather counter-intuitive and contradictory to our initial assumptions. Secondly, we use careful extrapolation and masking of projection data. This is not a general necessity, but needed for the chosen iterative algorithm, which includes rebinning and iterative filtered backprojection. Our idea here was to use an approximate reconstruction method which gives cone-beam artifacts and then improve the reconstructed result by iterative filtered backprojection. The experimental results seem very encouraging. The cone-beam artifacts can indeed be removed. Even voxels close to the boundary of the ROI are as well enhanced by the iterative loop as those in the middle of the ROI.

  • 5.
    Pedrosa, Joao
    et al.
    Katholieke University of Leuven, Belgium.
    Queiros, Sandro
    Katholieke University of Leuven, Belgium; University of Minho, Portugal; University of Minho, Portugal.
    Bernard, Olivier
    University of Lyon 1, France.
    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.
    Edvardsen, Thor
    University of Oslo, Norway; Oslo University Hospital, Norway.
    Nagel, Eike
    University Hospital Frankfurt Main, Germany.
    Dhooge, Jan
    Katholieke University of Leuven, Belgium.
    Fast and Fully Automatic Left Ventricular Segmentation and Tracking in Echocardiography Using Shape-Based B-Spline Explicit Active Surfaces2017In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 36, no 11, p. 2287-2296Article in journal (Refereed)
    Abstract [en]

    Cardiac volume/function assessment remains a critical step in daily cardiology, and 3-D ultrasound plays an increasingly important role. Fully automatic left ventricular segmentation is, however, a challenging task due to the artifacts and low contrast-to-noise ratio of ultrasound imaging. In this paper, a fast and fully automatic framework for the full-cycle endocardial left ventricle segmentation is proposed. This approach couples the advantages of the B-spline explicit active surfaces framework, a purely image information approach, to those of statistical shape models to give prior information about the expected shape for an accurate segmentation. The segmentation is propagated throughout the heart cycle using a localized anatomical affine optical flow. It is shown that this approach not only outperforms other state-of-the-art methods in terms of distance metrics with a mean average distances of 1.81 +/- 0.59 and 1.98 +/- 0.66 mm at end-diastole and end-systole, respectively, but is computationally efficient (in average 11 s per 4-D image) and fully automatic.

  • 6.
    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.

1 - 6 of 6
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