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We developed an automated technique for non-rigid image registration as a basis for tracking the heart in contrast-enhanced cardiac magnetic resonance (CMR) image sequences. The goal of the present work was to validate this technique against conventional manual analysis. Our approach is based on a multi-scale extension of the normalized 2D cross-correlation algorithm in combination with level-set...
We have recently developed an automated technique using noise-based level-set methods and non-rigid registration for endocardial and epicardial border detection as a basis for perfusion quantification from cardiac magnetic resonance (CMR) images. The goal of the present work was to validate this technique against conventional manual analysis both directly and using quantitative coronary angiography...
We developed a method for automated quantification of myocardial perfusion from cardiac magnetic resonance (CMR) images. Our approach uses region-based and edge-based level set techniques for endocardial and epicardial border detection combined with non-rigid registration achieved by a 2D multi-scale cross-correlation and contour adaptation. This method was tested on 66 short-axis image sequences...
We developed a technique for automated identification of 3D myocardial ROI suitable for translation-free quantification of myocardial videointensity over time, MVI(t), from RT3DE images. Our software was tested on 12 ECG-triggered RT3DE datasets obtained in pigs during transient contrast inflow. Analysis included: (1) semi-automated detection of endo- and epicardial surfaces using level-set techniques...
Assessment of left ventricular (LV) size and function from cardiac magnetic resonance (CMR) images requires manual tracing of LV borders on multiple 2D slices, which is subjective, experience dependent, tedious and time-consuming. We tested a new method for automated dynamic segmentation of CMR images based on a modified region-based model, in which a level set function minimizes a functional containing...
Off-line 3D color-encoding was applied to the left ventricular (LV) endocardial surfaces obtained from real-time 3D echocardiographic images. Systolic regional wall motion abnormalities (RWMA) were automatically detected and the accuracy of this method was tested against expert visual interpretation of 2D echocardiographic (2DE) images (apical 2-, 3- and 4- chamber). 20 subjects with normal wall motion...
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