Harold Litt

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Myocardial fiber orientations are an important element for accurate modeling of cardiac electromechanics. However it is extremely difficult to estimate these directly in vivo with current imaging techniques. Most current methods for cardiac modeling use synthetic models of fiber orientation which may fail to capture subtle variations of fiber orientations(More)
A 4D image registration method is proposed for consistent estimation of cardiac motion from MR image sequences. Under this 4D registration framework, all 3D cardiac images taken at different time-points are registered simultaneously, and motion estimated is enforced to be spatiotemporally smooth, thereby overcoming potential limitations of some methods that(More)
This paper presents an image-based classification method, and applies it to classification of cardiac MRI scans of individuals with Tetralogy of Fallot (TOF). Clinicians frequently diagnose cardiac disease by measuring the ventricular volumes from cardiac MRI scans. Interrater variability is a common issue with these measurements. We address this issue by(More)
We propose a novel two-layer level set approach for segmentation of the left ventricle (LV) from cardiac magnetic resonance (CMR) short-axis images. In our method, endocardium and epicardium are represented by two specified level contours of a level set function. Segmentation of the LV is formulated as a problem of optimizing the level set function such(More)
In this paper, we propose an efficient groupwise mor-phometric analysis to characterize morphological variations between healthy and pathological states. The proposed framework extends the work of Baloch [4] in which a manifold for each anatomy was constructed by collecting lossless [transformation, residual] descriptors with various transformation(More)
While manifold learning from images itself has become widely used in medical image analysis, the accuracy of existing implementations suffers from viewing each image as a single data point. To address this issue, we parcellate images into regions and then separately learn the manifold for each region. We use the regional manifolds as low-dimensional(More)