Deformable models with sparsity constraints for cardiac motion analysis

• We propose a new class of deformable models using sparse regularization.
• It is able to handle gross errors or outliers robustly and adaptively during deformation.
• It is formulated as convex optimization problems, and solved by harnessing sparse structures.
• We apply this method to tackle a challenging problem, i.e., mouse LV motion analysis using tMRI.

Deformable models integrate bottom-up information derived from image appearance cues and top-down priori knowledge of the shape. They have been widely used with success in medical image analysis. One limitation of traditional deformable models is that the information extracted from the image data may contain gross errors, which adversely affect the deformation accuracy. To alleviate this issue, we introduce a new family of deformable models that are inspired from the compressed sensing, a technique for accurate signal reconstruction by harnessing some sparseness priors. In this paper, we employ sparsity constraints to handle the outliers or gross errors, and integrate them seamlessly with deformable models. The proposed new formulation is applied to the analysis of cardiac motion using tagged magnetic resonance imaging (tMRI), where the automated tagging line tracking results are very noisy due to the poor image quality. Our new deformable models track the heart motion robustly, and the resulting strains are consistent with those calculated from manual labels.

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