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Learning correspondences of cardiac motion from images using biomechanics-informed modeling

By Xiaoran Zhang and others
Learning spatial-temporal correspondences in cardiac motion from images is important for understanding the underlying dynamics of cardiac anatomical structures. Many methods explicitly impose smoothness constraints such as the \(\mathcal{L}_2\) norm on the displacement vector field (DVF), while usually ignoring biomechanical feasibility in the transformation. Other geometric constraints either regularize specific... Show more
September 1, 2022
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Learning correspondences of cardiac motion from images using biomechanics-informed modeling
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