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Abstract #0786

3D sub-millimeter personalized estimation of cardiomyocyte orientation using dimensionality reduction

Johanna Stimm1, Stefano Buoso1, Martin Genet2,3,4, Sebastian Kozerke1, and Christian T Stoeck1
1Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland, 2Laboratoire de Mécanique des Solides, École Polytechnique, Paris, France, 3C.N.R.S./Université Paris-Saclay, Palaiseau, France, 4M3DISIM team, Inria / Université Paris-Saclay, Palaiseau, France

We propose a parametric low-rank representation of major characteristics of cardiomyocyte orientation in a shape-adapted coordinate system from 3D high-resolution ex-vivo cDTI data by exploiting structural similarity across hearts. We compare two dimensionality reduction methods, namely Proper Orthogonal Decomposition and Proper Generalized Decomposition. These low-order descriptions can be fit to sparse, noisy or low-resolution target data. Transferring high-resolution microstructural information with this parametric representation shows potential for in-vivo denoising and 3D extrapolation.

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