Key result
A data-driven, personalizable parametric model using low-rank basis functions extracted from ex-vivo cDTI demonstrated higher flexibility than existing atlas and rule-based approaches.
Why the study?
Studying the link between myocardial structural characteristics, strain, stresses, and cardiac function requires a full volumetric representation of the microstructure.
A novel data-driven parametric model using MRI data can accurately represent 3D cardiac myocyte orientation, offering higher flexibility than existing approaches and potential for personalized computational models.
May advance personalized computational cardiac models in research; leaves open human in vivo validation.
Cardiac myocyte aggregate orientation has a strong impact on cardiac electrophysiology and mechanics. Studying the link between structural characteristics, strain, and stresses over the cardiac cycle and cardiac function requires a full volumetric representation of the microstructure. In this work, we exploit the structural similarity across hearts to extract a low-rank representation of predominant myocyte orientation in the left ventricle from high-resolution magnetic resonance ex-vivo cardiac diffusion tensor imaging (cDTI) in porcine hearts. We compared two reduction methods, Proper Generalized Decomposition combined with Singular Value Decomposition and Proper Orthogonal Decomposition. We demonstrate the existence of a general set of basis functions of aggregated myocyte orientation which defines a data-driven, personalizable, parametric model featuring higher flexibility than existing atlas and rule-based approaches. A more detailed representation of microstructure matching the available patient data can improve the accuracy of personalized computational models. Additionally, we approximate the myocyte orientation of one ex-vivo human heart and demonstrate the feasibility of transferring the basis functions to humans.
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Stimm et al. (2021) studied this question. Low-rank representation of predominant myocyte orientation vs. Existing atlas and rule-based approaches was evaluated on Model flexibility and accuracy. A data-driven, personalizable parametric model using low-rank basis functions extracted from ex-vivo cDTI demonstrated higher flexibility than existing atlas and rule-based approaches.
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