Why the study?
Cardiac diffusion tensor imaging (cDTI) non-destructively measures average cardiomyocyte long-axis orientation, but long scan times challenge high-resolution acquisitions, requiring interpolation techniques for computational modeling.
Tensor interpolation techniques provide lower interpolation errors than low-rank models and rule-based methods for populating bio-mechanical models with patient-specific cardiomyocyte orientations from cDTI data.
Supports tensor interpolation for cDTI in animals; leaves open human validation for biomechanical models.
Cardiac electrophysiology and cardiac mechanics both depend on the average cardiomyocyte long-axis orientation. In the realm of personalized medicine, knowledge of the patient-specific changes in cardiac microstructure plays a crucial role. Patient-specific computational modelling has emerged as a tool to better understand disease progression. In vivo cardiac diffusion tensor imaging (cDTI) is a vital tool to non-destructively measure the average cardiomyocyte long-axis orientation in the heart. However, cDTI suffers from long scan times, rendering volumetric, high-resolution acquisitions challenging. Consequently, interpolation techniques are needed to populate bio-mechanical models with patient-specific average cardiomyocyte long-axis orientations. In this work, we compare five interpolation techniques applied to in vivo and ex vivo porcine input data. We compare two tensor interpolation approaches, one rule-based approximation, and two data-driven, low-rank models. We demonstrate the advantage of tensor interpolation techniques, resulting in lower interpolation errors than do low-rank models and rule-based methods adapted to cDTI data. In an ex vivo comparison, we study the influence of three imaging parameters that can be traded off against acquisition time: in-plane resolution, signal to noise ratio, and number of acquired short-axis imaging slices.
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Stimm et al. (2021) studied this question.
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