In-silico heart model phantom to validate cardiac strain imaging

Mukherjee, T.; Usman, M.; Mehdi, R. R.; Mendiola, E.; Ohayon, J.; Lindquist, D.; Shah, D.; Sadayappan, S.; Pettigrew, R.; Avazmohammadi, R.

doi:10.1101/2024.08.05.606672

bioRxiv · 10.1101/2024.08.05.606672

In-silico heart model phantom to validate cardiac strain imaging

Mukherjee, T.·Usman, M.·Mehdi, R. R.·Mendiola, E.·Ohayon, J.·Lindquist, D.·Shah, D.·Sadayappan, S.·Pettigrew, R.·Avazmohammadi, R.

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Abstract

The quantification of cardiac strains as structural indices of cardiac function has a growing prevalence in clinical diagnosis. However, the highly heterogeneous four-dimensional (4D) cardiac motion challenges accurate "regional" strain quantification and leads to sizable differences in the estimated strains depending on the imaging modality and post-processing algorithm, limiting the translational potential of strains as incremental biomarkers of cardiac dysfunction. There remains a crucial need for a feasible benchmark that successfully replicates complex 4D cardiac kinematics to determine the reliability of strain calculation algorithms. In this study, we propose an in-silico heart phantom derived from finite element (FE) simulations to validate the quantification of 4D regional strains. First, as a proof-of-concept exercise, we created synthetic magnetic resonance (MR) images for a hollow thick-walled cylinder under pure torsion with an exact solution and demonstrated that "ground-truth" values can be recovered for the twist angle, which is also a key kinematic index in the heart. Next, we used mouse-specific FE simulations of cardiac kinematics to synthesize dynamic MR images by sampling various sectional planes of the left ventricle (LV). Strains were calculated using our recently developed non-rigid image registration (NRIR) framework in both problems. Moreover, we studied the effects of image quality on distorting regional strain calculations by conducting in-silico experiments for various LV configurations. Our studies offer a rigorous and feasible tool to standardize regional strain calculations to improve their clinical impact as incremental biomarkers.

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Mukherjee, T., Usman, M., Mehdi, R. R., Mendiola, E., Ohayon, J., Lindquist, D., Shah, D., Sadayappan, S., Pettigrew, R., Avazmohammadi, R.. 2024-08-07. In-silico heart model phantom to validate cardiac strain imaging. https://doi.org/10.1101/2024.08.05.606672

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