  Quantification of cardiac motion with cine Cardiac Magnetic Resonance Imaging
(CMRI) is an integral part of arrhythmogenic right ventricular cardiomyopathy
(ARVC) diagnosis. Yet, the expert evaluation of motion abnormalities with CMRI
is a challenging task. To automatically assess cardiac motion, we register
CMRIs from different time points of the cardiac cycle using Implicit Neural
Representations (INRs) and perform a biomechanically informed regularization
inspired by the myocardial incompressibility assumption. To enhance the
registration performance, our method first rectifies the inter-slice
misalignment inherent to CMRI by performing a rigid registration guided by the
long-axis views, and then increases the through-plane resolution using an
unsupervised deep learning super-resolution approach. Finally, we propose to
synergically combine information from short-axis and 4-chamber long-axis views,
along with an initialization to incorporate information from multiple cardiac
time points. Thereafter, to quantify cardiac motion, we calculate global and
segmental strain over a cardiac cycle and compute the peak strain. The
evaluation of the method is performed on a dataset of cine CMRI scans from 47
ARVC patients and 67 controls. Our results show that inter-slice alignment and
generation of super-resolved volumes combined with joint analysis of the two
cardiac views, notably improves registration performance. Furthermore, the
proposed initialization yields more physiologically plausible registrations.
The significant differences in the peak strain, discerned between the ARVC
patients and healthy controls suggest that automated motion quantification
methods may assist in diagnosis and provide further understanding of
disease-specific alterations of cardiac motion.
