Deep Learning for Automatic Strain Quantification in Arrhythmogenic Right Ventricular Cardiomyopathy

TL;DR

This paper introduces a comprehensive deep learning-based framework for automatic cardiac motion quantification in ARVC diagnosis, combining image registration, super-resolution, and multi-view analysis to improve accuracy and clinical relevance.

Contribution

It develops an integrated method using implicit neural representations, biomechanical regularization, and multi-view data fusion for improved cardiac motion assessment.

Findings

Enhanced registration accuracy with inter-slice alignment and super-resolution.

More physiologically plausible registrations with improved initialization.

Significant differences in peak strain between ARVC patients and controls.

Abstract

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.