CNN-based Cardiac Motion Extraction to Generate Deformable Geometric Left Ventricle Myocardial Models from Cine MRI

TL;DR

This paper introduces a CNN-based framework for extracting patient-specific cardiac models from cine MRI, leveraging deep learning for accurate motion estimation and model propagation across cardiac phases.

Contribution

It presents a novel deep learning approach using VoxelMorph CNN to generate detailed LV myocardial models from cine MRI, outperforming traditional registration methods.

Findings

CNN-based models closely match segmented models at each cardiac phase.

The proposed method outperforms traditional nonrigid registration techniques.

Deep learning enables efficient and accurate cardiac motion estimation.

Abstract

Patient-specific left ventricle (LV) myocardial models have the potential to be used in a variety of clinical scenarios for improved diagnosis and treatment plans. Cine cardiac magnetic resonance (MR) imaging provides high resolution images to reconstruct patient-specific geometric models of the LV myocardium. With the advent of deep learning, accurate segmentation of cardiac chambers from cine cardiac MR images and unsupervised learning for image registration for cardiac motion estimation on a large number of image datasets is attainable. Here, we propose a deep leaning-based framework for the development of patient-specific geometric models of LV myocardium from cine cardiac MR images, using the Automated Cardiac Diagnosis Challenge (ACDC) dataset. We use the deformation field estimated from the VoxelMorph-based convolutional neural network (CNN) to propagate the isosurface mesh and volume mesh of the end-diastole (ED) frame to the subsequent frames of the cardiac cycle. We assess the CNN-based propagated models against segmented models at each cardiac phase, as well as models propagated using another traditional nonrigid image registration technique.