Automated Multi-Channel Segmentation for the 4D Myocardial Velocity Mapping Cardiac MR

TL;DR

This paper introduces an automated multi-channel segmentation framework using deep learning for 4D myocardial velocity mapping in cardiac MRI, improving delineation accuracy by leveraging multi-channel data and attention mechanisms.

Contribution

It presents a novel deep learning approach that fuses multi-channel CMR data with attention modules and shape-based post-processing for improved myocardial segmentation.

Findings

Enhanced Dice scores with multi-channel data

Improved myocardial velocity quantification

Outperforms standard U-Net methods

Abstract

Four-dimensional (4D) left ventricular myocardial velocity mapping (MVM) is a cardiac magnetic resonance (CMR) technique that allows assessment of cardiac motion in three orthogonal directions. Accurate and reproducible delineation of the myocardium is crucial for accurate analysis of peak systolic and diastolic myocardial velocities. In addition to the conventionally available magnitude CMR data, 4D MVM also acquires three velocity-encoded phase datasets which are used to generate velocity maps. These can be used to facilitate and improve myocardial delineation. Based on the success of deep learning in medical image processing, we propose a novel automated framework that improves the standard U-Net based methods on these CMR multi-channel data (magnitude and phase) by cross-channel fusion with attention module and shape information based post-processing to achieve accurate delineation of both epicardium and endocardium contours. To evaluate the results, we employ the widely used Dice scores and the quantification of myocardial longitudinal peak velocities. Our proposed network trained with multi-channel data shows enhanced performance compared to standard U-Net based networks trained with single-channel data. Based on the results, our method provides compelling evidence for the design and application for the multi-channel image analysis of the 4D MVM CMR data.