High-resolution 3D Maps of Left Atrial Displacements using an Unsupervised Image Registration Neural Network

TL;DR

This paper introduces an unsupervised neural network method for high-resolution 3D mapping of left atrial displacements from Cine MRI, enabling automatic segmentation and detailed motion analysis of the atrium.

Contribution

It presents a novel unsupervised image registration neural network that automatically segments the left atrium and accurately tracks its motion in 3D from Cine MRI data.

Findings

Achieved average Hausdorff distance of 2.51 mm

Obtained Dice score of 0.96 for segmentation

Enabled detailed 3D LA displacement mapping

Abstract

Functional analysis of the left atrium (LA) plays an increasingly important role in the prognosis and diagnosis of cardiovascular diseases. Echocardiography-based measurements of LA dimensions and strains are useful biomarkers, but they provide an incomplete picture of atrial deformations. High-resolution dynamic magnetic resonance images (Cine MRI) offer the opportunity to examine LA motion and deformation in 3D, at higher spatial resolution and with full LA coverage. However, there are no dedicated tools to automatically characterise LA motion in 3D. Thus, we propose a tool that automatically segments the LA and extracts the displacement fields across the cardiac cycle. The pipeline is able to accurately track the LA wall across the cardiac cycle with an average Hausdorff distance of $2.51 \pm 1.3~mm$ and Dice score of $0.96 \pm 0.02$.