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

09/05/2023

∙

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 ± 1.3 mm and Dice score of 0.96 ± 0.02.

READ FULL TEXT

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

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

GridNet with automatic shape prior registration for automatic MRI cardiac segmentation

Automatic Cardiac Disease Assessment on cine-MRI via Time-Series Segmentation and Domain Specific Features

Automatic Assessment of Full Left Ventricular Coverage in Cardiac Cine Magnetic Resonance Imaging with Fisher-Discriminative 3D CNN

Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection

3D High-Resolution Cardiac Segmentation Reconstruction from 2D Views using Conditional Variational Autoencoders

Flow Network Tracking for Spatiotemporal and Periodic Point Matching: Applied to Cardiac Motion Analysis

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

Related Research

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

GridNet with automatic shape prior registration for automatic MRI cardiac segmentation

Automatic Cardiac Disease Assessment on cine-MRI via Time-Series Segmentation and Domain Specific Features

Automatic Assessment of Full Left Ventricular Coverage in Cardiac Cine Magnetic Resonance Imaging with Fisher-Discriminative 3D CNN

Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection

3D High-Resolution Cardiac Segmentation Reconstruction from 2D Views using Conditional Variational Autoencoders

Flow Network Tracking for Spatiotemporal and Periodic Point Matching: Applied to Cardiac Motion Analysis