Neural Multi-Scale Self-Supervised Registration for Echocardiogram Dense Tracking

TL;DR

This paper introduces a neural multi-scale self-supervised registration method that significantly improves automated dense tracking of myocardial motion and blood flow in echocardiograms, addressing noise and variability challenges.

Contribution

The proposed NMSR method uniquely combines deep neural velocity field parameterization with multi-scale optimization for improved echocardiogram registration.

Findings

Outperforms state-of-the-art registration methods like ANTs and VoxelMorph.

Provides accurate and automated myocardial and blood flow tracking.

Enhances analysis speed and reliability of echocardiogram diagnostics.

Abstract

Echocardiography has become routinely used in the diagnosis of cardiomyopathy and abnormal cardiac blood flow. However, manually measuring myocardial motion and cardiac blood flow from echocardiogram is time-consuming and error-prone. Computer algorithms that can automatically track and quantify myocardial motion and cardiac blood flow are highly sought after, but have not been very successful due to noise and high variability of echocardiography. In this work, we propose a neural multi-scale self-supervised registration (NMSR) method for automated myocardial and cardiac blood flow dense tracking. NMSR incorporates two novel components: 1) utilizing a deep neural net to parameterize the velocity field between two image frames, and 2) optimizing the parameters of the neural net in a sequential multi-scale fashion to account for large variations within the velocity field. Experiments demonstrate that NMSR yields significantly better registration accuracy than state-of-the-art methods, such as advanced normalization tools (ANTs) and VoxelMorph, for both myocardial and cardiac blood flow dense tracking. Our approach promises to provide a fully automated method for fast and accurate analyses of echocardiograms.