Implicit Neural Representations for Registration of Left Ventricle Myocardium During a Cardiac Cycle

TL;DR

This paper introduces an efficient implicit neural representation method for registering the left ventricle myocardium in cardiac CT images, improving accuracy and robustness in modeling myocardial motion during the cardiac cycle.

Contribution

It extends implicit neural representations to cardiac image registration, integrating signed distance fields and tissue information for enhanced LVmyo motion analysis.

Findings

High registration accuracy achieved

Robust temporal registration demonstrated

Effective modeling of LVmyo motion

Abstract

Understanding the movement of the left ventricle myocardium (LVmyo) during the cardiac cycle is essential for assessing cardiac function. One way to model this movement is through a series of deformable image registrations (DIRs) of the LVmyo. Traditional deep learning methods for DIRs, such as those based on convolutional neural networks, often require substantial memory and computational resources. In contrast, implicit neural representations (INRs) offer an efficient approach by operating on any number of continuous points. This study extends the use of INRs for DIR to cardiac computed tomography (CT), focusing on LVmyo registration. To enhance the precision of the registration around the LVmyo, we incorporate the signed distance field of the LVmyo with the Hounsfield Unit values from the CT frames. This guides the registration of the LVmyo, while keeping the tissue information from the CT frames. Our framework demonstrates high registration accuracy and provides a robust method for temporal registration that facilitates further analysis of LVmyo motion.