Neural Fields for Continuous Periodic Motion Estimation in 4D Cardiovascular Imaging

TL;DR

This paper introduces a neural fields-based approach to estimate continuous, periodic wall deformations in 4D cardiovascular imaging, enabling detailed visualization and quantification of heart cycle dynamics from MRI data.

Contribution

It presents a novel neural implicit representation method that models time-dependent blood vessel wall motion, incorporating periodicity to improve 4D flow MRI analysis.

Findings

Effective on synthetic and real 4D flow MRI data

Accurately captures periodic wall deformations

Enhances visualization of cardiac cycle dynamics

Abstract

Time-resolved three-dimensional flow MRI (4D flow MRI) provides a unique non-invasive solution to visualize and quantify hemodynamics in blood vessels such as the aortic arch. However, most current analysis methods for arterial 4D flow MRI use static artery walls because of the difficulty in obtaining a full cycle segmentation. To overcome this limitation, we propose a neural fields-based method that directly estimates continuous periodic wall deformations throughout the cardiac cycle. For a 3D + time imaging dataset, we optimize an implicit neural representation (INR) that represents a time-dependent velocity vector field (VVF). An ODE solver is used to integrate the VVF into a deformation vector field (DVF), that can deform images, segmentation masks, or meshes over time, thereby visualizing and quantifying local wall motion patterns. To properly reflect the periodic nature of 3D + time cardiovascular data, we impose periodicity in two ways. First, by periodically encoding the time input to the INR, and hence VVF. Second, by regularizing the DVF. We demonstrate the effectiveness of this approach on synthetic data with different periodic patterns, ECG-gated CT, and 4D flow MRI data. The obtained method could be used to improve 4D flow MRI analysis.