Bidirectional Recurrence for Cardiac Motion Tracking with Gaussian Process Latent Coding

TL;DR

The paper introduces GPTrack, an unsupervised Gaussian Process-based framework that captures long-term and regional cardiac motion dynamics in 3D and 4D medical images, improving tracking accuracy.

Contribution

It proposes a novel bidirectional recursive Gaussian Process model that encodes temporal and spatial cardiac motion dynamics for improved tracking.

Findings

Enhanced motion tracking accuracy in 3D and 4D images

Robust temporal consistency and spatial variability modeling

Efficient computational performance

Abstract

Quantitative analysis of cardiac motion is crucial for assessing cardiac function. This analysis typically uses imaging modalities such as MRI and Echocardiograms that capture detailed image sequences throughout the heartbeat cycle. Previous methods predominantly focused on the analysis of image pairs lacking consideration of the motion dynamics and spatial variability. Consequently, these methods often overlook the long-term relationships and regional motion characteristic of cardiac. To overcome these limitations, we introduce the \textbf{GPTrack}, a novel unsupervised framework crafted to fully explore the temporal and spatial dynamics of cardiac motion. The GPTrack enhances motion tracking by employing the sequential Gaussian Process in the latent space and encoding statistics by spatial information at each time stamp, which robustly promotes temporal consistency and spatial variability of cardiac dynamics. Also, we innovatively aggregate sequential information in a bidirectional recursive manner, mimicking the behavior of diffeomorphic registration to better capture consistent long-term relationships of motions across cardiac regions such as the ventricles and atria. Our GPTrack significantly improves the precision of motion tracking in both 3D and 4D medical images while maintaining computational efficiency. The code is available at: https://github.com/xmed-lab/GPTrack