Deep-learning-based groupwise registration for motion correction of cardiac $T_1$ mapping

TL;DR

This paper introduces a deep-learning-based groupwise registration method called PCA-Relax that improves motion correction in cardiac T1 mapping MRI by aligning multiple images simultaneously without needing a template.

Contribution

The novel PCA-Relax framework enables template-free, simultaneous registration of multiple images using PCA and relaxometry losses, enhancing motion correction in cardiac MRI.

Findings

PCA-Relax outperforms baseline methods in registration accuracy.

The method improves T1 map quality across different training strategies.

It demonstrates robustness on in-house cardiac MRI datasets.

Abstract

Quantitative $T_1$ mapping by MRI is an increasingly important tool for clinical assessment of cardiovascular diseases. The cardiac $T_1$ map is derived by fitting a known signal model to a series of baseline images, while the quality of this map can be deteriorated by involuntary respiratory and cardiac motion. To correct motion, a template image is often needed to register all baseline images, but the choice of template is nontrivial, leading to inconsistent performance sensitive to image contrast. In this work, we propose a novel deep-learning-based groupwise registration framework, which omits the need for a template, and registers all baseline images simultaneously. We design two groupwise losses for this registration framework: the first is a linear principal component analysis (PCA) loss that enforces alignment of baseline images irrespective of the intensity variation, and the second is an auxiliary relaxometry loss that enforces adherence of intensity profile to the signal model. We extensively evaluated our method, termed ``PCA-Relax'', and other baseline methods on an in-house cardiac MRI dataset including both pre- and post-contrast $T_1$ sequences. All methods were evaluated under three distinct training-and-evaluation strategies, namely, standard, one-shot, and test-time-adaptation. The proposed PCA-Relax showed further improved performance of registration and mapping over well-established baselines. The proposed groupwise framework is generic and can be adapted to applications involving multiple images.