Quantitative MRI (qMRI) is an increasingly important tool for clinical
assessment of cardiovascular diseases. Quantitative maps are derived by fitting
a known signal model to a series of baseline images, while the quality of the
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.

本文提出了一种基于深度学习的组内注册框架，名为 “PCA-Relax”，用于纠正心脏磁共振图像中的呼吸和心动运动，实现定量 MRI 的精确测量和评估。