Quantitative cardiac magnetic resonance imaging (MRI) is an increasingly important diagnostic tool for cardiovascular diseases. Yet, co-registration of all baseline images within the quantitative MRI sequence is essential for the accuracy and precision of quantitative maps. However, co-registering all baseline images from a quantitative cardiac MRI sequence remains a nontrivial task because of the simultaneous changes in intensity and contrast, in combination with cardiac and respiratory motion. To address the challenge, we propose a novel motion correction framework based on robust principle component analysis (rPCA) that decomposes quantitative cardiac MRI into low-rank and sparse components, and we integrate the groupwise CNN-based registration backbone within the rPCA framework. The low-rank component of rPCA corresponds to the quantitative mapping (i.e. limited degree of freedom in variation), while the sparse component corresponds to the residual motion, making it easier to formulate and solve the groupwise registration problem. We evaluated our proposed method on cardiac T1 mapping by the modified Look-Locker inversion recovery (MOLLI) sequence, both before and after the Gadolinium contrast agent administration. Our experiments showed that our method effectively improved registration performance over baseline methods without introducing rPCA, and reduced quantitative mapping error in both in-domain (pre-contrast MOLLI) and out-of-domain (post-contrast MOLLI) inference. The proposed rPCA framework is generic and can be integrated with other registration backbones.

翻译：定量心脏磁共振成像（MRI）已成为心血管疾病日益重要的诊断工具。然而，对定量MRI序列中所有基线图像进行协同配准，是确保定量图准确性和精确度的关键。但由于图像强度与对比度同时变化，加之心脏和呼吸运动的影响，对定量心脏MRI序列中所有基线图像进行协同配准仍是一项艰巨任务。针对这一挑战，我们提出了一种基于鲁棒主成分分析（rPCA）的新型运动校正框架，该框架将定量心脏MRI分解为低秩分量和稀疏分量，并在rPCA框架内集成了基于卷积神经网络（CNN）的群组配准骨干网络。其中，rPCA的低秩分量对应定量映射（即变化自由度有限），而稀疏分量对应残余运动，使得群组配准问题的构建与求解更为简便。我们采用改良Look-Locker反转恢复（MOLLI）序列，在钆对比剂注射前后对心脏T1 mapping进行了方法评估。实验表明，与未引入rPCA的基线方法相比，本方法有效提升了配准性能，并在域内（注射前MOLLI）和域外（注射后MOLLI）推理中均降低了定量映射误差。所提出的rPCA框架具有通用性，可与其他配准骨干网络集成。