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.

提出了基于鲁棒主成分分析的新型运动校正框架，将定量心脏核磁共振成像分解为低秩和稀疏成分，并在 rPCA 框架内集成了基于组内卷积神经网络的配准骨干。通过实验证明，该方法在提高配准性能和减少定量映射误差方面比基准方法更有效，且该 rPCA 框架具有通用性，可以与其他配准骨干集成。