Contrast-Agnostic Groupwise Registration by Robust PCA for Quantitative
  Cardiac MRI
Xinqi Li Yi Zhang Yidong Zhao Jan van Gemert Qian Tao
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.