Contrast-Agnostic Groupwise Registration by Robust PCA for Quantitative Cardiac MRI

• URL: http://arxiv.org/abs/2311.01916v1
• Date: Fri, 3 Nov 2023 13:48:13 GMT
• Title: Contrast-Agnostic Groupwise Registration by Robust PCA for Quantitative Cardiac MRI
• Authors: Xinqi Li, Yi Zhang, Yidong Zhao, Jan van Gemert, Qian Tao
• Abstract summary: Co-registration of all baseline images within a quantitative cardiac MRI sequence is essential for the accuracy and precision of maps. We propose a novel motion correction framework that decomposes quantitative cardiac MRI into low-rank and sparse components. We show that our method effectively improved registration performance over baseline methods without introducing rPCA.
• Score: 15.778560241913674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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