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

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

Li, Xinqi (Student TU Delft)
Zhang, Y. (TU Delft ImPhys/Tao group)
Zhao, Y. (TU Delft ImPhys/Tao group)
van Gemert, J.C. (TU Delft Pattern Recognition and Bioinformatics)
Tao, Q. (TU Delft ImPhys/Tao group)

Camara, Oscar (editor)
Puyol-Antón, Esther (editor)
Suinesiaputra, Avan (editor)
Young, Alistair (editor)
Sermesant, Maxime (editor)
Tao, Qian (editor)
Wang, Chengyan (editor)

2024

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.

Groupwise registration
motion correction
Quantitative MRI
Robust PCA

http://resolver.tudelft.nl/uuid:7df8dacb-3cff-474c-b1bc-cfed9f6ef3bb

https://doi.org/10.1007/978-3-031-52448-6_8

Springer

2024-08-02

978-3-031-52447-9

Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers - 14th International Workshop, STACOM 2023, Held in Conjunction with MICCAI 2023, Revised Selected Papers

14th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2023 held in Conjunction with MICCAI 2023, 2023-10-12, Vancouver, Canada

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 0302-9743, 14507 LNCS

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2024 Xinqi Li, Y. Zhang, Y. Zhao, J.C. van Gemert, Q. Tao