Contrast-agnostic groupwise registration by robust PCA for quantitative cardiac MRI

Contrast-agnostic groupwise registration by robust PCA for quantitative cardiac MRI

Li, Xinqi (TU Delft Electrical Engineering, Mathematics and Computer Science)

van Gemert, J.C. (mentor)
Tao, Q. (mentor)
Chen, Lydia Y. (graduation committee)

Delft University of Technology

Computer Science

2023-06-21

Quantitative cardiac MRI is an increasingly important diagnostic tool for cardiovascular diseases. Yet, it is essential to have correct image registration for good accuracy and precision of quantitative mapping. Registering all baseline images from a quantitative cardiac MRI sequence, however, is nontrivial because the patient is moving, leading to simultaneous changes in motion, intensity, and contrast. The changes in image contrast, in particular, make it challenging to design a reliable registration metric for optimization.
In this paper, we propose a novel approach based on robust principle component analysis (rPCA) that decomposes quantitative cardiac MRI into low-rank and sparse components, in combination with a groupwise CNN-based registration backbone. The proposed framework aims for fast, robust motion correction for contrast-agnostic sequences, which benefits registration. We evaluated our proposed method on cardiac T1 mapping sequences, both pre-contrast and post-contrast. Additionally, we synthesize the numerical phantoms with gold standard to test the performance. Our experiments showed that our method effectively improved registration performance over baseline methods without rPCA, and reduced quantitative mapping error in both in-domain and out-of-domain MRI sequences. The proposed rPCA framework is generic and can be easily incorporated into existing registration methods and other clinical applications.

Image Registration
Quantitative MRI
Deep Learning

http://resolver.tudelft.nl/uuid:ee4afb40-6403-4152-a299-8e379ccd4a3e

Student theses

master thesis

© 2023 Xinqi Li