Abstract
Cardiac magnetic resonance imaging (CMR) has been widely used in clinical practice for the medical diagnosis of cardiac diseases. However, the long acquisition time hinders its development in real-time applications. Here, we propose a novel self-consistency guided multi-prior learning framework named k-t CLAIR to exploit spatiotemporal correlations from highly undersampled data for accelerated dynamic parallel MRI reconstruction. The k-t CLAIR progressively reconstructs faithful images by leveraging multiple complementary priors learned in the x-t, x-f, and k-t domains in an iterative fashion, as dynamic MRI exhibits high spatiotemporal redundancy. Additionally, k-t CLAIR incorporates calibration information for prior learning, resulting in a more consistent reconstruction. Experimental results on cardiac cine and T1W/T2W images demonstrate that k-t CLAIR achieves high-quality dynamic MR reconstruction in terms of both quantitative and qualitative performance.
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Acknowledgements
This work was supported by a grant from Innovation and Technology Commission of the Hong Kong SAR (MRP/046/20X); and by a Faculty Innovation Award from the Faculty of Medicine of The Chinese University of Hong Kong.
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Zhang, L., Chen, W. (2024). k-t CLAIR: Self-consistency Guided Multi-prior Learning for Dynamic Parallel MR Image Reconstruction. In: Camara, O., et al. Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers. STACOM 2023. Lecture Notes in Computer Science, vol 14507. Springer, Cham. https://doi.org/10.1007/978-3-031-52448-6_30
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