A Self-Consistency Guided Multi-Prior Deep Learning Framework for Reconstruction of Fast Spatiotemporal MRI and Its Applications in Cardiac MRI

Motivation: Cardiac MRI (CMR) is widely used for assessment of cardiac diseases, but long acquisition time can cause patient discomfort and motion artifacts. Existing methods face challenges in reconstructing detailed information from highly undersampled spatiotemporal CMR acquisitions. Goal(s): We propose a self-consistency guided multi-prior deep learning framework termed $$$k$$$-$$$t$$$ CLAIR to address this challenge. Approach: This method exploits spatiotemporal correlations in data and incorporates calibration information to learn complementary priors across the $$$x$$$-$$$t$$$, $$$x$$$-$$$f$$$, and $$$k$$$-$$$t$$$ domains. Results: Evaluation performed on publicly available cardiac cine and T1/T2 mapping datasets demonstrated that the proposed method can effectively reconstruct detailed information from highly undersampled CMR data. Impact: The proposed method achieves high-quality reconstruction of highly undersampled CMR datasets including both cine imaging and T1/T2 mapping. This method has potential to improve CMR in clinical use.