$k$-$t$ CLAIR: Self-Consistency Guided Multi-Prior Learning for Dynamic Parallel MR Image Reconstruction
This work addresses the need for accelerated dynamic parallel MRI reconstruction in clinical practice for cardiac disease diagnosis, representing an incremental improvement through a novel multi-prior learning approach.
The paper tackles the problem of long acquisition times in cardiac magnetic resonance imaging (CMR) by proposing a self-consistency guided multi-prior learning framework, $k$-$t$ CLAIR, which achieves high-quality dynamic MR reconstruction from highly undersampled data, as demonstrated on cardiac cine and T1W/T2W images.
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.