  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.
