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.

通过自一致性引导的多先验学习框架$k$-$t$ CLAIR，利用高度欠采样数据中的时空相关性来加速动态并行磁共振成像重建，$k$-$t$ CLAIR通过在迭代过程中学习$x$-$t$、$x$-$f$和$k$-$t$领域中的多个互补先验逐步重建可靠图像，实验结果表明$k$-$t$ CLAIR在心脏影像重建方面具有高质量的定量和定性性能。

$k$-$t$ CLAIR: 动态并行MR图像重构的自一致引导多先验学习