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.

翻译：心脏磁共振成像（CMR）已广泛应用于心脏疾病的临床诊断。然而，较长的采集时间限制了其在实时应用中的发展。本文提出了一种新颖的自一致性引导多先验学习框架，命名为$k$-$t$ CLAIR，旨在从高度欠采样数据中挖掘时空相关性，以加速动态并行MRI重建。$k$-$t$ CLAIR通过迭代方式利用在$x$-$t$域、$x$-$f$域和$k$-$t$域中学习到的多个互补先验，逐步重建高质量的图像，这是因为动态MRI表现出高度的时空冗余性。此外，$k$-$t$ CLAIR在先验学习中融合了校准信息，从而实现更一致的重建。在心脏电影和T1W/T2W图像上的实验结果表明，$k$-$t$ CLAIR在定量和定性性能方面均实现了高质量的动态MR重建。