k-t CLAIR: Self-Consistency Guided Multi-Prior Learning for Dynamic Parallel MR Image Reconstruction

Code

• github.com/lpzhang/ktclair
  OfficialIn paperpytorch★ 11

Abstract

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.

Tasks

Reproductions