Multi-dimensional low rank plus sparse decomposition for reconstruction of under-sampled dynamic MRI

作者：

Highlights：

• A novel multi-dimensional analysis model is learnt to recover higher quality MRI sequences.

• The dynamic MRI reconstruction is formulated as a higher-dimensional low rank plus sparse tensor reconstruction problem.

• An efficient numerical algorithm based on ADMM is proposed to solve the optimization problem.

摘要

Highlights•A novel multi-dimensional analysis model is learnt to recover higher quality MRI sequences.•The dynamic MRI reconstruction is formulated as a higher-dimensional low rank plus sparse tensor reconstruction problem.•An efficient numerical algorithm based on ADMM is proposed to solve the optimization problem.

论文关键词：Low-rank and sparse tensor decomposition,Dynamic 3D MRI,Image reconstruction,Compressive sensing

论文评审过程：Received 30 January 2016, Revised 15 September 2016, Accepted 21 September 2016, Available online 10 October 2016, Version of Record 27 November 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.09.040