Efficient MR Image Reconstruction for Compressed MR Imaging

摘要：

In this paper, we propose an efficient algorithm for MR image reconstruction. The algorithm minimizes a linear combination of three terms corresponding to a least square data fitting, total variation (TV) and L1 norm regularization. This has been shown to be very powerful for the MR image reconstruction. First, we decompose the original problem into L1 and TV norm regularization subproblems respectively. Then, these two subproblems are efficiently solved by existing techniques. Finally, the reconstructed image is obtained from the weighted average of solutions from two subproblems in an iterative framework. We compare the proposed algorithm with previous methods in term of the reconstruction accuracy and computation complexity. Numerous experiments demonstrate the superior performance of the proposed algorithm for compressed MR image reconstruction.

作者: Junzhou Huang Shaoting Zhang Dimitris Metaxas

作者单位: Division of Computer and Information Sciences,Rutgers University,NJ, USA 08854 Division of Computer and Information Sciences, Rutgers University, NJ, USA 08854c

会议类型: 国际会议

会议名称: The 13th International Conference on Medical Image Computing and Computer-Assisted Intervention(第13届医学影像计算与计算机辅助介入国际会议 MICCAI 2010)

会议地点: 北京

会议语种:英文

页码: 135-142

在线出版日期: 2010-09-01（万方平台首次上网日期，不代表论文的发表时间）