Efficient MR image reconstruction for compressed MR imaging

Junzhou Huang, Shaoting Zhang, Dimitris Metaxas

Research output: Contribution to journalArticlepeer-review

241 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)670-679
Number of pages10
JournalMedical Image Analysis
Volume15
Issue number5
DOIs
StatePublished - Oct 1 2011
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

Keywords

  • Compressive sensing
  • Convex optimization
  • MR image reconstruction

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