Efficient MR Image Reconstruction for Compressed MR Imaging

  • Junzhou Huang
  • Shaoting Zhang
  • Dimitris Metaxas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6361)


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.


Conjugate Gradient Compressive Sensing Reconstruction Accuracy Regularization Problem Acceleration Step 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Junzhou Huang
    • 1
  • Shaoting Zhang
    • 1
  • Dimitris Metaxas
    • 1
  1. 1.Division of Computer and Information SciencesRutgers UniversityUSA

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