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Real Time Dynamic MRI with Dynamic Total Variation

  • Chen Chen
  • Yeqing Li
  • Leon Axel
  • Junzhou Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

In this study, we propose a novel scheme for real time dynamic magnetic resonance imaging (dMRI) reconstruction. Different from previous methods, the reconstructions of the second frame to the last frame are independent in our scheme, which only require the first frame as the reference. Therefore, this scheme can be naturally implemented in parallel. After the first frame is reconstructed, all the later frames can be processed as soon as the k-space data is acquired. As an extension of the convention total variation, a new online model called dynamic total variation is used to exploit the sparsity on both spatial and temporal domains. In addition, we design an accelerated reweighted least squares algorithm to solve the challenging reconstruction problem. This algorithm is motivated by the special structure of partial Fourier transform in sparse MRI. The proposed method is compared with 4 state-of-the-art online and offline methods on in-vivo cardiac dMRI datasets. The results show that our method significantly outperforms previous online methods, and is comparable to the offline methods in terms of reconstruction accuracy.

Keywords

Dynamic Magnetic Resonance Imaging Preconditioned Conjugate Gradient Dictionary Learning Sampling Ratio Perfusion Sequence 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Chen Chen
    • 1
  • Yeqing Li
    • 1
  • Leon Axel
    • 2
  • Junzhou Huang
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of Texas at ArlingtonUSA
  2. 2.Department of RadiologyNew York UniversityNew YorkUSA

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