Sparse decomposition learning based dynamic MRI reconstruction

Peifei Zhu, Qieshi Zhang, Sei Ichiro Kamata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Dynamic MRI is widely used for many clinical exams but slow data acquisition becomes a serious problem. The application of Compressed Sensing (CS) demonstrated great potential to increase imaging speed. However, the performance of CS is largely depending on the sparsity of image sequence in the transform domain, where there are still a lot to be improved. In this work, the sparsity is exploited by proposed Sparse Decomposition Learning (SDL) algorithm, which is a combination of low-rank plus sparsity and Blind Compressed Sensing (BCS). With this decomposition, only sparsity component is modeled as a sparse linear combination of temporal basis functions. This enables coefficients to be sparser and remain more details of dynamic components comparing learning the whole images. A reconstruction is performed on the undersampled data where joint multicoil data consistency is enforced by combing Parallel Imaging (PI). The experimental results show the proposed methods decrease about 15∼20% of Mean Square Error (MSE) compared to other existing methods.

Original languageEnglish
Title of host publicationSeventh International Conference on Machine Vision, ICMV 2014
EditorsBranislav Vuksanovic, Jianhong Zhou, Antanas Verikas, Petia Radeva
PublisherSPIE
ISBN (Electronic)9781628415605
DOIs
Publication statusPublished - 2015 Jan 1
Event7th International Conference on Machine Vision, ICMV 2014 - Milan, Italy
Duration: 2014 Nov 192014 Nov 21

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume9445
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference7th International Conference on Machine Vision, ICMV 2014
CountryItaly
CityMilan
Period14/11/1914/11/21

Keywords

  • Compressed Sensing (CS)
  • Dynamic MRI
  • Parallel imaging (PI)
  • Sparse Decomposition Learning (SDL)

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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  • Cite this

    Zhu, P., Zhang, Q., & Kamata, S. I. (2015). Sparse decomposition learning based dynamic MRI reconstruction. In B. Vuksanovic, J. Zhou, A. Verikas, & P. Radeva (Eds.), Seventh International Conference on Machine Vision, ICMV 2014 [944516] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 9445). SPIE. https://doi.org/10.1117/12.2180534