Sparse decomposition learning based dynamic MRI reconstruction

Peifei Zhu, Qieshi Zhang, Sei Ichiro Kamata

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルSeventh International Conference on Machine Vision, ICMV 2014
編集者Branislav Vuksanovic, Jianhong Zhou, Antanas Verikas, Petia Radeva
出版社SPIE
ISBN(電子版)9781628415605
DOI
出版ステータスPublished - 2015 1 1
イベント7th International Conference on Machine Vision, ICMV 2014 - Milan, Italy
継続期間: 2014 11 192014 11 21

出版物シリーズ

名前Proceedings of SPIE - The International Society for Optical Engineering
9445
ISSN(印刷版)0277-786X
ISSN(電子版)1996-756X

Conference

Conference7th International Conference on Machine Vision, ICMV 2014
国/地域Italy
CityMilan
Period14/11/1914/11/21

ASJC Scopus subject areas

  • 電子材料、光学材料、および磁性材料
  • 凝縮系物理学
  • コンピュータ サイエンスの応用
  • 応用数学
  • 電子工学および電気工学

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