Adaptive sampling and wavelet tree based compressive sensing for MRI reconstruction

Qieshi Zhang, Jun Zhang, Seiichiro Kamata

研究成果: Conference contribution

1 引用 (Scopus)

抜粋

Magnetic Resonance Imaging (MRI) has been widely used in medical diagnose because of its non-invasive manner and excellent depiction of soft-tissue changes. Recently, the compressive sensing (CS) theory has been applied to reconstruct the MR image from highly down-sampled k-space data, which can reduce the scanning duration. To obtain useful information as much as possible with the same sampling rate, a weighted sampling strategy is studied. Moreover, based on the advantage of CS, a Wavelet tree based reconstruction approach is proposed. The experimental results demonstrate that the proposed method is preferable to other methods.

元の言語English
ホスト出版物のタイトル2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
出版者IEEE Computer Society
ページ2524-2528
ページ数5
2016-August
ISBN(電子版)9781467399616
DOI
出版物ステータスPublished - 2016 8 3
イベント23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States
継続期間: 2016 9 252016 9 28

Other

Other23rd IEEE International Conference on Image Processing, ICIP 2016
United States
Phoenix
期間16/9/2516/9/28

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • これを引用

    Zhang, Q., Zhang, J., & Kamata, S. (2016). Adaptive sampling and wavelet tree based compressive sensing for MRI reconstruction. : 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings (巻 2016-August, pp. 2524-2528). [7532814] IEEE Computer Society. https://doi.org/10.1109/ICIP.2016.7532814