Adaptive sampling and wavelet tree based compressive sensing for MRI reconstruction

Qieshi Zhang, Jun Zhang, Sei Ichiro Kamata

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
出版社IEEE Computer Society
ページ2524-2528
ページ数5
ISBN(電子版)9781467399616
DOI
出版ステータス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

出版物シリーズ

名前Proceedings - International Conference on Image Processing, ICIP
2016-August
ISSN(印刷版)1522-4880

Other

Other23rd IEEE International Conference on Image Processing, ICIP 2016
国/地域United States
CityPhoenix
Period16/9/2516/9/28

ASJC Scopus subject areas

  • ソフトウェア
  • コンピュータ ビジョンおよびパターン認識
  • 信号処理

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