Fast dual-tree wavelet composite splitting algorithms for compressed sensing MRI

Jianwu Li*, Jinpeng Zhou, Qiang Tu, Javaria Ikram, Zhengchao Dong

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

We presented new reconstruction algorithms for compressed sensing magnetic resonance imaging (CS-MRI) based on the combination of the fast composite splitting algorithm (FCSA) and complex dual-tree wavelet transform (DT-CWT) and on the combination of FCSA and double density dual-tree wavelet transform (DDDT-DWT), respectively. We applied the bivariate thresholding to these two combinations. The proposed methods not only inherit the effectiveness and fast convergence of FCSA but also improve the sparse representation of both point-like and curve-like features. Experimental results validate the effectiveness and efficiency of the proposed methods.

Original languageEnglish
Title of host publicationNeural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
EditorsKenji Doya, Kazushi Ikeda, Minho Lee, Akira Hirose, Seiichi Ozawa, Derong Liu
PublisherSpringer Verlag
Pages517-525
Number of pages9
ISBN (Print)9783319466866
DOIs
Publication statusPublished - 2016
Event23rd International Conference on Neural Information Processing, ICONIP 2016 - Kyoto, Japan
Duration: 16 Oct 201621 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9947 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Neural Information Processing, ICONIP 2016
Country/TerritoryJapan
CityKyoto
Period16/10/1621/10/16

Keywords

  • Composite splitting
  • Compressed sensing
  • Double density dual-tree wavelet
  • Dual-tree wavelet
  • MRI

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