Two-Layer Tight Frame Sparsifying Model for Compressed Sensing Magnetic Resonance Imaging

Shanshan Wang; Jianbo Liu; Xi Peng; Pei Dong; Qiegen Liu; Dong Liang

doi:10.1155/2016/2860643

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Two-Layer Tight Frame Sparsifying Model for Compressed Sensing Magnetic Resonance Imaging

Journal article

Open access

Peer reviewed

Two-Layer Tight Frame Sparsifying Model for Compressed Sensing Magnetic Resonance Imaging

Shanshan Wang, Jianbo Liu, Xi Peng, Pei Dong, Qiegen Liu and Dong Liang

BioMed research international, Vol.2016, pp.2860643-7

01/01/2016

DOI: 10.1155/2016/2860643

PMCID: PMC5056000

PMID: 27747226

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https://doi.org/10.1155/2016/2860643View

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Abstract

Compressed sensing magnetic resonance imaging (CSMRI) employs image sparsity to reconstruct MR images from incoherently undersampled K-space data. Existing CSMRI approaches have exploited analysis transform, synthesis dictionary, and their variants to trigger image sparsity. Nevertheless, the accuracy, efficiency, or acceleration rate of existing CSMRI methods can still be improved due to either lack of adaptability, high complexity of the training, or insufficient sparsity promotion. To properly balance the three factors, this paper proposes a two-layer tight frame sparsifying (TRIMS) model for CSMRI by sparsifying the image with a product of a fixed tight frame and an adaptively learned tight frame. The two-layer sparsifying and adaptive learning nature of TRIMS has enabled accurate MR reconstruction from highly undersampled data with efficiency. To solve the reconstruction problem, a three-level Bregman numerical algorithm is developed. The proposed approach has been compared to three state-of-the-art methods over scanned physical phantom and in vivo MR datasets and encouraging performances have been achieved.

Algorithms

Data Compression

Image Processing, Computer-Assisted

Magnetic Resonance Imaging - methods

Models, Theoretical

Signal-To-Noise Ratio

Time Factors

Details

Title: Subtitle: Two-Layer Tight Frame Sparsifying Model for Compressed Sensing Magnetic Resonance Imaging
Creators: Shanshan Wang
Jianbo Liu
Xi Peng
Pei Dong - The University of Sydney
Qiegen Liu - Nanchang University
Dong Liang - National Center for Mathematics and Interdisciplinary Sciences
Resource Type: Journal article
Publication Details: BioMed research international, Vol.2016, pp.2860643-7
DOI: 10.1155/2016/2860643
PMID: 27747226
PMCID: PMC5056000
NLM abbreviation: Biomed Res Int
ISSN: 2314-6133
eISSN: 2314-6141
Grant note: DOI: 10.13039/501100001809, name: National Natural Science Foundation of China, award: 61601450, 61471350, 11301508, 61671441, 61401449, 2015A020214019, 2015A030310314, 2015A030313740, JCYJ20160531183834938, JCYJ20140610152828678, JCYJ20150630114942318, JCYJ20140610151856736, 201403, 201313
Language: English
Date published: 01/01/2016
Academic Unit: Radiology
Record Identifier: 9984446449302771

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