Exploiting parameter sparsity in model-based reconstruction to accelerate proton density and T-2 mapping

Xi Peng; Xin Liu; Hairong Zheng; Dong Liang

doi:10.1016/j.medengphy.2014.06.002

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Exploiting parameter sparsity in model-based reconstruction to accelerate proton density and T-2 mapping

Journal article

Peer reviewed

Exploiting parameter sparsity in model-based reconstruction to accelerate proton density and T-2 mapping

Xi Peng, Xin Liu, Hairong Zheng and Dong Liang

Medical engineering & physics, Vol.36(11), pp.1428-1435

11/01/2014

DOI: 10.1016/j.medengphy.2014.06.002

PMID: 24998900

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Abstract

T-2 mapping is a powerful noninvasive technique providing quantitative biological information of the inherent tissue properties. However, its clinical usage is limited due to the relative long scanning time. This paper proposed a novel model-based method to address this problem. Typically, we directly estimated the relaxation values from undersampled k-space data by exploiting the sparse property of proton density and T-2 map in a penalized maximum likelihood formulation. An alternating minimization approach was presented to estimate the relaxation maps separately. Both numerical phantom and in vivo experiment dataset were used to demonstrate the performance of the proposed method. We showed that the proposed method outperformed the state-of-the-art techniques in terms of detail preservation and artifact suppression with various reduction factors and in both moderate and heavy noise circumstances. The superior reconstruction performance validated its promising potential in fast T-2 mapping applications. (C) 2014 IPEM. Published by Elsevier Ltd. All rights reserved.

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Engineering, Biomedical

Science & Technology

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Title: Subtitle: Exploiting parameter sparsity in model-based reconstruction to accelerate proton density and T-2 mapping
Creators: Xi Peng - Chinese Acad Sci, Shenzhen Inst Adv Technol, Paul C Lauterbur Res Ctr Biomed Imaging, Shenzhen, Guangdong, Peoples R China
Xin Liu - Shenzhen Institutes of Advanced Technology
Hairong Zheng - Shenzhen Institutes of Advanced Technology
Dong Liang - Shenzhen Institutes of Advanced Technology
Resource Type: Journal article
Publication Details: Medical engineering & physics, Vol.36(11), pp.1428-1435
Publisher: Elsevier
DOI: 10.1016/j.medengphy.2014.06.002
PMID: 24998900
ISSN: 1350-4533
eISSN: 1873-4030
Number of pages: 8
Language: English
Date published: 11/01/2014
Academic Unit: Radiology
Record Identifier: 9984446455202771

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