Journal article
Undersampled MR Image Reconstruction with Data-Driven Tight Frame
Computational and mathematical methods in medicine, Vol.2015, pp.424087-10
01/01/2015
DOI: 10.1155/2015/424087
PMCID: PMC4495234
PMID: 26199641
Abstract
Undersampled magnetic resonance image reconstruction employing sparsity regularization has fascinated many researchers in recent years under the support of compressed sensing theory. Nevertheless, most existing sparsity-regularized reconstruction methods either lack adaptability to capture the structure information or suffer from high computational load. With the aim of further improving image reconstruction accuracy without introducing too much computation, this paper proposes a data-driven tight frame magnetic image reconstruction (DDTF-MRI) method. By taking advantage of the efficiency and effectiveness of data-driven tight frame, DDTF-MRI trains an adaptive tight frame to sparsify the to-be-reconstructed MR image. Furthermore, a two-level Bregman iteration algorithm has been developed to solve the proposed model. The proposed method has been compared to two state-of-the-art methods on four datasets and encouraging performances have been achieved by DDTF-MRI.
Details
- Title: Subtitle
- Undersampled MR Image Reconstruction with Data-Driven Tight Frame
- Creators
- Jianbo Liu - Chinese Acad Sci, Shenzhen Inst Adv Technol, Paul C Lauterbur Res Ctr Biomed Imaging, Shenzhen 518055, Peoples R ChinaShanshan Wang - Chinese Acad Sci, Shenzhen Inst Adv Technol, Paul C Lauterbur Res Ctr Biomed Imaging, Shenzhen 518055, Peoples R ChinaXi Peng - Chinese Acad Sci, Shenzhen Inst Adv Technol, Paul C Lauterbur Res Ctr Biomed Imaging, Shenzhen 518055, Peoples R ChinaDong Liang - Shenzhen Institutes of Advanced Technology
- Resource Type
- Journal article
- Publication Details
- Computational and mathematical methods in medicine, Vol.2015, pp.424087-10
- DOI
- 10.1155/2015/424087
- PMID
- 26199641
- PMCID
- PMC4495234
- NLM abbreviation
- Comput Math Methods Med
- ISSN
- 1748-670X
- eISSN
- 1748-6718
- Publisher
- Hindawi Publishing Group
- Number of pages
- 10
- Grant note
- JCYJ20140610152828678 / Shenzhen Basical Research Project 61102043; 11301508; 81120108012; 61471350; KQCX20120816155710259 / China NSFC; National Natural Science Foundation of China (NSFC)
- Language
- English
- Date published
- 01/01/2015
- Academic Unit
- Radiology
- Record Identifier
- 9984446553902771
Metrics
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