Journal article
Sparse MRI reconstruction using multi-contrast image guided graph representation
Magnetic resonance imaging, Vol.43, pp.95-104
11/2017
DOI: 10.1016/j.mri.2017.07.009
PMID: 28734954
Abstract
Accelerating the imaging speed without sacrificing image structures plays an important role in magnetic resonance imaging. Under-sampling the k-space data and reconstructing the image with sparsity constraint is one efficient way to reduce the data acquisition time. However, achieving high acceleration factor is challenging since image structures may be lost or blurred when the acquired information is not sufficient. Therefore, incorporating extra knowledge to improve image reconstruction is expected for highly accelerated imaging. Fortunately, multi-contrast images in the same region of interest are usually acquired in magnetic resonance imaging protocols. In this work, we propose a new approach to reconstruct magnetic resonance images by learning the prior knowledge from these multi-contrast images with graph-based wavelet representations. We further formulate the reconstruction as a bi-level optimization problem to allow misalignment between these images. Experiments on realistic imaging datasets demonstrate that the proposed approach improves the image reconstruction significantly and is practical for real world application since patients are unnecessarily to stay still during successive reference image scans.
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•Multi-contrast guided graph representation is proposed to sparsify MRI images.•A bi-level programming framework is proposed for sparse MRI reconstruction.•The proposed method can significantly improve the reconstruction.•The proposed achieves lower reconstruction error than state-of-art methods.
Details
- Title: Subtitle
- Sparse MRI reconstruction using multi-contrast image guided graph representation
- Creators
- Zongying Lai - Xiamen UniversityXiaobo Qu - Xiamen UniversityHengfa Lu - Xiamen UniversityXi Peng - Shenzhen Institutes of Advanced TechnologyDi Guo - Xiamen University of TechnologyYu Yang - Xiamen UniversityGang Guo - Xiamen Chang Gung HospitalZhong Chen - Xiamen University
- Resource Type
- Journal article
- Publication Details
- Magnetic resonance imaging, Vol.43, pp.95-104
- Publisher
- Elsevier Inc
- DOI
- 10.1016/j.mri.2017.07.009
- PMID
- 28734954
- ISSN
- 0730-725X
- eISSN
- 1873-5894
- Grant note
- DOI: 10.13039/501100001809, name: National Natural Science Foundation of China, award: 61571380, 11375147, 61671441, 61672335, 61601276; DOI: 10.13039/501100012166, name: National Key Research and Development Program of China, award: 2017YFC0108703; DOI: 10.13039/501100003392, name: Natural Science Foundation of Fujian Province, award: 2015J01346, 2016J05205; name: Important Joint Research Project on Major Diseases of Xiamen City, award: 3502Z20149032; DOI: 10.13039/501100012226, name: Fundamental Research Funds for the Central Universities, award: 20720150109
- Language
- English
- Date published
- 11/2017
- Academic Unit
- Radiology
- Record Identifier
- 9984446068402771
Metrics
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