Journal article
Accelerated MRI Reconstruction With Separable and Enhanced Low-Rank Hankel Regularization
IEEE transactions on medical imaging, Vol.41(9), pp.2486-2498
09/01/2022
DOI: 10.1109/TMI.2022.3164472
PMID: 35377841
Abstract
Magnetic resonance imaging serves as an essential tool for clinical diagnosis, however, suffers from a long acquisition time. Sparse sampling effectively saves this time but images need to be faithfully reconstructed from undersampled data. Among the existing reconstruction methods, the structured low-rank methods have advantages in robustness to the sampling patterns and lower error. However, the structured low-rank methods use the 2D or higher dimension k-space data to build a huge block Hankel matrix, leading to considerable time and memory consumption. To reduce the size of the Hankel matrix, we proposed to separably construct multiple small Hankel matrices from rows and columns of the k-space and then constrain the low-rankness on these small matrices. This separable model can significantly reduce the computational time but ignores the correlation existed in inter- and intra-row or column, resulting in increased reconstruction error. To improve the reconstructed image without obviously increasing the computation, we further introduced the self-consistency of k-space and virtual coil prior. Besides, the proposed separable model can be extended into other imaging scenarios which hold exponential characteristics in the parameter dimension. The in vivo experimental results demonstrated that the proposed method permits the lowest reconstruction error with a fast reconstruction. The proposed approach requires only 4% of the state-of-the-art STDLR-SPIRiT runtime for parallel imaging reconstruction, and achieves the fastest computational speed in parameter imaging reconstruction.
Details
- Title: Subtitle
- Accelerated MRI Reconstruction With Separable and Enhanced Low-Rank Hankel Regularization
- Creators
- Xinlin Zhang - Xiamen UniversityHengfa Lu - The University of Texas at AustinDi Guo - Xiamen University of TechnologyZongying Lai - Jimei UniversityHuihui Ye - Zhejiang UniversityXi Peng - Department of Radiology, Mayo Clinic, Rochester, MN, USABo Zhao - The University of Texas at AustinXiaobo Qu - Xiamen University
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on medical imaging, Vol.41(9), pp.2486-2498
- DOI
- 10.1109/TMI.2022.3164472
- PMID
- 35377841
- NLM abbreviation
- IEEE Trans Med Imaging
- ISSN
- 0278-0062
- eISSN
- 1558-254X
- Publisher
- Institute of Electrical and Electronics Engineers
- Grant note
- 2021J011184 / Natural Science Foundation of Fujian Province of China (10.13039/501100003392) 2017YFC0108703 / National Key Research and Development Program of China (10.13039/501100012166) 61971361; 62122064; 61871341; 61901188 / National Natural Science Foundation of China (10.13039/501100001809) Xiamen University Nanqiang Outstanding Talents Program (10.13039/501100008865) 202006310142 / China Scholarship Council (10.13039/501100004543)
- Language
- English
- Date published
- 09/01/2022
- Academic Unit
- Radiology
- Record Identifier
- 9984446448202771
Metrics
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