Journal article
JSENSE-Pro: Joint sensitivity estimation and image reconstruction in parallel imaging using pre-learned subspaces of coil sensitivity functions
Magnetic resonance in medicine, Vol.89(4), pp.1531-1542
04/2023
DOI: 10.1002/mrm.29548
PMID: 36480000
Abstract
To improve calibrationless parallel imaging using pre-learned subspaces of coil sensitivity functions.
A subspace-based joint sensitivity estimation and image reconstruction method was developed for improved parallel imaging with no calibration data. Specifically, we proposed to use a probabilistic subspace model to capture prior information of the coil sensitivity functions from previous scans acquired using the same receiver system. Both the subspace basis and coefficient distributions were learned from a small set of training data. The learned subspace model was then incorporated into the regularized reconstruction formalism that includes a sparsity prior. The nonlinear optimization problem was solved using alternating minimization algorithm. Public fastMRI brain dataset was retrospectively undersampled by different schemes for performance evaluation of the proposed method.
With no calibration data, the proposed method consistently produced the most accurate coil sensitivity estimation and highest quality image reconstructions at all acceleration factors tested in comparison with state-of-the-art methods including JSENSE, DeepSENSE, P-LORAKS, and Sparse BLIP. Our results are comparable to or even better than those from SparseSENSE, which used calibration data for sensitivity estimation. The work also demonstrated that the probabilistic subspace model learned from T
w data can be generalized to aiding the reconstruction of FLAIR data acquired from the same receiver system.
A subspace-based method named JSENSE-Pro has been proposed for accelerated parallel imaging without the acquisition of companion calibration data. The method is expected to further enhance the practical utility of parallel imaging, especially in applications where calibration data acquisition is not desirable or limited.
Details
- Title: Subtitle
- JSENSE-Pro: Joint sensitivity estimation and image reconstruction in parallel imaging using pre-learned subspaces of coil sensitivity functions
- Creators
- Lihong Tang - Shanghai Jiao Tong UniversityYibo Zhao - University of Illinois Urbana-ChampaignYudu Li - University of Illinois Urbana-ChampaignRong Guo - University of Illinois Urbana-ChampaignBingyang Cai - Shanghai Jiao Tong UniversityJia Wang - Shanghai Jiao Tong UniversityYao Li - Shanghai Jiao Tong UniversityZhi-Pei Liang - University of Illinois Urbana-ChampaignXi Peng - Department of Radiology, Mayo Clinic, Rochester, Minnesota, USAJie Luo - Shanghai Jiao Tong University
- Resource Type
- Journal article
- Publication Details
- Magnetic resonance in medicine, Vol.89(4), pp.1531-1542
- DOI
- 10.1002/mrm.29548
- PMID
- 36480000
- ISSN
- 0740-3194
- eISSN
- 1522-2594
- Grant note
- DOI: 10.13039/501100001809, name: National Natural Science Foundation of China, award: 6210132
- Language
- English
- Date published
- 04/2023
- Academic Unit
- Radiology
- Record Identifier
- 9984446536102771
Metrics
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