Efficient operator splitting algorithm for joint sparsity-regularized SPIRiT-based parallel MR imaging reconstruction

Self-consistent parallel imaging (SPIRiT) is an auto-calibrating model for the reconstruction of parallel magnetic resonance imaging, which can be formulated as a regularized SPIRiT problem. The Projection Over Convex Sets (POCS) method was used to solve the formulated regularized SPIRiT problem. Ho...

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Veröffentlicht in:Magnetic resonance imaging Jg. 46; S. 81 - 89
Hauptverfasser: Duan, Jizhong, Liu, Yu, Jing, Peiguang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Netherlands Elsevier Inc 01.02.2018
Schlagworte:
Algorithms
Auto-calibrating
Barzilai and Borwein method
Brain - diagnostic imaging
Calibration
Computer Simulation
FISTA
Humans
Image Processing, Computer-Assisted
Joint total variation
Linear Models
Magnetic Resonance Imaging
Operator splitting
Parallel magnetic resonance imaging
Self-consistent parallel imaging (SPIRiT)
Software
99-00
FISTA
Barzilai and Borwein method
00-01
Joint total variation
Auto-calibrating
Operator splitting
Parallel magnetic resonance imaging
Self-consistent parallel imaging (SPIRiT)
ISSN:0730-725X, 1873-5894, 1873-5894
Online-Zugang:Volltext
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Zusammenfassung:Self-consistent parallel imaging (SPIRiT) is an auto-calibrating model for the reconstruction of parallel magnetic resonance imaging, which can be formulated as a regularized SPIRiT problem. The Projection Over Convex Sets (POCS) method was used to solve the formulated regularized SPIRiT problem. However, the quality of the reconstructed image still needs to be improved. Though methods such as NonLinear Conjugate Gradients (NLCG) can achieve higher spatial resolution, these methods always demand very complex computation and converge slowly. In this paper, we propose a new algorithm to solve the formulated Cartesian SPIRiT problem with the JTV and JL1 regularization terms. The proposed algorithm uses the operator splitting (OS) technique to decompose the problem into a gradient problem and a denoising problem with two regularization terms, which is solved by our proposed split Bregman based denoising algorithm, and adopts the Barzilai and Borwein method to update step size. Simulation experiments on two in vivo data sets demonstrate that the proposed algorithm is 1.3 times faster than ADMM for datasets with 8 channels. Especially, our proposal is 2 times faster than ADMM for the dataset with 32 channels.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0730-725X
1873-5894
1873-5894
DOI:10.1016/j.mri.2017.10.013