Phase-Constrained Parallel Magnetic Resonance Imaging Reconstruction Based on Low-Rank Matrix Completion

被引：2
|
作者：
Jiang, Longyu [1 ,2 ,3 ]
He, Runguo [1 ]
Liu, Jie [1 ]
Chen, Yang [1 ,2 ,3 ]
Wu, Jiasong [1 ,2 ,3 ]
Shu, Huazhong [1 ,2 ,3 ]
Coatrieux, Jean-Louis [3 ,4 ]
机构：
[1] Southeast Univ, Lab Image Sci & Technol, Nanjing 210096, Jiangsu, Peoples R China
[2] Southeast Univ, Minist Educ, Key Lab Comp Network & Informat Integrat, Nanjing 210096, Jiangsu, Peoples R China
[3] Ctr Rech Informat Biomed Sino Francais, F-35000 Rennes, France
[4] Univ Rennes 1, Lab Traitement Signal & Image, F-35042 Rennes, France
来源：
IEEE ACCESS | 2018年 / 6卷
基金：
中国国家自然科学基金;
关键词：
Magnetic resonance imaging; low-rank; matrix completion; phase constraint; parallel imaging; K-SPACE NEIGHBORHOODS; THRESHOLDING ALGORITHM; FOURIER RECONSTRUCTION; MRI; ENHANCEMENT; ACQUISITION; ESPIRIT; LORAKS; GRAPPA; SENSE;
D O I：
10.1109/ACCESS.2017.2780921
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Low-rank matrix completion with phase constraints have been applied to the single-channel magnetic resonance imaging (MRI) reconstruction. In this paper, the reconstruction of sparse parallel imaging with smooth phase in each coil is formulated as the completion of a low-rank data matrix, which is modeled by the k-space neighborhoods and symmetric property of samples. The proposed algorithm is compared with a calibrationless parallel MRI reconstruction method based on both simulation data and real data. The experiment results show the proposed method has better performance in terms of MRI imaging enhancement, scanning time reduction, and denoising capability.
引用
收藏
页码：4941 / 4954
页数：14
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