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Incorporating Reference in Parallel Imaging and Compressed Sensing
Journal article   Open access   Peer reviewed

Incorporating Reference in Parallel Imaging and Compressed Sensing

Xi Peng, Leslie Ying, Qiegen Liu, Yanjie Zhu, Yuanyuan Liu, Xiaobo Qu, Xin Liu, Hairong Zheng and Dong Liang
Magnetic resonance in medicine, Vol.73(4), pp.1490-1504
04/01/2015
DOI: 10.1002/mrm.25272
PMID: 24771404
url
https://doi.org/10.1002/mrm.25272View
Published (Version of record) Open Access

Abstract

PurposeTo develop a new compressed sensing parallel imaging technique called READ-PICS that can effectively incorporate prior information from a reference scan for MR image reconstruction from highly undersampled multichannel measurements. MethodsREAD-PICS incorporates information from a high-spatial-resolution reference prior using the generalized series model, to achieve increased image sparsity and mitigated noise amplification simultaneously. To further improve the ill-conditioning of the parallel imaging system, an annular area in the central residual k-space is used for calibration. Additionally, the mixed L1-L2 norm of the coefficients from the prior component and residual component is used to enforce joint sparsity. ResultsThe evaluations on parametric imaging and multiscan experiment demonstrate superior performance of READ-PICS in terms of detail preservation and noise suppression compared to state-of-the-art technique, L1-Iterative self-consistent parallel imaging reconstruction, and prescan required method, correlation imaging. ConclusionsThe proposed method can significantly increase signal sparsity and improve the ill-conditioning of the parallel imaging system using reference adaptive regularization. This technique can be easily adapted to other imaging applications where multiple images need to be acquired sequentially and a reference prior is also available. Magn Reson Med 73:1490-1504, 2015. (c) 2014 Wiley Periodicals, Inc.
Life Sciences & Biomedicine Radiology, Nuclear Medicine & Medical Imaging Science & Technology

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