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Image reconstruction with low-rankness and self-consistency of k-space data in parallel MRI by Xinlin Zhang; Di Guo; Yiman Huang; Ying Chen; Liansheng Wang; Feng Huang; Qin Xu; Xiaobo Qu is a Medicine article available to read on EtoBox.

What is Image reconstruction with low-rankness and self-consistency of k-space data in parallel MRI about?

Parallel magnetic resonance imaging has served as an effective and widely adopted technique for accelerating data collection. The advent of sparse sampling offers aggressive acceleration, allowing flexible sampling and better reconstruction. Nevertheless, faithfully reconstructing the image from limited data still poses a challenging task. Recent low-rank reconstruction methods are superior in providing high-quality images. Nevertheless, none of them employ the routinely acquired calibration data to improve image quality in parallel magnetic resonance imaging. In this work, an image reconstruction approach named STDLR-SPIRiT is proposed to explore the simultaneous two-directional low-rankness (STDLR) in the k-space data and to mine the data correlation from multiple receiver coils with the iterative self-consistent parallel imaging reconstruction (SPIRiT). The reconstruction problem is then solved with a singular value decomposition-free numerical algorithm. Experimental results of phantom and brain imaging data show that the proposed method outperforms the state-of-the-art methods in terms of suppressing artifacts and achieving the lowest error. Moreover, the proposed method exhib

Who reads Image reconstruction with low-rankness and self-consistency of k-space data in parallel MRI?

Author

Xinlin Zhang; Di Guo; Yiman Huang; Ying Chen; Liansheng Wang; Feng Huang; Qin Xu; Xiaobo Qu

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 1361-8415)

Published

2020

Language

EN

Field

Medicine (Physical Sciences)