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

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

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 exhibits robust reconstruction even when the auto-calibration signals are limited in parallel imaging. Overall the proposed method can be exploited to achieve better image quality for accelerated parallel magnetic resonance imaging. (C) 2020 Elsevier B.V. All rights reserved.

并行磁共振成像已成为一种有效且被广泛采用的加速数据采集技术。稀疏采样的出现提供了极大的加速，允许灵活采样和更好的重建。然而，从有限的数据中忠实地重建图像仍然是一项具有挑战性的任务。近期的低秩重建方法在提供高质量图像方面表现出色。然而，它们中没有一种利用常规获取的校准数据来提高并行磁共振成像中的图像质量。在这项工作中，提出了一种名为STDLR - SPIRiT的图像重建方法，以探索k空间数据中的双向同时低秩性（STDLR），并利用迭代自洽并行成像重建（SPIRiT）挖掘来自多个接收线圈的数据相关性。然后用一种无奇异值分解的数值算法解决重建问题。体模和脑成像数据的实验结果表明，所提出的方法在抑制伪影和实现最低误差方面优于现有技术。此外，即使在并行成像中自动校准信号有限的情况下，所提出的方法也能表现出稳健的重建能力。总体而言，所提出的方法可用于为加速的并行磁共振成像实现更好的图像质量。（C）2020爱思唯尔有限公司。保留所有权利。