Tian Ye, Lim Yongwan, Zhao Ziwei, Byrd Dani, Narayanan Shrikanth, Nayak Krishna S
Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA.
Department of Linguistics, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, California, USA.
Magn Reson Med. 2021 Aug;86(2):916-925. doi: 10.1002/mrm.28746. Epub 2021 Mar 16.
To mitigate a common artifact in spiral real-time MRI, caused by aliasing of signal outside the desired FOV. This artifact frequently occurs in midsagittal speech real-time MRI.
Simulations were performed to determine the likely origin of the artifact. Two methods to mitigate the artifact are proposed. The first approach, denoted as "large FOV" (LF), keeps an FOV that is large enough to include the artifact signal source during reconstruction. The second approach, denoted as "estimation-subtraction" (ES), estimates the artifact signal source before subtracting a synthetic signal representing that source in multicoil k-space raw data. Twenty-five midsagittal speech-production real-time MRI data sets were used to evaluate both of the proposed methods. Reconstructions without and with corrections were evaluated by two expert readers using a 5-level Likert scale assessing artifact severity. Reconstruction time was also compared.
The origin of the artifact was found to be a combination of gradient nonlinearity and imperfect anti-aliasing in spiral sampling. The LF and ES methods were both able to substantially reduce the artifact, with an averaged qualitative score improvement of 1.25 and 1.35 Likert levels for LF correction and ES correction, respectively. Average reconstruction time without correction, with LF correction, and with ES correction were 160.69 ± 1.56, 526.43 ± 5.17, and 171.47 ± 1.71 ms/frame.
Both proposed methods were able to reduce the spiral aliasing artifacts, with the ES-reduction method being more effective and more time efficient.
减轻螺旋实时磁共振成像（MRI）中一种常见的伪影，该伪影由期望视野（FOV）外信号的混叠引起。这种伪影在矢状位语音实时MRI中经常出现。
进行模拟以确定伪影的可能来源。提出了两种减轻伪影的方法。第一种方法，记为“大视野”（LF），在重建过程中保持足够大的视野以包含伪影信号源。第二种方法，记为“估计-减法”（ES），在多线圈k空间原始数据中减去代表该源的合成信号之前，先估计伪影信号源。使用25个矢状位语音产生实时MRI数据集来评估这两种提出的方法。由两位专家读者使用5级李克特量表评估有无校正的重建的伪影严重程度。还比较了重建时间。
发现伪影的来源是梯度非线性和螺旋采样中不完善的抗混叠的组合。LF和ES方法都能够大幅减少伪影，LF校正和ES校正的平均定性评分分别提高了1.25和1.35个李克特等级。无校正、LF校正和ES校正的平均重建时间分别为160.69±1.56、526.43±5.17和171.47±1.71毫秒/帧。
两种提出的方法都能够减少螺旋混叠伪影，其中ES减少方法更有效且更省时。
