Department of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, South Korea.
GE Healthcare, Seoul, South Korea.
Neuroimage. 2022 Oct 1;259:119411. doi: 10.1016/j.neuroimage.2022.119411. Epub 2022 Jun 23.
Magnetic Resonance Imaging (MRI) is sensitive to motion caused by patient movement due to the relatively long data acquisition time. This could cause severe degradation of image quality and therefore affect the overall diagnosis. In this paper, we develop an efficient retrospective 2D deep learning method called stacked U-Nets with self-assisted priors to address the problem of rigid motion artifacts in 3D brain MRI. The proposed work exploits the usage of additional knowledge priors from the corrupted images themselves without the need for additional contrast data. The proposed network learns the missed structural details through sharing auxiliary information from the contiguous slices of the same distorted subject. We further design a refinement stacked U-Nets that facilitates preserving the spatial image details and improves the pixel-to-pixel dependency. To perform network training, simulation of MRI motion artifacts is inevitable. The proposed network is optimized by minimizing the loss of structural similarity (SSIM) using the synthesized motion-corrupted images from 83 real motion-free subjects. We present an intensive analysis using various types of image priors: the proposed self-assisted priors and priors from other image contrast of the same subject. The experimental analysis proves the effectiveness and feasibility of our self-assisted priors since it does not require any further data scans. The overall image quality of the motion-corrected images via the proposed motion correction network significantly improves SSIM from 71.66% to 95.03% and declines the mean square error from 99.25 to 29.76. These results indicate the high similarity of the brain's anatomical structure in the corrected images compared to the motion-free data. The motion-corrected results of both the simulated and real motion data showed the potential of the proposed motion correction network to be feasible and applicable in clinical practices.
磁共振成像（MRI）对由于数据采集时间相对较长而导致的患者运动引起的运动非常敏感。这可能会导致图像质量严重下降，从而影响整体诊断。在本文中，我们开发了一种高效的回顾性 2D 深度学习方法，称为堆叠 U-Nets 与自辅助先验，以解决 3D 脑 MRI 中的刚性运动伪影问题。所提出的工作利用了从受污染图像本身中获取的额外知识先验，而无需额外的对比数据。该网络通过共享同一扭曲对象的连续切片的辅助信息来学习丢失的结构细节。我们进一步设计了细化堆叠 U-Nets，以方便保留空间图像细节并提高像素到像素的依赖性。为了进行网络训练，模拟 MRI 运动伪影是不可避免的。所提出的网络通过使用从 83 个无运动的真实对象合成的运动伪影图像，通过最小化结构相似性（SSIM）的损失来进行优化。我们使用各种类型的图像先验（包括我们提出的自辅助先验和同一对象的其他图像对比度的先验）进行了深入分析。实验分析证明了我们的自辅助先验的有效性和可行性，因为它不需要任何进一步的数据扫描。通过所提出的运动校正网络校正后的运动图像的整体图像质量显著提高了 SSIM，从 71.66%提高到 95.03%，并降低了均方误差，从 99.25 降低到 29.76。这些结果表明，与无运动数据相比，校正后的图像中大脑解剖结构的相似性很高。模拟和真实运动数据的运动校正结果表明，所提出的运动校正网络具有可行性和适用于临床实践的潜力。
