Department of Electrical and Electronic Engineering, Yonsei University, Seodaemun-gu, Seoul 120-749, Republic of Korea.
Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 065-591, Republic of Korea.
Magn Reson Imaging. 2019 Dec;64:13-20. doi: 10.1016/j.mri.2019.04.002. Epub 2019 Apr 4.
For quantitative neuroimaging studies using multi-echo gradient echo (mGRE) images, additional T-weighted magnetization prepared rapid gradient echo (MPRAGE) images are often acquired to supplement the insufficient morphometric information of mGRE for tissue segmentation which require lengthened scan time and additional processing such as image registration. This study investigated the feasibility of generating synthetic MPRAGE images from mGRE images using a deep convolutional neural network. Tissue segmentation results derived from the synthetic MPRAGE showed good agreement with those from actual MPRAGE (DSC = 0.882 ± 0.017). There was no statistically significant difference between the mean susceptibility values obtained with the regions of interest from synthetic and actual MPRAGEs and high correlation between the two measurements.
对于使用多回波梯度回波（mGRE）图像的定量神经影像学研究，通常会额外采集 T1 加权磁化准备快速梯度回波（MPRAGE）图像，以补充 mGRE 对组织分割的形态计量信息不足，这需要延长扫描时间并进行额外的处理，例如图像配准。本研究探讨了使用深度卷积神经网络从 mGRE 图像生成合成 MPRAGE 图像的可行性。从合成 MPRAGE 中得出的组织分割结果与实际 MPRAGE 的结果具有很好的一致性（DSC=0.882±0.017）。从合成和实际 MPRAGE 的感兴趣区域获得的平均磁化率值之间没有统计学上的显著差异，并且这两种测量之间具有高度相关性。
