Chai Yaqiong, Xu Botian, Zhang Kangning, Lepore Natasha, Wood John
Department of Biomedical Engineering, University of Southern California, CA, USA.
CIBORG lab, Department of Radiology, Children's Hospital Los Angeles, CA, USA.
IEEE Access. 2020;8:83858-83870. doi: 10.1109/access.2020.2992204. Epub 2020 May 13.
Magnetic resonance imaging (MRI) images acquired as multislice two-dimensional (2D) images present challenges when reformatted in orthogonal planes due to sparser sampling in the through-plane direction. Restoring the "missing" through-plane slices, or regions of an MRI image damaged by acquisition artifacts can be modeled as an image imputation task. In this work, we consider the damaged image data or missing through-plane slices as image masks and proposed an edge-guided generative adversarial network to restore brain MRI images. Inspired by the procedure of image inpainting, our proposed method decouples image repair into two stages: edge connection and contrast completion, both of which used general adversarial networks (GAN). We trained and tested on a dataset from the Human Connectome Project to test the application of our method for thick slice imputation, while we tested the artifact correction on clinical data and simulated datasets. Our Edge-Guided GAN had superior PSNR, SSIM, conspicuity and signal texture compared to traditional imputation tools, the Context Encoder and the Densely Connected Super Resolution Network with GAN (DCSRN-GAN). The proposed network may improve utilization of clinical 2D scans for 3D atlas generation and big-data comparative studies of brain morphometry.
作为多层二维（2D）图像采集的磁共振成像（MRI）图像，在正交平面中重新格式化时会面临挑战，因为在层面方向上采样较为稀疏。恢复“缺失”的层面切片或MRI图像中因采集伪影而受损的区域可被建模为图像插补任务。在这项工作中，我们将受损的图像数据或缺失的层面切片视为图像掩码，并提出了一种边缘引导生成对抗网络来恢复脑部MRI图像。受图像修复过程的启发，我们提出的方法将图像修复解耦为两个阶段：边缘连接和对比度完成，这两个阶段均使用通用对抗网络（GAN）。我们在人类连接组计划的数据集上进行训练和测试，以测试我们的方法在厚切片插补中的应用，同时我们在临床数据和模拟数据集上测试了伪影校正。与传统的插补工具、上下文编码器和带GAN的密集连接超分辨率网络（DCSRN-GAN）相比，我们的边缘引导GAN具有更高的峰值信噪比（PSNR）、结构相似性指数（SSIM）、显著度和信号纹理。所提出的网络可能会提高临床二维扫描在三维图谱生成和脑形态计量学大数据比较研究中的利用率。
