Jung Euijin, Chikontwe Philip, Zong Xiaopeng, Lin Weili, Shen Dinggang, Park Sang Hyun
Department of Robotics Engineering, DGIST, Daegu 42988, South Korea.
Biomedical Research Imaging Center, Department of Radiology, The University of North Carolina, Chapel Hill, NC 27599, USA.
IEEE Access. 2019;7:18382-18391. doi: 10.1109/ACCESS.2019.2896911. Epub 2019 Feb 1.
Perivascular spaces (PVS) in the human brain are related to various brain diseases. However, it is difficult to quantify them due to their thin and blurry appearance. In this paper, we introduce a deep-learning-based method, which can enhance a magnetic resonance (MR) image to better visualize the PVS. To accurately predict the enhanced image, we propose a very deep 3D convolutional neural network that contains densely connected networks with skip connections. The proposed networks can utilize rich contextual information derived from low-level to high-level features and effectively alleviate the gradient vanishing problem caused by the deep layers. The proposed method is evaluated on 17 7T MR images by a twofold cross-validation. The experiments show that our proposed network is much more effective to enhance the PVS than the previous PVS enhancement methods.
人脑的血管周围间隙（PVS）与多种脑部疾病相关。然而，由于其外观纤细且模糊，难以对其进行量化。在本文中，我们介绍了一种基于深度学习的方法，该方法可以增强磁共振（MR）图像，以便更好地可视化PVS。为了准确预测增强后的图像，我们提出了一种非常深的3D卷积神经网络，它包含具有跳跃连接的密集连接网络。所提出的网络可以利用从低级到高级特征派生的丰富上下文信息，并有效缓解由深层导致的梯度消失问题。通过双重交叉验证在17张7T MR图像上对所提出的方法进行了评估。实验表明，我们提出的网络在增强PVS方面比以前的PVS增强方法有效得多。
