论文标题
通过深度神经网络模型对1.5T-3T MRI转换的比较研究
A Comparative Study on 1.5T-3T MRI Conversion through Deep Neural Network Models
论文作者
论文摘要
在本文中，我们探讨了许多深神经网络模型的功能，从而从临床1.5T MRI产生了全脑3T样MR图像。这些模型包括完全卷积网络（FCN）方法和三种最先进的超分辨率解决方案，ESPCN [26]，SRGAN [17]和PRSR [7]。 FCN解决方案U-Convert-NET通过类似U-NET的体系结构进行了1.5t-To-3T切片的映射，并通过多视图集合集成了3D邻域信息。模型的优缺点以及相关的评估指标是通过实验测量的，并深入讨论。据我们所知，这项研究是第一项评估全脑MRI转换的多种深度学习解决方案的工作，也是为此目的使用FCN/U-NET样结构的首次尝试。
In this paper, we explore the capabilities of a number of deep neural network models in generating whole-brain 3T-like MR images from clinical 1.5T MRIs. The models include a fully convolutional network (FCN) method and three state-of-the-art super-resolution solutions, ESPCN [26], SRGAN [17] and PRSR [7]. The FCN solution, U-Convert-Net, carries out mapping of 1.5T-to-3T slices through a U-Net-like architecture, with 3D neighborhood information integrated through a multi-view ensemble. The pros and cons of the models, as well the associated evaluation metrics, are measured with experiments and discussed in depth. To the best of our knowledge, this study is the first work to evaluate multiple deep learning solutions for whole-brain MRI conversion, as well as the first attempt to utilize FCN/U-Net-like structure for this purpose.
