  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.
