  Deep learning techniques have led to state-of-the-art image super resolution
with natural images. Normally, pairs of high-resolution and low-resolution
images are used to train the deep learning models. These techniques have also
been applied to medical image super-resolution. The characteristics of medical
images differ significantly from natural images in several ways. First, it is
difficult to obtain high-resolution images for training in real clinical
applications due to the limitations of imaging systems and clinical
requirements. Second, other modal high-resolution images are available (e.g.,
high-resolution T1-weighted images are available for enhancing low-resolution
T2-weighted images). In this paper, we propose an unsupervised image
super-resolution technique based on simple prior knowledge of the human
anatomy. This technique does not require target T2WI high-resolution images for
training. Furthermore, we present a guided residual dense network, which
incorporates a residual dense network with a guided deep convolutional neural
network for enhancing the resolution of low-resolution images by referring to
different modal high-resolution images of the same subject. Experiments on a
publicly available brain MRI database showed that our proposed method achieves
better performance than the state-of-the-art methods.
