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Deep learning techniques have led to state-of-the-art image super resolutionwith natural images. Normally, pairs of high-resolution and low-resolutionimages are used to train the deep learning models. These techniques have alsobeen applied to medical image super-resolution. The characteristics of medicalimages differ significantly from natural images in several ways. First, it isdifficult to obtain high-resolution images for training in real clinicalapplications due to the limitations of imaging systems and clinicalrequirements. Second, other modal high-resolution images are available (e.g.,high-resolution T1-weighted images are available for enhancing low-resolutionT2-weighted images). In this paper, we propose an unsupervised imagesuper-resolution technique based on simple prior knowledge of the humananatomy. This technique does not require target T2WI high-resolution images fortraining. Furthermore, we present a guided residual dense network, whichincorporates a residual dense network with a guided deep convolutional neuralnetwork for enhancing the resolution of low-resolution images by referring todifferent modal high-resolution images of the same subject. Experiments on apublicly available brain MRI database showed that our proposed method achievesbetter performance than the state-of-the-art methods.

Unsupervised MRI Super-Resolution Using Deep External Learning and Guided Residual Dense Network with Multimodal Image Priors