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

Yutaro Iwamoto, Kyohei Takeda, Yinhao Li, Akihiko Shiino, Yen-Wei Chen
2020 arXiv   pre-print
Deep learning techniques have led to state-of-the-art single image super-resolution (SISR) with natural images. Pairs of high-resolution (HR) and low-resolution (LR) images are used to train the deep learning model (mapping function). These techniques have also been applied to medical image super-resolution (SR). Compared with natural images, medical images have several unique characteristics. First, there are no HR images for training in real clinical applications because of the limitations of
more » ... imaging systems and clinical requirements. Second, other modal HR images are available (e.g., HR T1-weighted images are available for enhancing LR T2-weighted images). In this paper, we propose an unsupervised SISR technique based on simple prior knowledge of the human anatomy; this technique does not require HR 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 LR images by referring to different HR 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.
arXiv:2008.11921v2 fatcat:uls762ztunclbln3xa5xeelv2u