Generative adversarial networks with adaptive normalization for synthesizing T2-weighted magnetic resonance images from diffusion-weighted images.

Recently, attention has been drawn toward brain imaging technology in the medical field, among which MRI plays a vital role in clinical diagnosis and lesion analysis of brain diseases. Different sequences of MR images provide more comprehensive information and help doctors to make accurate clinical diagnoses. However, their costs are particularly high. For many image-to-image synthesis methods in the medical field, supervised learning-based methods require labeled datasets, which are often difficult to obtain. Therefore, we propose an unsupervised learning-based generative adversarial network with adaptive normalization (AN-GAN) for synthesizing T2-weighted MR images from rapidly scanned diffusion-weighted imaging (DWI) MR images. In contrast to the existing methods, deep semantic information is extracted from the high-frequency information of original sequence images, which are then added to the feature map in deconvolution layers as a modality mask vector. This image fusion operation results in better feature maps and guides the training of GANs. Furthermore, to better preserve semantic information against common normalization layers, we introduce AN, a conditional normalization layer that modulates the activations using the fused feature map. Experimental results show that our method of synthesizing T2 images has a better perceptual quality and better detail than the other state-of-the-art methods.