Generative adversarial networks with adaptive normalization for synthesizing T2-weighted magnetic resonance images from diffusion-weighted images - Mao, Yanyan; Chen, Chao; Wang, Zhenjie; Cheng, Dapeng; You, Panlu; Huang, Xingdan; Zhang, Baosheng; Zhao, Feng (Frontiers in Neuroscience, 2022)
Recently, more and more attention is drawn to brain imaging technology in medical field. Among them, MRI plays a vital role in clinical diagnosis and lesion analysis of brain diseases. Different sequences of MR images provide more comprehensive information and jointly help doctors to make accurate clinical diagnoses, however, its costs are particular. Image-to-image synthesis in medical field is generally divided into supervised learning-based methods and unsupervised learning-based methods. 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 DWI MR images. Different from the existing methods, the deep semantic information is extracted from the high-frequency information of the original sequence images, 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 generative adversarial networks. Then we introduce adaptive normalization, a conditional normalization layer that modulates the activations using the fused feature map. Experimental results show that our method in synthesizing T2 images has better perceptual quality and greater details than other state-of-the-art methods.