CDGAN：Cross Datasets Generative Adversarial Network for MR multi-contrast Image Synthesis

Motivation: Multi-contrast MR images usually take a long time to scan, resulting in only a part of the valuable contrasts being obtained. Current deep learning methods face challenges when applied to domain adaptation across datasets or when tasked with generating high-quality images of various contrast. Goal(s): Our purpose is to synthesize diverse contrast MRIs across different datasets. Approach: we propose a cross-dataset generative adversarial network (CDGAN).The synthesized MR modalities of one specific object not only conform to the characteristics of the modalities themselves, but also have the same structure. Results: This method effectively addresses the issue of synthesizing across datasets. Impact: The method demonstrates a significant improvement in the quality of generated images when tested on different datasets.