Keywords: Other AI/ML, Brain, 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.
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