Image synthesis methods based on deep learning has recently achieved success in reducing the dosage of gadolinium-based contrast agents (GBCAs). However, these methods cannot focus on the region of interest to synthesize realistic images. To address this issue, a mask guided attention generative adversarial network (MGA-GAN) was proposed to synthesize contrast enhanced T1-weight images from the multi-channel inputs. Qualitive and quantitative results indicate that the proposed MGA-GAN can improve the synthesized images with higher quality for details of brainstem glioma, compared with state-of-the-art methods.
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