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Abstract #1968

A Mask Guided Attention Generative Adversarial Network for Contrast-enhanced T1-weight MR Synthesis

Yajing Zhang1, Xiangyu Xiong1, and Chuanqi Sun1
1MR Clinical Science, Philips Healthcare, Suzhou, China


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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