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Abstract #1041

OneforAll: Improving the Quality of Multi-contrast Synthesized MRI Using a Multi-Task Generative Model

Guanhua Wang1,2, Enhao Gong2,3, Suchandrima Banerjee4, Huijun Chen1, John Pauly2, and Greg Zaharchuk5

1Biomedical Engineering, Tsinghua University, Beijing, China, 2Electrical Engineering, Stanford University, Stanford, CA, United States, 3Subtle Medical, Menlo Park, CA, United States, 4Global Applied Science Laboratory, GE Healthcare, Menlo Park, CA, United States, 5Radiology, Stanford University, Stanford, CA, United States

To improve the quality and efficiency of multi-contrast MR neuroimaging, a new Multi-Task Generative Adversarial Network (GAN) is proposed to synthesize multiple contrasts using a uniformed network. The cohort of 104 subjects consisting of both healthy and pathological cases is used for training and evaluation. For both the subjective and non-subjective evaluation, the proposed method achieved improved diagnostic quality compared with state-of-the-art synthetic MRI image reconstruction methods based on model-fitting and also previously shown deep learning methods.

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