Abstract #1041

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

Guanhua Wang^1,2, Enhao Gong^2,3, 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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