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Abstract #3580

Deep Inversion Net: A Novel Neural Network Architecture for Rapid, and Accurate T2 Relaxometry Inversion

Jeremy Kim1, Thanh Nguyen2, Pascal Spincemaille2, and Yi Wang2
1Hunter College High School, New York, NY, United States, 2Weill Cornell Medical College, New York, NY, United States

A novel deep neural network architecture, Deep Inversion Net, and a training scheme is proposed to accurately solve the multi-compartmental T2 relaxometry inverse problem for myelin water imaging in multiple sclerosis. Multiple neural networks communicate their outputs to regularize each other — thus better handling the ill-posed nature of this inverse problem. Results in simulated T2 relaxometry data and patients with demyelination show that Deep Inversion Net outperforms conventional optimization algorithms and other neural network architectures.

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