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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