Keywords: Signal Modeling, MicrostructureMicrostructure models are traditionally fitted via computationally expensive non-linear least squares. Recent model fitting techniques use supervised deep learning algorithms trained on synthetic datasets, however the training data distribution affects parameter estimates. Self-supervised learning can address this by extracting training labels directly from the input data. We introduce a self-supervised machine learning algorithm for fitting the VERDICT MRI model for prostate to diffusion-weighted MRI. On simulated data, our approach improves parameter estimation compared to non-linear least squares and supervised machine learning. We also reveal plausible tumour contrast on in-vivo prostate data.
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