Keywords: Machine Learning/Artificial Intelligence, Prostate, RelaxometryThis work uses convolutional neural networks (CNNS) with two training strategies for estimating quantitative T2 values from prostate relaxometry measurements, and compares the results to conventional non-linear least squares fitting. The CNN trained with synthetic data in a supervised manner gave lower median errors and better noise robustness than either NLLS fitting or a CNN trained on in vivo data with a self-supervised loss.
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