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Abstract #2530

Quantitative MRI parameter estimation with supervised deep learning: MLE-derived labels outperform groundtruth labels

Sean Epstein1, Timothy J.P. Bray2, Margaret A. Hall-Craggs2, and Hui Zhang1
1Centre for Medical Image Computing, University College London, London, United Kingdom, 2University College London, London, United Kingdom


We propose a novel deep learning technique for quantitative MRI parameter estimation. Our method is trained to map noisy qMRI signals to conventional best-fit parameter labels, which act as proxies for the groundtruth parameters we wish to estimate. We show that this training leads to more accurate predictions of groundtruth model parameters than traditional approaches which train on these groundtruths directly. Furthermore, we show that our proposed method is both conceptually and empirically equivalent to existing unsupervisedapproaches, with the advantage of being formulated as supervised training.

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