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Abstract #0397

How do we know we measure tissue parameters, not the prior?

Santiago Coelho1, Els Fieremans1, and Dmitry S. Novikov1
1Center for Biomedical Imaging and Center for Advanced Imaging Innovation and Research (CAI$^2$R), Department of Radiology, New York University School of Medicine, New York, NY, United States

In the machine-learning (ML) era, we are transitioning from max-likelihood parameter estimation to learning the mapping from measurements to model parameters. While such maps look smooth, there is danger of them becoming too smooth: At low SNR, ML estimates become the mean of the training set. Here we derive fit quality (MSE) as function of SNR, and show that MSE for various ML methods (regression, neural-nets, random forest) approaches a universal curve interpolating between Cramér-Rao bound at high SNR, and variance of the prior at low SNR. Theory is validated numerically and on white matter Standard Model in vivo.

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