Training data distribution significantly impacts the estimation of tissue microstructure with machine learning

Supervised machine learning (ML) provides a compelling alternative to traditional model fitting for parameter mapping in quantitative MRI. The aim of this work is to demonstrate and quantify the effect of different training data distributions on the accuracy and precision of parameter estimates when supervised ML is used for fitting.We fit a two- and three-compartment biophysical model to diffusion measurements from in-vivo human brain, as well...