Using the residual bootstrap to quantify uncertainty in mean apparent propagator MRI

摘要: Abstract Estimation of noise-induced variability in MAP-MRI is needed to properly characterize the amount uncertainty quantities derived from estimated coefficients. Bootstrap metrics, such as standard deviation, provides additional valuable diffusion information addition common parameters, and can be incorporated studies provide more extensive insight. To best our knowledge, this first paper study metrics. The noise have been quantified using residual bootstrap, which residuals are resampled two sampling schemes. bootstrap method empirical distributions for quantities, even when exact not easily derived. methods applied SPARC data HCP-MGH data, obtained zero-displacement probabilities. Here, we compare contrast schemes all shells within same shell. We show that resampling each shell generates larger than data. Standard deviation quartile coefficient maps provided.