How Well Does the Residual Bootstrap Predict Scan-Rescan Repeatability of Spherical Deconvolution Diffusion MRI?

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acceleration achieved acquisitions additionally affect angular appealing applied array assessed assumes assuming attributed automated averaging balanced bars basis bootstrap bootstrapping brain certain channel coil comparing confidence cores corpus corrected correspondence cortical counted create dataset datasets deconvolution degree density designed despite determine determined diffusion distribution edges eight encoding estimating estimation evaluated even example expansion expected exploits fitting fractional function generate geometry goal good gradients greater harmonic head healthy hence include institute intervals investigate isotropic iterations likely little major many maps masks match medical motion mutual neurological noise often orientation orientations original parallel parker part partial pathways percent percentage permits pike positioning predict predicted prediction predicts probability processed processing profile profiles propagate quantifying reconstruction refocused registered registration repeatability repositioning rescan residual residuals resolution scanner separate series shot since slices slight smoothness span spherical spin spinal statistical subject synthetic systems tensor thalamus threshold tracking tract tracts trio twice uncertainty underestimates underestimation variability volume white