Using the Wild Bootstrap to Quantify Uncertainty in Mean Apparent Propagator MRI

Published on June 12, 2019

Purpose: Estimation of uncertainty of MAP-MRI metrics
is an important topic, for several reasons. Bootstrap derived
uncertainty, such as the standard deviation, provides
valuable information, and can be incorporated in MAP-MRI
studies to provide more extensive insight.

Methods: In this paper, the uncertainty of different MAPMRI
metrics was quantified by estimating the empirical distributions
using the wild bootstrap. We applied the wild
bootstrap to both phantom data and human brain data, and
obtain empirical distributions for theMAP-MRImetrics returnto-
origin probability (RTOP), non-Gaussianity (NG) and propagator
anisotropy (PA).

Results: We demonstrated the impact of diffusion acquisition
scheme (number of shells and number of measurements
per shell) on the uncertainty of MAP-MRI metrics.
We demonstrated how the uncertainty of these metrics can
be used to improve group analyses, and to compare different
preprocessing pipelines. We demonstrated that with
uncertainty considered, the results for a group analysis can
be different.

Conclusion: Bootstrap derived uncertain measures provide
additional information to the MAP-MRI derived metrics, and
should be incorporated in ongoing and future MAP-MRI
studies to provide more extensive insight.

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