Using the model-based residual bootstrap to quantify uncertainty in fiber orientations from Q-ball analysis

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Biophysics

Background:

• Diffusion-weighted imaging (DWI) is crucial for mapping brain white matter architecture.
• Quantifying uncertainty in fiber orientation estimation is essential for robust analysis.
• Conventional bootstrapping is computationally intensive and requires repeated datasets.

Purpose of the Study:

• To implement and evaluate model-based residual bootstrapping with q-ball analysis for DWI data.
• To compare the performance of residual bootstrapping against conventional bootstrapping.
• To assess the utility of these bootstrapping methods for multifiber analysis and probabilistic tractography.

Main Methods:

• Implementation of model-based residual bootstrapping using q-ball analysis on a single DWI dataset.
• Comparison of results with conventional bootstrapping methods.
• Application of bootstrapping techniques for estimating fiber population probabilities and informing probabilistic tractography.

Main Results:

• Model-based residual bootstrap q-ball closely replicates the outputs of conventional bootstrapping.
• Both residual and conventional bootstrapping effectively estimate the probability of multiple fiber populations.
• The methods provide a viable input for probabilistic tractography, bypassing calibration and model selection issues.

Conclusions:

• Model-based residual bootstrapping offers an efficient and accurate alternative to conventional bootstrapping for DWI analysis.
• This technique enables robust uncertainty quantification in fiber orientation estimation.
• The approach facilitates improved probabilistic tractography by addressing existing limitations.