Just how much data need to be collected for reliable bootstrap DT-MRI?

Area of Science:

• Medical Imaging
• Neuroscience
• Biophysics

Background:

• Diffusion Tensor MRI (DT-MRI) estimates water molecule diffusivity to map white matter fiber orientation.
• Noise in DT-MRI can lead to inaccuracies in fiber orientation estimation and tractography.
• The bootstrap method is being explored to quantify uncertainty in DT-MRI parameters, but optimal parameters are unclear.

Purpose of the Study:

• To investigate the accuracy and precision of the bootstrap method for characterizing DT-MRI parameter distributions.
• To determine the optimal number of repeat measurements and bootstraps for reliable DT-MRI analysis.
• To provide guidance on experimental designs for robust uncertainty quantification in DT-MRI.

Main Methods:

• Utilized an idealized system with known parameter variability.
• Evaluated various bootstrap designs and sampling schemes with different numbers of gradient vectors.
• Assessed the impact of repeat sampling and bootstrap iterations on parameter distribution accuracy.

Main Results:

• Demonstrated that approximately 500 bootstraps are generally sufficient for robust DT-MRI results.
• Indicated that a minimum of five repeat samples per diffusion-weighted intensity are recommended for bootstrapping.
• Showed that specific bootstrap designs and sampling schemes influence the precision of parameter distributions.

Conclusions:

• The bootstrap method can accurately characterize DT-MRI parameter distributions when appropriate experimental designs are used.
• Recommends a minimum of 500 bootstraps and five repeat samples for reliable uncertainty quantification in DT-MRI.
• Provides evidence-based guidelines for improving the precision and accuracy of white matter tractography.