Model Averaging and Bootstrap Consensus‐based Uncertainty Reduction in Diffusion MRI Tractography

Abstract

Diffusion magnetic resonance imaging (dMRI) tractography has the unique ability to reconstruct major white matter tracts non‐invasively and is, therefore, widely used in neurosurgical planning and neuroscience. In this work, we reduce two sources of uncertainty within the tractography pipeline. The first one is the model uncertainty that arises in crossing fibre tractography, from having to estimate the number of relevant fibre compartments in each voxel. We propose a mathematical framework to estimate model uncertainty, and we reduce this type of uncertainty with a model averaging approach that combines the fibre direction estimates from all candidate models, weighted by the posterior probability of the respective model. The second source of uncertainty is measurement noise. We use bootstrapping to estimate this data uncertainty, and consolidate the fibre direction estimates from all bootstraps into a consensus model. We observe that, in most voxels, a traditional model selection strategy selects different models across bootstraps. In this sense, the bootstrap consensus also reduces model uncertainty. Either approach significantly increases the accuracy of crossing fibre tractography in multiple subjects, and combining them provides an additional benefit. However, model averaging is much more efficient computationally.