Learning diffusion MR from commercially available protocols: bringing advanced tractography into routine neurosurgical practice

Accurate localization of diffusion magnetic resonance imaging (dMRI) tractography can aid surgical planning by identifying white matter (WM) tracts that should be preserved to avoid functional deficits. However, there are still shortcomings that limit optimal tractography in a clinical setting. Firstly, signal modeling depends on acquisition parameters including signal-to-noise ratio (SNR), signal magnitude (b-values), and the minimum number of diffusion-weighting gradients (b-vecs) for high-quality local fiber orientation modeling. However, acquisition time is limited, and commercial scanners may not provide robust state-of-the-art dMRI acquisitions. Secondly, fiber tracking methods have intrinsic unresolved challenges in how to distinguish between different complex fiber configurations within a voxel, where axons can cross, kiss, bend, or fan out. Thirdly, there is variability in the fiber tracking algorithms with regards to tracts locations that usually are not taken into account. Most of the state-of-the-art tractography approaches do not estimate the uncertainty of WM pathways, and post-processing user-guided filtering is often performed to eliminate spurious streamlines. In this Ph.D. project, I aim to develop an end-to-end approach to ensure optimal tractography for clinically acquired dMRI and thereby provide more accurate guidance during neurosurgery. I will do that by improving the ability to recover complex local fiber orientations from commercial dMRI acquisitions and providing a tract uncertainty measure for preoperative guidance using deep learning. To achieve this I will: 1) Develop a convolutional neural network (CNN) approach to improve commercial
dMRI model fitting; 2) Compute tractography uncertainty quantification, 3) Incorporate tractography specific features to improve model fitting and validate pipeline on patients with gliomas.