Neuroscience research is rapidly expanding the scale and scope of MR imaging data collection, making it common to conduct studies involving thousands of participants. Diffusion MRI is a critical component in such efforts, as it provides a wide array of imaging metrics that reflect tissue microstructure and brain connectivity.
However, the robustness and scalability of computational methods for diffusion MRI are limiting factors in making neurobiological inferences in such large-cohort imaging studies.
In this talk, I will discuss some of our work to address these issues using the Quantitative ImagingToolkit, a robust software platform developed for MR image analysis, modeling and visualization.
I will share some of our recent methodological advances in microstructure imaging, which improve how we apply and interpret biophysically-based multi-compartment models.
I will also share our work on fiber bundle modeling for examining and quantitatively characterizing specific pathways of the brain.
Finally, Ill discuss some of the ongoing applications of these tools and how they can be applied in your own studies.