Spherical deconvolution (SD) of diffusion-weighted MRI (DW-MRI) is a popular analysis method that allows extraction of white matter (WM) fibre orientation information in the living human brain, completely noninvasively. It can be used to track the long-range connections of the brain or serve as a tract-specific biomarker for neuronal loss in the study of neurodegenerative diseases.
Recently, I proposed a new analysis method based on SD that models the presence of non-WM tissue in voxels, which was previously unaccounted for, enabling unprecedented tractography and quantification of WM. However, significant challenges remain, preventing SD from realizing its full potential:
* The current approach models the signal arising from the three macroscopic tissue types. With a new approach, I want to take this to the microscopic level, taking into account the presence of axons, cell bodies and extracellular water. This will improve current neuronal fibre estimates and will introduce new quantitative measures that can be used as biomarkers in the study of neurodegenerative diseases.
* Clinical scans are limited in spatial resolution due to constraints on scan time and signal-to-noise ratio (SNR). However, the very fine structures of the WM, and particularly the intricate folding patterns of the cortical surface, require high spatial resolution. I propose a new SD algorithm that can obtain high-resolution fibre information, with adequate SNR and within a practical acquisition time.