Research team

Vision lab

Expertise

My work is focused on extracting robust and clinically relevant information from quantitative MRI data acquired within realistic scan times. The novel acquisition and analysis techniques that I develop are applicable across a wide range of clinical and neuroscientific problems where white matter and its connectivity are of interest, including Alzheimer's disease, brain development and neurosurgical planning.

Spherical deconvolution of high-dimensional diffusion MRI for improved microstructural imaging of the brain. 01/10/2018 - 30/09/2021

Abstract

Multi-tissue spherical deconvolution of diffusion MRI (dMRI) is a popular analysis method that provides the full white matter fiber orientation density function as well as the densities of cerebrospinal fluid and grey matter tissue in the living human brain, completely noninvasively. It can be used to track the long-range connections of the brain and provides a tract-specific biomarker for neuronal loss in the study of neurodegenerative diseases. Currently, the technique can be regarded as a macroscopic approach: it breaks up the dMRI voxels in terms of tissues rather than cellular components, the latter being potentially more relevant biomarkers. Unfortunately, recent studies have demonstrated that conventional low-dimensional dMRI scans lack the information to resolve these microstructural features. In this proposal, I will take multi-tissue spherical deconvolution to the next (microscopic) level by leveraging high-dimensional dMRI scans. These next-generation scans have shown great promise to disentangle different microstructural compartments. The new multi-compartment spherical deconvolution approach will allow simultaneous estimation of a high quality axonal orientation density function as well as the densities of cell bodies and extracellular space. This will enable high-quality fiber tracking and at the same time provide more relevant biomarkers, and will help spherical deconvolution to maintain its position as one of the go-to tools for dMRI analysis.

Researcher(s)

Research team(s)

Mapping the white matter fiber connections in the brain using diffusion-weighted MRI. 17/11/2015 - 31/12/2016

Abstract

Diffusion Weighted (DW) MRI is a unique and noninvasive method to characterise tissue microstructure, based on the random thermal motion of water molecules. Of particular interest is its potential for inferring the orientation of the coherently oriented fiber bundles within brain white matter tissue, as this opens up the possibility of investigating brain connectivity in vivo using so-called fiber-tracking algorithms. This relatively new technique is becoming a valuable diagnostic tool for a large number of neuropathological diseases. The main goals of this research topic are: - to use clinically relevant acquisitions (< 15 minutes) - to estimate fiber orientations in each voxel as accurate as possible - to assess global brain connectivity by means of tractography

Researcher(s)

Research team(s)

Generalised spherical deconvolution of diffusion MRI data for improved microstructural specificity and higher resolution imaging of white matter. 01/10/2015 - 30/09/2018

Abstract

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.

Researcher(s)

Research team(s)