Anatomy-Driven Brain Connectivity Mapping
Lead Research Organisation:
University College London
Department Name: Computer Science
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Organisations
People |
ORCID iD |
Daniel Alexander (Principal Investigator) | |
Hui Zhang (Co-Investigator) |
Publications
Palombo M
(2020)
SANDI: A compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI.
in NeuroImage
Graham M
(2018)
A supervised learning approach for diffusion MRI quality control with minimal training data
in NeuroImage
Kaden E
(2016)
Multi-compartment microscopic diffusion imaging.
in NeuroImage
Alexander DC
(2017)
Image quality transfer and applications in diffusion MRI.
in NeuroImage
Grussu F
(2015)
Neurite orientation dispersion and density imaging of the healthy cervical spinal cord in vivo.
in NeuroImage
Alexander DC
(2019)
Imaging brain microstructure with diffusion MRI: practicality and applications.
in NMR in biomedicine
Ferizi U
(2017)
Diffusion MRI microstructure models with in vivo human brain Connectome data: results from a multi-group comparison.
in NMR in biomedicine
Alexander D
(2017)
Imaging brain microstructure with diffusion MRI: practicality and applications
in NMR in Biomedicine
Description | Two key findings so far: - Using non-symmetric fibre orientation patterns improves brain connectivity mapping via tractography. Within the grant we have developed a method for estimating non-symmetric fibre configurations from MRI data using neighbourhood information and shown that embedding these new objects within existing tractography algorithms resolves long-standing ambiguities in brain connectivity mapping. - Dispersion is often mistaken for crossing in diffusion MRI. Various studies suggest that fibre crossing configurations arise in a large proportion of MRI voxels in white matter of the brain. Within this grant we have challenges that view noting that more continuous patterns of fibre dispersion can appear as crossing although in fact should be considered separately. Experimental work reveals that many of the voxels previously identified as containing crossings in fact are more likely to contain dispersed fibre configurations. This influences the design of future tractography algorithms for brain connectivity mapping. |
Exploitation Route | These findings prompt further development of tractography algorithms and the underlying models they use, both of which are evolving fast. |
Sectors | Healthcare,Pharmaceuticals and Medical Biotechnology |
Description | EPSRC Centre for Doctoral Training in Intelligent, Integrated Imaging In Healthcare (i4health) |
Amount | £6,034,274 (GBP) |
Funding ID | EP/S021930/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 09/2019 |
End | 03/2028 |
Description | Enabling clinical decisions from low-power MRI in developing nations through image quality transfer |
Amount | £1,020,000 (GBP) |
Funding ID | EP/R014019/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 02/2018 |
End | 01/2021 |
Description | Image Quality Transfer |
Amount | £60,000 (GBP) |
Organisation | Microsoft Research |
Sector | Private |
Country | Global |
Start | 10/2015 |
End | 09/2018 |
Description | Learning MRI and histology image mappings for cancer diagnosis and prognosis |
Amount | £774,000 (GBP) |
Funding ID | EP/R006032/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 12/2017 |
End | 01/2020 |