Anatomy-Driven Brain Connectivity Mapping
Lead Research Organisation:
University College London
Department Name: Computer Science
Abstract
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Organisations
People |
ORCID iD |
Daniel Alexander (Principal Investigator) | |
Hui Zhang (Co-Investigator) |
Publications
Chandran K
(2022)
Docking simulation and ADMET prediction based investigation on the phytochemical constituents of Noni (Morinda citrifolia) fruit as a potential anticancer drug.
in In silico pharmacology
Palombo M
(2020)
SANDI: A compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI.
in NeuroImage
Palombo M
(2019)
A generative model of realistic brain cells with application to numerical simulation of the diffusion-weighted MR signal.
in NeuroImage
Alexander DC
(2019)
Imaging brain microstructure with diffusion MRI: practicality and applications.
in NMR in biomedicine
Zhang J
(2018)
In vivo characterization of white matter pathology in premanifest huntington's disease.
in Annals of neurology
Ganepola T
(2018)
Using diffusion MRI to discriminate areas of cortical grey matter.
in NeuroImage
Alfaro-Almagro F
(2018)
Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank
in NeuroImage
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 |