ConFiG: Contextual Fibre Growth to generate realistic axonal packing for diffusion MRI simulation
- Submitting institution
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University College London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 16256
- Type
- D - Journal article
- DOI
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10.1016/j.neuroimage.2020.117107
- Title of journal
- NeuroImage
- Article number
- 117107
- First page
- 117107
- Volume
- 220
- Issue
- -
- ISSN
- 1053-8119
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2020
- URL
-
-
- Supplementary information
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-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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3
- Research group(s)
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-
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper established ConFig, the current state-of-the-art computational tool for creating realistic geometries of densely packed axons (the parts of neurons enabling communication) supporting simulation of particle mobility within and thus realistic MRI signal generation. ConFig is a first-of-its-kind algorithm that encodes the natural rules of real axonal growth, allowing to reproduce subtle morphological features like axonal undulation and bulging that cannot be realistically reproduced by any other existing method. Offering a flexible and controllable simulation platform, ConFig is already supporting the development and validation of next-generation imaging techniques for mapping axonal microstructure and brain-connectivity non-invasively through MRI.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -