Detecting microstructural deviations in individuals with deep diffusion MRI tractometry
| Autor: | Adam C. Cunningham, Erika P. Raven, Sila Genc, Derek K. Jones, Chantal M. W. Tax, Greg D. Parker, Khalid Hamandi, William P. Gray, Dmitri Shastin, Kristin Koller, Maxime Chamberland, Marianne Bernadette van den Bree, Joanne L. Doherty |
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| Rok vydání: | 2022 |
| Předmět: |
Computational neuroscience
medicine.diagnostic_test Computer Networks and Communications Computer science business.industry Early detection Magnetic resonance imaging Pattern recognition 030218 nuclear medicine & medical imaging 3. Good health Computer Science Applications Unmet needs 03 medical and health sciences 0302 clinical medicine Discriminative model Computer Science (miscellaneous) medicine Anomaly detection Artificial intelligence Personalized medicine business 030217 neurology & neurosurgery Diffusion MRI |
| Zdroj: | Nature computational science. 1 |
| ISSN: | 2662-8457 |
| Popis: | Most diffusion magnetic resonance imaging studies of disease rely on statistical comparisons between large groups of patients and healthy participants to infer altered tissue states in the brain; however, clinical heterogeneity can greatly challenge their discriminative power. There is currently an unmet need to move away from the current approach of group-wise comparisons to methods with the sensitivity to detect altered tissue states at the individual level. This would ultimately enable the early detection and interpretation of microstructural abnormalities in individual patients, an important step towards personalized medicine in translational imaging. To this end, Detect was developed to advance diffusion magnetic resonance imaging tractometry towards single-patient analysis. By operating on the manifold of white-matter pathways and learning normative microstructural features, our framework captures idiosyncrasies in patterns along white-matter pathways. Our approach paves the way from traditional group-based comparisons to true personalized radiology, taking microstructural imaging from the bench to the bedside. The authors propose Detect, a browser-based anomaly detection framework for diffusion magnetic resonance imaging tractometry data. The tool leverages normative microstructural brain features derived from healthy participants using deep autoencoders to detect anomalies at the individual level. |
| Databáze: | OpenAIRE |
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