Disentangling micro from mesostructure by diffusion MRI: A Bayesian approach
Open accessAbstract
Diffusion-sensitized magnetic resonance imaging probes the cellular structure of the human brain, but the primary microstructural information gets lost in averaging over higher-level, mesoscopic tissue organization such as different orientations of neuronal fibers. While such averaging is inevitable due to the limited imaging resolution, we propose a method for disentangling the microscopic cell properties from the effects of mesoscopic structure. We further avoid the classical fitting paradigm and use supervised machine learning in terms of a Bayesian estimator to estimate the microstructural properties. The method finds detectable parameters of a given microstructural model and calculates them within seconds, which makes it suitable for a broad range of neuroscientific applications.
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For ROI analysis, selected areas are manually drawn, which is more time consuming, requires a priori anatomical localization, and is prone to bias.
“For ROI analysis, selected areas are manually drawn, which is more time consuming, requires a priori anatomical localization, and is prone to bias [48].”
From Diffusion tensor imaging reveals whole-brain microstructural changes in the P301L mouse model of tauopathy. (2020) · cites this paper as [48]
The Discussion describes computation time for diffusion-MRI model fitting, not time spent drawing ROIs. Searches for manual ROI delineation, anatomical localization, and selection bias found no passage addressing those claims in our copy. (LLM, conf=0.76)
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Self-citationScattered slice shard reconstruction for motion correction in multi-shell diffusion MRI
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- Authors
- Reisert, M., Kellner, E., Dhital, B., Hennig, J., & Kiselev, V. G.
- Affiliations
- University Medical Center Freiburg (DE)
- Corresponding author
- Reisert, Marco
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2017-02
- Volume
- 147
- Pages
- 964-975
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2538791563
- Cited by
- 197
- References
- 1
- Citations by year
- 2026: 12 · 2025: 30 · 2024: 23 · 2023: 14 · 2022: 17 · 2021: 15 · 2020: 22 · 2019: 22 · 2018: 27 · 2017: 15
- FWCI
- 18.52
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (DFG RE 3286/2-1, DFG KI 1089/3-2)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › NMR spectroscopy and applications
- Keywords
- diffusion MRI, mesoscopic structure, Bayesian estimation, microstructure modeling, supervised learning, brain tissue structure
- MeSH terms
- Adult, Bayes Theorem, Humans, Image Processing, Computer-Assisted, Models, Neurological, Neurites, Diffusion Magnetic Resonance Imaging, White Matter
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