کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5631560 | 1580863 | 2017 | 12 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Disentangling micro from mesostructure by diffusion MRI: A Bayesian approach Disentangling micro from mesostructure by diffusion MRI: A Bayesian approach](/preview/png/5631560.png)
- Disentanglement of microstructural properties of neurites from their orientation distribution.
- Microstructure estimation from clinical feasible dMRI, including fast protocols (as few as 28 diffusion weighting directions).
- Computation time of seconds.
- In-vivo results are consistent with existing anatomical knowledge.
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.
Journal: NeuroImage - Volume 147, 15 February 2017, Pages 964-975