Szczegóły publikacji
Opis bibliograficzny
The sensitivity of diffusion MRI to microstructural properties and experimental factors / Maryam Afzali, Tomasz PIĘCIAK, Sharlene Newman, Eleftherios Garyfallidis, Evren Özarslan, Hu Cheng, Derek K. Jones // Journal of Neuroscience Methods ; ISSN 0165-0270. — 2021 — vol. 347 art. no. 108951, s. 1-29. — Bibliogr. s. 22-29, Abstr. — Publikacja dostępna online od: 2020-10-02. — T. Pięciak - dod. afiliacja: Universidad de Valladolid, Spain
Autorzy (7)
- Afzali Maryam
- AGHPięciak Tomasz
- Newman Sharlene
- Garyfallidis Eleftherios
- Özarslan Evren
- Cheng Hu
- Jones Derek K.
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 130697 |
|---|---|
| Data dodania do BaDAP | 2020-10-20 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.jneumeth.2020.108951 |
| Rok publikacji | 2021 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Journal of Neuroscience Methods |
Abstract
Diffusion MRI is a non-invasive technique to study brain microstructure. Differences in the microstructural properties of tissue, including size and anisotropy, can be represented in the signal if the appropriate method of acquisition is used. However, to depict the underlying properties, special care must be taken when designing the acquisition protocol as any changes in the procedure might impact on quantitative measurements. This work reviews state-of-the-art methods for studying brain microstructure using diffusion MRI and their sensitivity to microstructural differences and various experimental factors. Microstructural properties of the tissue at a micrometer scale can be linked to the diffusion signal at a millimeter-scale using modeling. In this paper, we first give an introduction to diffusion MRI and different encoding schemes. Then, signal representation-based methods and multi-compartment models are explained briefly. The sensitivity of the diffusion MRI signal to the microstructural components and the effects of curvedness of axonal trajectories on the diffusion signal are reviewed. Factors that impact on the quality (accuracy and precision) of derived metrics are then reviewed, including the impact of random noise, and variations in the acquisition parameters (i.e., number of sampled signals, b-value and number of acquisition shells). Finally, yet importantly, typical approaches to deal with experimental factors are depicted, including unbiased measures and harmonization. We conclude the review with some future directions and recommendations on this topic.