Toward nonparametric diffusion‐ characterization of crossing fibers in the human brain

Autor: Giuliana Durighel, Karin Bryskhe, Daniel Topgaard, Jeffrey Critchley, Tim Sprenger, Alexis Reymbaut, Michael Sughrue
Rok vydání: 2020
Předmět:
Zdroj: Magnetic Resonance in Medicine. 85:2815-2827
ISSN: 1522-2594
0740-3194
Popis: Purpose To estimate T 1 for each distinct fiber population within voxels containing multiple brain tissue types. Methods A diffusion- T 1 correlation experiment was carried out in an in vivo human brain using tensor-valued diffusion encoding and multiple repetition times. The acquired data were inverted using a Monte Carlo algorithm that retrieves nonparametric distributions P ( D , R 1 ) of diffusion tensors and longitudinal relaxation rates R 1 = 1 / T 1 . Orientation distribution functions (ODFs) of the highly anisotropic components of P ( D , R 1 ) were defined to visualize orientation-specific diffusion-relaxation properties. Finally, Monte Carlo density-peak clustering (MC-DPC) was performed to quantify fiber-specific features and investigate microstructural differences between white matter fiber bundles. Results Parameter maps corresponding to P ( D , R 1 ) 's statistical descriptors were obtained, exhibiting the expected R 1 contrast between brain tissue types. Our ODFs recovered local orientations consistent with the known anatomy and indicated differences in R 1 between major crossing fiber bundles. These differences, confirmed by MC-DPC, were in qualitative agreement with previous model-based works but seem biased by the limitations of our current experimental setup. Conclusions Our Monte Carlo framework enables the nonparametric estimation of fiber-specific diffusion- T 1 features, thereby showing potential for characterizing developmental or pathological changes in T 1 within a given fiber bundle, and for investigating interbundle T 1 differences.
Databáze: OpenAIRE
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