Toward nonparametric diffusion‐T1 characterization of crossing fibers in the human brain. Issue 5 (10th December 2020)

• Record Type: Journal Article
• Authors: Reymbaut, Alexis; Critchley, Jeffrey; Durighel, Giuliana; Sprenger, Tim; Sughrue, Michael; Bryskhe, Karin; Topgaard, Daniel
• Is Part Of: Magnetic resonance in medicine. Volume 85:Issue 5(2021)
• Page Start: 2815
• Page End: 2827
• Publication Date: 2020-12-10
• Subjects: diffusion‐relaxation correlation -- fiber‐specific microstructure -- inverse Laplace transform -- multivariate distribution -- orientation distribution function -- tensor‐valued diffusion encoding; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.28604
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : 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.