High-angular resolution diffusion tensor imaging: physical foundation and geometric framework
This paper proposes a statistical physics-based data assimilation model for the mobility of water-bound hydrogen nuclear spins in the brain in the context of diffusion weighted magnetic resonance imaging (DWI or DW-MRI). Point of departure is a statistical hopping model that emulates molecular motio...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Physics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2024.1447311/full |
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| Summary: | This paper proposes a statistical physics-based data assimilation model for the mobility of water-bound hydrogen nuclear spins in the brain in the context of diffusion weighted magnetic resonance imaging (DWI or DW-MRI). Point of departure is a statistical hopping model that emulates molecular motion in the presence of static and stationary microscale obstacles, statistically reflected in the apparent inhomogeneous anisotropic DWI signal profiles. Subsequently, we propose a Riemann–Finsler geometric interpretation in terms of a metric transform that simulates this molecular process as free diffusion on a vacuous manifold with all diffusion obstacles absorbed in its geometry. The geometrization procedure supports the reconstruction of neural tracts (geodesic tractography) and their quantitative characterization (tractometry). The Riemann-DTI model for geodesic tractography based on diffusion tensor imaging (DTI) arises as a limiting case. The genuine Finslerian case is a geometric representation of high-angular resolution DTI, i.e., a generalized rank-two DTI framework without the quadratic restriction implied by a simplifying Gaussianity assumption on local diffusion or a second-order harmonic approximation of local orientation distributions. |
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| ISSN: | 2296-424X |