The role of diffusion and perivascular spaces in dynamic susceptibility contrast MRI
Bibliographic record
Abstract
Abstract We investigated the effects of brain tissue orientation, diffusion, and perivascular spaces on dynamic susceptibility contrast MRI. A 3D numerical model of a white matter voxel was created that consists of an isotropic capillary bed and anisotropic vessels that run in parallel with white matter tracts and are surrounded by perivascular spaces. The signal within the voxel was simulated by solving the Bloch-Torrey equation. Experimental perfusion data were acquired with a gradient echo dynamic susceptibility contrast scan. White matter fibre orientation was mapped with diffusion tensor imaging. Our numerical model of the contrast agent induced increase in R 2 * , as a function of tissue orientation, was fit to dynamic susceptibility contrast MRI data from thirteen subjects by minimizing the bias-corrected Akaike information criterion. White matter blood volume fraction in both the isotropic and the anisotropic vessels was determined as a free parameter, and results were analyzed as a function of diffusivity and perivascular space size. Total white matter blood volume was found to be 2.57%, with one third of the blood residing in blood vessels that run parallel with white matter tracts. Gradient echo dynamic susceptibility contrast MRI strongly depended on white matter tissue orientation and, according to the numerical simulations, this effect is amplified by diffusion and perivascular spaces.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".