Dependence of the MR signal on the magnetic susceptibility of blood studied with models based on real microvascular networks
Bibliographic record
Abstract
Purpose The primary goal of this study was to estimate the value of , the exponent in the power law relating changes of the transverse relaxation rate and intra‐extravascular local magnetic susceptibility differences as . The secondary objective was to evaluate any differences that might exist in the value of obtained using a deoxyhemoglobin‐weighted distribution versus a constant distribution assumed in earlier computations. The third objective was to estimate the value of β that is relevant for methods based on susceptibility contrast agents with a concentration of higher than that used for BOLD fMRI calculations. Methods Our recently developed model of real microvascular anatomical networks is used to extend the original simplified Monte‐Carlo simulations to compute from the first principles. Results Our results show that for most BOLD fMRI measurements of real vascular networks, as opposed to earlier predictions of .5 using uniform distributions. For perfusion or fMRI methods based on contrast agents, which generate larger values for , for 9.4 T, whereas at 14 T can drop below 1 and the variation across subjects is large, indicating that a lower concentration of contrast agent with a lower value of is desired for experiments at high B0. Conclusion These results improve our understanding of the relationship between R2* and the underlying microvascular properties. The findings will help to infer the cerebral metabolic rate of oxygen and cerebral blood volume from BOLD and perfusion MRI, respectively.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".