<sup>19</sup>F MRI oximetry: simulation of perfluorocarbon distribution impact
Bibliographic record
Abstract
In (19)F MRI oximetry, a method used to image tumour hypoxia, perfluorocarbons serve as oxygenation markers. The goal of this study is to evaluate the impact of perfluorocarbon distribution and concentration in (19)F MRI oximetry through a computer simulation. The simulation studies the correspondence between (19)F measured (pO(FNMR)(2)) and actual tissue oxygen tension (pO(2)) for several tissue perfluorocarbon distributions. For this, a Krogh tissue model is implemented which incorporates the presence of perfluorocarbons in blood and tissue. That is, in tissue the perfluorocarbons are distributed homogeneously according to Gaussian diffusion profiles, or the perfluorocarbons are concentrated in the capillary wall. Using these distributions, the oxygen tension in the simulation volume is calculated. The simulated mean oxygen tension is then compared with pO(FNMR)(2), the (19)F MRI-based measure of pO(2) and with pO(0)(2), pO(2) in the absence of perfluorocarbons. The agreement between pO(FNMR)(2) and actual pO(2) is influenced by vascular density and perfluorocarbon distribution. The presence of perfluorocarbons generally gives rise to a pO(2) increase in tissue. This effect is enhanced when perfluorocarbons are also present in blood. Only the homogeneous perfluorocarbon distribution in tissue with no perfluorocarbons in blood guarantees small deviations of pO(FNMR)(2) from pO(2). Hence, perfluorocarbon distribution in tissue and blood has a serious impact on the reliability of (19)F MRI-based measures of oxygen tension. In addition, the presence of perfluorocarbons influences the actual oxygen tension. This finding may be of great importance for further development of (19)F MRI oximetry.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".