Contribution of perfusion to the <sup>11</sup>C‐acetate signal in brown adipose tissue assessed by DCE‐MRI and <sup>68</sup>Ga‐DOTA PET in a rat model
Bibliographic record
Abstract
Purpose Determine if dynamic contrast enhanced (DCE) ‐MRI and/or 68 gallium 1,4,7,10‐tetraazacyclododecane N, N′, N″, N‴‐tretraacetic acid (68Ga‐DOTA) positron emission tomography (PET) can assess perfusion in rat brown adipose tissue (BAT). Evaluate changes in perfusion between cold‐stimulated and heat‐inhibited BAT. Determine if the 11C‐acetate pharmacokinetic model can be constrained with perfusion information to improve assessment of BAT oxidative metabolism. Methods Rats were split into three groups. In group 1 (N = 6), DCE‐MRI with gadobutrol was compared directly to 68Ga‐DOTA PET following exposure to 10 °C for 48 h. 11C‐Acetate PET was also performed to assess oxidation. In group 2 (N = 4), only 68Ga‐DOTA PET was acquired following exposure to 10 °C for 48 h. Finally, in group 3 (N = 10), perfusion was assessed with DCE‐MRI in rats exposed to 10 °C or 30 °C for 48 h, and oxidation was measured with 11C‐acetate. Perfusion was quantified with a two‐compartment pharmacokinetic model, while oxidation was assessed by a four‐compartment model. Results DCE‐MRI and 68Ga‐DOTA PET provided similar perfusion measures, but a decrease in the perfusion signal was noted with longer imaging sessions. Exposure to 10 °C or 30 °C did not affect the perfusion measures, but the 11C‐acetate signal increased in BAT at 10 °C. Without prior information about blood volume, the 11C‐acetate compartment model overestimated blood volume and underestimated oxidation in 10 °C BAT. Conclusion Precise assessment of oxidation via 11C‐acetate PET requires prior information about blood volume which can be obtained by DCE‐MRI or 68Ga‐DOTA PET. Since perfusion can change rapidly, simultaneous PET‐MRI would be preferred.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.001 |
| 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".