The influence of measurement uncertainties on the evaluation of the distribution volume ratio in rat studies on a microPET/spl reg/ R4: a phantom study
Bibliographic record
Abstract
In small animal imaging the injectable radiotracer dose is often limited by the tracer mass effect. Biological considerations as opposed to the count rate capabilities of the scanner are thus often the limiting factor for the maximum allowed radiotracer dose, which in turn, together with the tracer kinetics and scanner sensitivity, dictates the statistical quality of the time activity curves (TACs) used to extract biologically significant parameters. We investigated the effect of measurement uncertainty on the determination of the distribution volume ratio (DVR) and binding potential (BP) as estimated using the tissue input Logan (DVR/sub L/, BP/sub L/) and the ratio (DVR/sub r/, BP/sub r/) methods for two different tracers, with the Concorde microPET/spl reg/ R4 camera. TAC templates extracted from rat studies and phantom data were used. We found that for a tracer with relatively fast kinetics, /sup 11/C-dihydrotetrabenazine (DTBZ), the overall coefficient of variation (COV) was 11% for the BP/sub L/ and 13.4% for the BP/sub r/, when the BP was calculated using TACs obtained from individual regions of interest (ROIs). The COVs were reduced to 7.5% (BP/sub L/) and 8.6% (BP/sub r/) when the striatal and cerebellar TACs were estimated as averages of 3 and 2 ROIs respectively. Similar results obtained for a tracer with slower kinetics /sup 11/C-methylphenidate (MP), yielded approximately 30% higher COVs. The above values were obtained when segmented attenuation correction was applied to the data; with measured attenuation correction the COVs were on average 50% higher.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".