The influence of measurement uncertainties on the evaluation of the distribution volume ratio and binding potential in rat studies on a microPET® R4: a phantom study
Bibliographic record
Abstract
In small animal positron emission tomography (PET) imaging, the injectable radiotracer dose is often limited by the tracer mass which, together with the tracer kinetics and scanner sensitivity, dictates the statistical quality of the time activity curves (TACs) used to extract biological 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(L), BP(L)) and the ratio (DVRr, BPr) methods for two tracers, with the Concorde microPET R4 camera. Parameters' coefficients of variation (COV) were estimated from a combination of rat and phantom data. For 11C-dihydrotetrabenazine, the COV was 11% for the BP(L) and 13.4% for the BPr when using TACs obtained from individual regions of interest (ROIs) and segmented attenuation correction. The COVs were reduced to 7.5% (BP(L)) and 8.6% (BPr) when the striatal and cerebellar TACs were estimated as averages of 3 and 2 ROIs respectively. Results obtained for 11C-methylphenidate (MP) yielded approximately 30% higher COVs. With measured attenuation correction, the COVs were on average 100% higher. The presented method can be used to examine the contribution of a variety of imaging conditions to the uncertainty of biologically meaningful parameters.
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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 | 0.017 |
| 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) | 0.001 | 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".