Effect of measurement uncertainty on region of interest based and parametric binding potential estimates for the high resolution research tomograph (HRRT)
Bibliographic record
Abstract
High resolution Positron Emission Tomography (PET) imaging leads to very small pixel sizes. Generally the increase in resolution is not paralleled by a corresponding increase in sensitivity which may cause the count density per voxel to be low. Here we are exploring how the statistical quality of the data acquired with the high resolution research tomograph (HRRT) influences the accuracy of the determination of the binding potential (BP) for typical human studies performed with11middotC-raclopride. Susceptibility to noise was tested for 3 modelling approaches: the Logan graphical model, the simplified reference tissue method (RTM) and the delayed ratio method (DRM). For each approach BP was calculated on a region of interest (ROI) and voxel basis (parametric maps). Using a method based on experimentally defined replicas of time activity curves (TACs) representative of those obtained in human scans we found that for this tracer the contribution of the statistical noise to the BP determination is ~ 5-8 % when the TACs are evaluated on an ROI basis (either ROI TACs used as input, or ROI placed on the BP parametric image) and 9-12 % when calculated on a single pixel basis. The Logan approach was found to suffer from a considerable bias due to statistical noise when the BP was calculated on a single pixel basis, while RTM and DRM showed no such bias. Overall, for this tracer and these scanning conditions the RTM proved to be the least sensitive to statistical noise in the data.
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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 | 0.054 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".