A New Tool for Evaluating the Spatial Resolvability in Hot Spot Phantoms
Bibliographic record
Abstract
Hot Spot phantoms are widely used to assess the spatial resolution performance and image quality of PET scanners. Conventionally, a simple visual examination is conducted to determine whether hot spots of specific sizes are discernible. However, the lack of quantitative assessment could lead to different conclusions based on the observer’s perception of spot resolvability. To address this, some groups have recently reported valley-to-peak ratios (VPRs) of line profiles drawn across the spots to determine whether they are resolved using the Rayleigh criterion. We propose an opensource tool for semi-automatic drawing of line profiles through all possible directions of sections in Hot Spot phantoms to determine the VPRs and fraction of discernible spots. Various techniques are available to the users for extracting the valleys and peaks, for instance with fitting or region-averaging to mitigate the influence of noise. This tool can help the nuclear imaging community to complement the conventional qualitative assessment of Hot Spot phantoms with a more robust quantitative evaluation of the resolvability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".