Statistical And Physical Content Of Low-energy Photons In Nuclear Medicine Imaging
Bibliographic record
Abstract
Limit in the energy resolution of present gamma camera technology prevents a total rejection of Compton events: inclusion of bad photons in the image is inescapable. Various methods acquiring data over a large portion of the spectrum have already been described. Usefulness of low energy photons can be investigated using statistical and physical models. Holospectral Imaging, for instance, exploits correlation between energy frames to build an information related transformation optimizing primary photon image. One can also use computer simulation to show that a portion of low energy photons is detected at the same location (pixel) as pure primary photons. These events are for instance: 1) photons undergoing scatter interaction in the crystal; 2) photons undergoing a small angle backscatter or forwardscatter interaction in the medium, 3) photons backscattered by the Pyrex into the crystal. For a 140 keV source in lOcm of water and a 1/4” thick crystal, more than 6% of all the photons detected do not have the primary energy and still are located in the right 4mm pixel. Similarly, it is possible to show that more than 5% of all the photons detected at 140 keV deposit their energy in more that one pixel. These results give additional support to techniques considering low energy photons and more sophisticated ways to segregate between good and bad events.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| 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".