A method for measuring the energy spectrum of coincidence events in positron emission tomography
Bibliographic record
Abstract
Positron emission tomography (PET) system energy response is typically characterized in singles detection mode, yet there are situations in which the energy spectrum of coincidence events might be different than the spectrum measured in singles mode. Examples include imaging with isotopes that emit a prompt gamma in coincidence with a positron emission, imaging with low activity in a LSO/LYSO-based cameras, in which the intrinsic activity is significant, and in high scatter situations where the two 511 keV photons have different scattering probabilities (i.e. off-center line source). The ability to accurately measure the energy spectrum of coincidence events could be used for validating simulation models, optimizing energy discriminator levels and examining scatter models and corrections. For many PET systems operating in coincidence mode, the only method available for estimating the energy spectrum is to step the lower and upper level discriminators (LLD and ULD). Simple measurement techniques such as using a narrow sliding energy window or stepping only the LLD will not yield a spectrum of coincidence events that is accurate for cases where there are different energy components contributing to the spectrum. In this work we propose a new method of measuring the energy spectrum of coincidence events in PET based on a linear combination of two sets of coincident count measurements: one made by stepping the LLD and one made by stepping the ULD. The method was tested using both Monte Carlo simulations of a Siemens microPET R4 camera and measured data acquired on a Siemens Inveon PET camera. The results show that our energy spectrum calculation method accurately measures the coincident energy spectra for cases including the beta/gamma spectrum of the (176)Lu intrinsic activity present in the LSO scintillator crystals, a (68)Ge source and an (124)I source (in which there are prompt gamma-rays emitted together with the positron).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".