Bibliographic record
Abstract
Positron emission tomography (PET) is a highly effective functional imaging modality. Unfortunately, PET cannot be easily used for dual-isotope imaging (which would allow for simultaneous investigation of two different biological processes), because positron-electron annihilation products from different tracers are indistinguishable in terms of energy. Methods have been proposed for dual-isotope PET based on different half-lives of the participating isotopes, however, those approaches are based on many assumptions concerning kinetic behavior of the tracers and may not always lead to optimal results. In this manuscript we propose another approach for dualisotope PET and investigate its effectiveness using GATE simulations. Our method requires that one of the two radioactive isotopes is a pure positron emitter while the second isotope emits an additional high-energy photon in a cascade (simultaneously) with positron emission. Measurement of this auxiliary photon in coincidence with the annihilation event allows us to identify the corresponding 511 keV photon pair as originating from the same isotope. Two list-mode datasets are created: a primary dataset that contains all detected 511 keV photon pairs from both isotopes; and a second, auxiliary (much smaller) dataset that contains only those PET events for which coincident auxiliary gamma has also been detected. An image reconstructed from the auxiliary dataset reflects the distribution of the second radiotracer and serves as a prior for the reconstruction of the primary dataset. Our preliminary simulation study with partially overlapping18F and22Na radiotracer distributions suggest that this method allows for separation of two activities with relative errors less than 0.07.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".