Bibliographic record
Abstract
Positron emission tomography (PET) is widely recognized as a highly effective functional imaging modality. Unfortunately, standard PET cannot be 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 that have been proposed for dual-isotope PET rely on differences in half-lives of the participating isotopes; these approaches, however, require making assumptions concerning kinetic behavior of the tracers and may not lead to optimal results. In this paper we propose a novel approach for dual-isotope PET and investigate its performance using GATE simulations. Our method requires one of the two radioactive isotopes to be a pure positron emitter and the second isotope to emit an additional high-energy gamma in a cascade simultaneously with positron emission. Detection of this auxiliary prompt gamma 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, tagged (much smaller) dataset that contains only those PET events for which a coincident prompt gamma has also been detected. An image reconstructed from the tagged dataset reflects the distribution of the second positron-gamma radiotracer and serves as a prior for the reconstruction of the primary dataset. Our preliminary simulation study with partially overlapping (18)F/(22)Na and (18)F/(60)Cu radiotracer distributions showed that in these two cases the dual-isotope PET method allowed for separation of the two activity distributions and recovered total activities with relative errors of about 5%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".