A fast and simple dose-calibrator-based quality control test for the radionuclidic purity of cyclotron-produced<sup>99m</sup>Tc
Bibliographic record
Abstract
Cyclotron production of 99mTc through the (100)Mo(p,2n)99mTc reaction channel is actively being investigated as an alternative to reactor-based (99)Mo generation by nuclear fission of (235)U. Like most radioisotope production methods, cyclotron production of 99mTc will result in creation of unwanted impurities, including Tc and non-Tc isotopes. It is important to measure the amounts of these impurities for release of cyclotron-produced 99mTc (CPTc) for clinical use. Detection of radioactive impurities will rely on measurements of their gamma (γ) emissions. Gamma spectroscopy is not suitable for this purpose because the overwhelming presence of 99mTc and the count-rate limitations of γ spectroscopy systems preclude fast and accurate measurement of small amounts of impurities. In this article we describe a simple and fast method for measuring γ emission rates from radioactive impurities in CPTc. The proposed method is similar to that used to identify (99)Mo breakthrough in generator-produced 99mTc: one dose calibrator (DC) reading of a CPTc source placed in a lead shield is followed by a second reading of the same source in air. Our experimental and theoretical analysis show that the ratio of DC readings in lead to those in air are linearly related to γ emission rates from impurities per MBq of 99mTc over a large range of clinically-relevant production conditions. We show that estimates of the γ emission rates from Tc impurities per MBq of 99mTc can be used to estimate increases in radiation dose (relative to pure 99mTc) to patients injected with CPTc-based radiopharmaceuticals. This enables establishing dosimetry-based clinical-release criteria that can be tested using commercially-available dose calibrators. We show that our approach is highly sensitive to the presence of 93gTc, 93mTc, 94gTc, 94mTc, 95mTc, 95gTc, and 96gTc, in addition to a number of non-Tc impurities.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".