Graphical user interface for yield and doses estimations for cyclotron technetium production
Bibliographic record
Abstract
Technetium-99m (99mTc) production using medical cyclotrons has been proposed to solve the anticipated shortage of99mTc, which traditionally is obtained from the reactor-produced generators. Optimizing reaction conditions to maximize99mTc production yield becomes crucial. Additionally, since besides99mTc, many other undesirable stable and radioactive isotopes can be produced, reaction parameters must also minimize their production yields. Manual calculations of all these yields are very time consuming. Therefore, the aim of our study was to create a graphical user interface (GUI) that would automate these calculations. The GUI, based on Matlab, includes three layers, allowing for yields calculations, and facilitating gamma spectrum analysis. Moreover, the impact of different technetium impurities on patient doses can be evaluated. Theoretical estimates obtained from the GUI were compared with the results of four cyclotron runs. The gamma spectroscopy measurements were performed at multiple time points using an HPGe detector. The preliminary results showed that the activities of99mTc measured at 3h post irradiation agree well with theoretical predictions, indicating that the theoretical cross sections reflect well the true reaction probabilities. Agreement between measured and predicted activities for other isotopes varied. Our initial experience shows that theoretical estimates provided by this GUI helped us to efficiently analyze gamma spectroscopy data from early cyclotron experiments, allowing us to test the method and optimize production parameters. We expect however that the main advantage of this GUI will be at the later clinical stage when entering reaction parameters will allow the users to predict production yields and estimate radiation doses for each particular cyclotron run.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.327 | 0.077 |
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".