SU‐E‐T‐241: Characterization of MOSFET Detectors for in Vivo Dosimetry in Interventional Radiology and for Dose Reconstruction in Case of Accident
Bibliographic record
Abstract
Purpose: As MOSFET detectors allow dose measurements in real‐time, the interest for these dosemeters is growing. The aim of this study was to investigate the dosimetric properties of these detectors in order to use them for in vivo dosimetry in interventional radiology and for dose reconstruction in case of accident.Methods: Commercially available TN‐502RD‐H MOSFET detectors (Best medical canada, Ottawa, Canada) were investigated in this study by tests in calibration laboratories. Reproducibility of the measurements, dose response and dose rate dependence of the MOSFET detectors were studied with a Co‐60 source and their energy response with continuous X‐rays beams. In addition, MOSFETs behaviour has been investigated in pulsed X‐rays fields in order to study the influence of the frequency, dose rate and duration of pulses on MOSFET responses. Finally, in order to validate the integrated dose given by MOSFET detectors, MOSFETs and TLD (7LiF:Cu,P) were fixed on an Alderson‐Rando phantom in the conditions of an interventional neuroradiology procedure and their response has been compared.Results: The reproducibility of the dose measurements was found to be very satisfying. The response was linear with the dose and no dependency with the dose rate was found. However, relatively strong energy dependence was observed, i.e. a factor of 1.4 was seen in the response between 57 and 79 keV. In pulsed X‐rays, the response was correct only for duration of pulses equal to or higher than 10 ms. Good agreement was found between TLD and MOSFET measurements Conclusions: The results of this study show the suitability of MOSFET detectors for in vivo dosimetry in interventional radiology and for dose reconstruction in case of accident, provided a well‐corrected energy dependence and a pulse duration equal to or higher than 10 ms and an optimized contact between the detector and the skin of the patient.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".