Initial Evaluation of the Performance of Novel Inorganic Scintillating Detectors for Small Animal Irradiation Dosimetry
Bibliographic record
Abstract
The purpose of this study was to design and evaluate the performance of four novel inorganic scintillating detectors (ISDs) on the Small Animal Radiation Research Platform (SARRP). Relative scintillator output, measurement repeatability, setup uncertainty, linearity with dose rate, and signal reproducibility over time were investigated. The Gd2O2S:Tb detector had the highest relative signal output, generating up to 219 times more charge than a previously characterized BCF-60-based plastic scintillating detector (PSD). The Gd2O2S:Tb detector was then used to measure 220 kVp therapy beam profiles of 10 x 10 and 5 x 5 mm2fields. Beam profiles using the ZnS-based phosphor were also obtained and compared to investigate the performance of a lower density inorganic scintillator. 10 x 10 and 5 x 5 mm2therapy beam profile measurements made with the Gd2O2S:Tb and BCF-60 detectors differed, on average, by 1.1% and 1.9%, respectively. The ZnS:Ag measurements differed, on average, by 2.5% and 6% relative to BCF-60 measurements of the 10 x 10 and 5 x 5 mm2beam profiles, respectively. MicroCT imaging of the detector volumes was also performed, revealing poor packing of the ZnS:Ag crystalline phosphor in the deepest region of the cylindrical cavity. The Gd2O2S:Tb detector, in particular, has proven to be a promising candidate for real-time dosimetry of small fields in small animal irradiators, primarily because of the very large signal intensities observed, along with good repeatability, dose rate linearity, reproducibility and agreement with beam profile measurements made with a previously validated detector.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".