Comparative optic and dosimetric characterization of the HYPERSCINT scintillation dosimetry research platform for multipoint applications
Bibliographic record
Abstract
This study introduces the HYPERSCINT research platform (HYPERSCINT-RP100, Medscint Inc., Quebec, Canada), the first commercially available scintillation dosimetry platform capable of multi-point dosimetry through the hyperspectral approach. Optic and dosimetric performances of the system were investigated through comparison with another commercially available solution, the Ocean Optics QE65Pro spectrometer. The optical characterization was accomplished by measuring the linearity of the signal as a function of integration time, photon detection efficiency and spectral resolution for both systems under the same conditions. Dosimetric performances were then evaluated with a 3-point plastic scintillator detector (mPSD) in terms of signal to noise ratio (SNR) and signal to background ratio (SBR) associated with each scintillator. The latter were subsequently compared with those found in the literature for the Exradin W1, a single-point plastic scintillator detector. Finally, various beam measurements were realized with the HYPERSCINT platform to evaluate its ability to perform clinical photon beam dosimetry. Both systems were found to be comparable in terms of linearity of the signal as a function of the intensity. Although the QE65Pro possesses a higher spectral resolution, the detection efficiency of the HYPERSCINT is up to 1000 time greater. Dosimetric measurements shows that the latter also offers a better SNR and SBR, surpassing even the SNR of the Exradin W1 single-point PSD. While doses ranging from 1 to 600 cGy were accurately measured within 2.1% of the predicted dose using the HYPERSCINT platform coupled to the mPSD, the Ocean optics spectrometer shows discrepancies up to 86% under 50cGy. Similarly, depth dose, full width at half maximum region of the beam profile and output factors were all accurately measured within 2.3% of the predicted dose using the HYPERSCINT platform and exhibit an average difference of 0.5%, 1.6% and 0.6%, respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".