Recent developments in scintillating fiber detection systems in radiation therapy
Bibliographic record
Abstract
Sophisticated radiotherapy techniques lead to more conformal dose distributions but increase treatment complexity. Image guidance allows for varying degrees of accuracy in patient set-up. However, the consequences of inaccurate set-up and/or patient motion during treatment become more serious when treatment doses are increased and treatment margins are decreased. Thus, the need to know if the dose has been delivered as planned has driven the development of plastic scintillation detector systems for accurate measurements in real time with high spatial resolution. We have developed a clinical prototype comprising 29 scintillating fiber detectors 1 mm in diameter and 2 mm in length. The detectors are coupled to clear optical fibers that collect the scintillation photons and transport them to a CCD for detection. Open field profiles and depth-dose profiles in water-equivalent phantoms were compared to ionization chamber measurements in water. The maximum relative in-field difference was 1.6%. With a standard deviation for in-field measurements smaller than 1%, this prototype array was found to be accurate, precise and practical. Monte Carlo simulations were also used to evaluate the response of the scintillation detector to proton beams and to optimize the light collection efficiency. The Monte Carlo code Geant4 was used to simulate dose deposition, the production of scintillation photons and the propagation of those photons inside the scintillation detector. Further development of the system will allow thousands of measurement points distributed in a three-dimensional volume per single irradiation, therefore producing a rapid evaluation of complex dose distributions emanating from these new complex treatment modalities.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".