WE‐G‐500‐05: Red Spectral Shift of Cherenkov Emission with Applications in Image‐Guided and Intensity‐Modulated Radiation Therapy
Bibliographic record
Abstract
Purpose: This work aims to validate the potential application of Cherenkov emission (CE) in radiotherapy by a spectral shift to the optical window of biological tissue in order to increase CE detection during radiotherapy. Methods: 18 MeV and 18 MV clinical electron and photon beams are used for the experiments. The CE detector consists of a multi‐mode fiber optic cable (numerical aperture = 0.22), positioned out of the beam and connected to a spectrometer incorporating a front‐illuminated CCD array. In order to evaluate the dose versus CE correlation, depth and profile scans were acquired at the angle of maximum emission. A Monte Carlo CE simulator, designed with the Geant4 simulation toolkit, was used to validate the correlation. A spectral shift was achieved with CdSe/ZnS core/shell nanoparticles (NPs) emitting at 650 nm. Measurements were acquired with a water tank, in order to test the signal's capacity to stimulate NP photoluminescence, and at varying depths in a tissue‐simulating phantom composed of water, Intralipid and bovine blood. Results: A strong correlation between dose and CE is evident with a Spearman correlation coefficient of 0.99 or higher for simulated data. Water tank results confirm that CE by radiotherapy beams sufficiently stimulates NP photoluminescence. A considerable signal increase was observed near 650 nm with NPs placed at depths up to 1 cm in the tissue‐simulating phantom. Conclusion: These results indicate that development of spectral shifting techniques that enhance tissue transmission and detection of CE during radiotherapy will be beneficial for online tumor imaging and localization, since CE is intrinsic to the beam and non‐ionizing, and for intensity modulation based on tumor microenvironment information, such as oxygenation, contained within the spectral distribution. This setup and methodology will be used to investigate different beam qualities and wavelength‐shifting schemes, and fine‐tune the phantom optical properties. NSERC ‐ Discovery Grant
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 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".