TH-C-17A-04: Shining Light On the Implementation of Cherenkov Emission in Radiation Therapy
Bibliographic record
Abstract
Purpose: We hypothesize that Cherenkov emission (CE) by radiotherapy beams is correlated with radiation dose, CE detection can be maximized by a spectral shift towards the near-infrared (NIR) window of biological tissue, and in certain tissue types (ex. breast/oropharynx), it could prove superior to mega-voltage (MV) imaging. Therefore, we compare CE imaging to onboard MV imaging. Methods: Dose-CE correlation was investigated via simulation and experiment. A Monte Carlo (MC) CE simulator was designed using Geant4. Experimental phantoms include: water; tissuesimulating phantom composed of water, fat emulsion, and beef blood; plastic phantom with solid water insert. The optical spectrometry system consisted of a multi-mode optical fiber and diffraction-grating spectrometer incorporating a front/back-illuminated charge-coupled device (CCD). CdSe/ZnS quantum dots (QDs), emitting at (650±10) nm, were used to achieve NIR shift of the CE signal. CE and MV images were acquired with a complementary metal-oxide-semiconductor (CMOS) camera and an electronic portal imaging device (EPID), respectively. Results: MC and experimental studies indicate a strong linear correlation between radiation dose and CE (Pearson coefficient > 0.99). CE by an 18 MeV beam was effectively shifted towards the NIR in water and in a tissue-simulating phantom, exhibiting a 50% increase at 650 nm for QD depths of ∼3 mm. CE images exhibited relative contrast superior to EPID images by a factor of 30. Conclusion: Our work supports the potential for application of CE in radiotherapy online imaging for patient setup and treatment verification, since CE is intrinsic to the beam and non-ionizing, and QDs can be used to improve CE detectability, yielding image quality superior to MV imaging for the case of low density variability, low optical attenuation materials, such as breast or oropharyngeal cavities. Ongoing work involves microenvironment functionalization of QDs and application of multichannel spectrometry for simultaneous acquisition of dosimetric and tumor oxygenation signals. Funding received from the following organizations: Natural Sciences and Engineering Research Council of Canada, McGill University. YZ acknowledges partial support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council (Grant number: 432290).
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".