Poster — Thur Eve — 18: Characterization of a camera and LED lightbox imaging system for radiochromic film dosimetry
Bibliographic record
Abstract
Radiation therapy treatment modalities continue to develop and have become increasingly complex. With this, dose verification and quality assurance (QA) is of great importance to ensure that a prescribed dose is accurately and precisely delivered to a patient. Radiochromic film dosimetry has been adopted as a convenient option for QA, because it is relatively energy independent, is near tissue equivalent, and has high spatial resolution. Unfortunately, it is not always easy to use. In this study, preliminary work towards developing a novel method of imaging radiochromic film is presented. The setup consists of a camera mounted vertically above a lightbox containing red LEDs, interfaced with computer image acquisition software. Imaging results from this system will be compared with imaging performed using an Epson Expression 10000XL scanner (a device in common clinical use). The lightbox imaging technique with camera readout is much faster relative to a flatbed scanner. The film measurements made using the camera are independent of film orientation, and show reduced artifacts, so that there are fewer corrections required compared to the use of flatbed scanners. Optical scatter also appears to be less of an issue with this design than with the flat bed scanner. While further work needs to be done to optimize the lightbox imaging system, the lightbox system shows great promise for a rapid, simple, and orientation independent setup, improving on existing film scanning systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".