Imaging with Iridium photons: an application in brachytherapy
Bibliographic record
Abstract
In external-beam radiotherapy efforts are currently devoted to research on image-guided verification techniques. In brachytherapy the situation is far less advanced; usually, there is no treatment verification imaging. We are studying the possibility to use the photons emitted from a conventional 192Ir brachytherapy source for High Dose Rate (HDR) treatments, when inserted in a patient. We investigated whether the images can be used for dose delivery verification and to interrupt faulty dose deliveries. Phantoms were built to accommodate a remote controlled HDR 192Ir source. Images were collected with an x-ray intensifier, and predicted from calculations based on ray-tracing. For a bone/tissue/air/lung phantom with the source on top of the phantom measured contrasts were 8% (bone/tissue), 19% (tissue/lung) and 26% (lung/bone). When a thick Lucite slab was added on top of the contrast phantom, the contrasts decreased to 3, 7 and 10%, respectively, indicating that phantom scatter is an important issue. Differences between measured and simulated images and the influence of scatter were quantified. From this feasibility study it is concluded that imaging with 192Ir photons is possible but that work on scatter rejection through simulation and anti-scatter grids is needed.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".