Poster — Thur Eve — 44: Digital tomosynthesis image quality in a Co‐60 treatment beam
Bibliographic record
Abstract
Image guidance capability is an important feature of modern radiotherapy machines. Cobalt-60 units will require some form of image guidance if they are to be brought up to modern standards. Imaging in the treatment beam is an appealing option, for reasons of simplicity and cost, but the dose needed to produce cone beam CT images in a Co-60 treatment beam is too high for this modality to be clinically useful. Digital tomosynthesis (DT) offers a quasi-3D image, of sufficient quality to identify bony anatomy or fiducial markers, while delivering a much lower dose than CBCT. A series of experiments were conducted on a prototype Co-60 cone beam imaging system to quantify the resolution, selectivity, geometric accuracy and contrast sensitivity of Co-60 DT. Although the resolution was severely limited by the penumbra cast by the ∼2cm diameter source, it was possible to identify high contrast objects on the order of 1 mm in width, and bony anatomy in anthropomorphic phantoms was clearly recognizable. Low contrast sensitivity down to electron density differences of 3% was obtained, for uniform features of similar thickness. The conventional shift-and-add algorithm was compared to the FDK filtered backprojection algorithm using several different spatial filters. The Co-60 DT images were obtained with a total dose of 5 to 15 cGy. We conclude that, should Co-60 radiotherapy units be upgraded with image guidance capabilities, filtered backprojection DT in the treatment beam is a versatile and promising modality that would be well suited to the task of patient positioning.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".