The influence of bowtie filtration on cone‐beam CT image quality
Bibliographic record
Abstract
The large variation of x-ray fluence at the detector in cone-beam CT (CBCT) poses a significant challenge to detectors' limited dynamic range, resulting in the loss of skinline as well as reduction of CT number accuracy, contrast-to-noise ratio, and image uniformity. The authors investigate the performance of a bowtie filter implemented in a system for image-guided radiation therapy (Elekta oncology system, XVI) as a compensator for improved image quality through fluence modulation, reduction in x-ray scatter, and reduction in patient dose. Dose measurements with and without the bowtie filter were performed on a CTDI Dose phantom and an empirical fit was made to calculate dose for any radial distance from the central axis of the phantom. Regardless of patient size, shape, anatomical site, and field of view, the bowtie filter results in an overall improvement in CT number accuracy, image uniformity, low-contrast detectability, and imaging dose. The implemented bowtie filter offers a significant improvement in imaging performance and is compatible with the current clinical system for image-guided radiation therapy.
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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 | 0.002 | 0.012 |
| 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 | 0.001 |
| 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".