Po‐Thur Eve General‐18: Comparison of Imaging Performance: Cone Beam CT versus Conventional CT Simulators
Bibliographic record
Abstract
Cone Beam Computed Tomography (CBCT) is emerging as an alternative modality for radiotherapy treatment simulation and verification. Evaluation of CBCT performance for radiotherapy applications has been confined to in‐house built or modified CBCT devices. In this work, we investigated the imaging performance of a commercial (Varian Acuity) simulator having CBCT capability. The objective of this study was to compare its performance to that of a conventional CT simulator (Picker PQ5000) used for acquiring 3D data for generating radiotherapy treatment plans. Specifically the tests conducted include: density resolution, temporal stability, scan uniformity and spatial linearity, effect of varying scan widths, resolution (high and low contrast), and noise measurements, performed with a Catphan phantom. The variation in density resolution that is representative of the linearity in CT numbers was negligible for both modalities except for high density materials. Operational stability monitored by routinely tracking the CT numbers showed variations <14%, which we predict based on other studies, will result in only <2% uncertainty in dose calculations. Spatial resolution measurements showed better resolution capability for CBCT unlike for low contrast resolution, which was better for conventional CT. These results indicate that CBCT is comparable to CT and demonstrates its potential for treatment planning applications. We will explore this possibility further in future work. Currently, neither AAPM nor CAPCA, have standards for evaluating CBCT performance tolerances. Hence, we also anticipate that this study will provide guidelines for these documents when establishing tolerances for using commercial CBCT simulators.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".