Data of "Multi-institutional study on image quality for a novel CBCT solution on O-ring linac"
Bibliographic record
Abstract
Data of "Multi-institutional study on image quality for a novel CBCT solution on O-ring linac". Abstract Introduction: This work presents a multi-institutional study on image quality provided by a novel cone beam computed tomography (CBCT). The main goal is to investigate the consistency of imaging performance across multiple institutions. Methods: Phantoms for measuring relative electron density (RED) and image quality were sent to six institutions for imaging on Ethos and Halcyon units equipped with HyperSight CBCT. The imaging protocols included tube potential from 100 to 140 kVp and exposure from 80 to 800 mAs. Imaging performance was evaluated with regard to RED versus Hounsfield Units (HU), uniformity, contrast-to-noise ratio (CNR), slice thickness, circular symmetry, Modulation Transfer Function (MTF), and spatial resolution. Results: Among all institutions, some variability was observed among institutions in the RED-to-HU relationship, especially for RED values greater than 1, although no outliers were found (|z-score|<2 in all cases). In this range, RED/HU slopes were 475 ± 25 10-6 RED/HU at 100kVp, 505 ± 20 10-6 RED/HU at 125kVp, and 550 ± 20 10-6 RED/HU at 140kVp. Radial uniformity ranged from 1 to 7 HU, depending on protocol. Circular symmetry for two points 50 mm apart showed consistency within one-pixel dimension. Integral non-uniformity was between 1 to 10, with no difference observed between vertical and horizontal dimensions. Contrast rods with 1 % gave CNR=0.5, 1 and 2 for 100(88), 125(176) and 140(528) in kVp(mAs), and contrast rods with 0.5 % had CNR=0.2, 0.4 and 0.8 for 100(88), 125(176) and 140(528) in kVp(mAs). Spatial resolution given by MTF at 10 % and 50 % yielded values of 0.55 ± 0.01 mm-1 and 0.35 ± 0.02 mm-1, respectively. Conclusions: This multi-institutional analysis of CBCT imaging performance showed consistency in radial uniformity, circular symmetry, integral non-uniformity, contrast and spatial resolution. Some variability was seen in the RED-to-HU relationship for RED>1 depending on exposure. More data from different institutions would be necessary to establish more robust statistical metrics which ensure quality parameters. Agulles-Pedrós L, MacDonald RL, Cherpak AJ, et al.Multi-institutional study on image quality for a novel CBCT solution on O-ring linac. J Appl Clin Med Phys. 2025;e70023. https://doi.org/10.1002/acm2.70023
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".