Quantitative performance characterization of image quality and radiation dose for a CS 9300 dental cone beam computed tomography machine
Bibliographic record
Abstract
This paper aims to characterize the radiation dose and image quality (IQ) performance of a dental cone beam computed tomography (CBCT) unit over a range of fields of view (FOV). IQ and dose were measured using a Carestream 9300 dental CBCT. Phantoms were positioned in the FOV to imitate clinical positioning. IQ was assessed by scanning a SEDENTEXCT IQ phantom, and images were analyzed in ImageJ. Dose index 1 was obtained using a thimble ionization chamber and SEDENTEXCT DI phantom. Mean gray values agreed within 93.5% to 99.7% across the images, with pixel-to-pixel fluctuations of 6% to 12.5%, with poorer uniformity and increased noise for child protocols. CNR was fairly constant across FOVs, with higher CNR for larger patient settings. The measured limiting spatial resolution agreed well with 10% MTF and bar pattern measurements. Dose was reduced for smaller patient settings within a given FOV; however, smaller FOVs obtained with different acquisition settings did not necessarily result in reduced dose. The use of patient-specific acquisition settings decreased the radiation dose for smaller patients, with minimal impact on the IQ. The full set of IQ and dose measurements is reported to allow dental professionals to compare the different FOV settings for clinical use.
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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 | 0.003 | 0.009 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".