Optimization of exposure parameters for cone beam computed tomography sialography
Bibliographic record
Abstract
OBJECTIVES: The assessment of image quality is a crucial step in the development of a new imaging protocol. Having proposed and reported on a preliminary protocol for sialography using cone beam CT (CBCT), the purpose of this study was to further optimize this protocol by maximizing the image signal difference-to-noise ratio (SDNR) and to relate these new data to previously published dosimetric data for CBCT sialography. METHODS: An imaging phantom was constructed using samples with different concentrations of iodine and a water-immersed mandible. The CB MercuRay (Hitachi Medical Systems, Tokyo, Japan) was used to image the phantom using different peak kilovoltage (kVp) and milliamperage (mA) settings. SDNR was then calculated using the raw images based on mean pixel values (MPV) measured in selected regions of interest (ROI). Finally, a figure of merit (FOM) was calculated to examine the trade-off between image SDNR and effective radiation dose. RESULTS: The SDNR demonstrated an expected increase as the kVp increased from 60 to 120. Also, images made with the higher mA setting (15) had greater SDNR. The iodine concentration also influenced the image quality such that SDNR increased with increased amounts of iodine. The calculated FOM was greatest for the technique using 80 kVp, with equivalent results for 10 mA and 15 mA. CONCLUSION: An optimized protocol for CBCT sialography using CB MercuRay entails a 6 inch field of view with 80 kVp and 10 mA.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".