SU‐E‐I‐59: Image Quality and Dose Measurement for Partial Cone‐Beam CT
Bibliographic record
Abstract
Purpose: To characterize performance of cone beam CT (CBCT) used in dentistry investigating quantitatively the image quality and radiation dose during dental CBCT over different settings for partial rotation of the x‐ray tube. Methods: Image quality and dose measurements were done on a variable field of view (FOV) dental CBCT (Carestream 9300). X‐ray parameters for clinical settings were adjustable for 2–10 mA, 60–90 kVp, and two optional voxel size values, but time was fixed for each FOV. Image quality was assessed by scanning cylindrical poly‐methyl methacrylate (PMMA) image quality phantom (SEDENTEXCT IQ), and then the images were analyzed using ImageJ to calculate image quality parameters such as noise, uniformity, and contrast to noise ratio (CNR). A protocol proposed by SEDENTEXCT, dose index 1 (DI1), was applied to dose measurements obtained using a thimble ionization chamber and cylindrical PMMA dose index phantom (SEDENTEXCT DI). Dose distributions were obtained using Gafchromic film. The phantoms were positioned in the FOV to imitate a clinical positioning. Results: The image noise was 6–12.5% which, when normalized to the difference of mean voxel value of PMMA and air, was comparable between different FOVs. Uniformity was 93.5ߝ99.7% across the images. CNR was 1.7–4.2 and 6.3–14.3 for LDPE and Aluminum, respectively. Dose distributions were symmetric about the rotation angle's bisector. For large and medium FOVs at 4 mA and 80–90 kVp, DI1 values were in the range of 1.26–3.23 mGy. DI1 values were between 1.01–1.93 mGy for small FOV (5×5 cm2) at 4–5 mA and 75–84 kVp. Conclusion: Noise decreased by increasing kVp, and the CNR increased for each FOV. When FOV size increased, image noise increased and CNR decreased. DI1 values were increased by increasing tube current (mA), tube voltage (kVp), and/or FOV. Funding for this project from NSERC Discovery grant, UBC Faculty of Dentistry Research Equipment Grant and UBC Faculty of Dentistry S. Wah Leung Endowment Fund.
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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) | 0.000 | 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) | 0.001 | 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".