Optimization of cone beam computed tomography image quality in implant dentistry
Bibliographic record
Abstract
Abstract This study was conducted to optimize the cone beam computed tomography image quality in implant dentistry using both clinical and quantitative image quality evaluation with measurement of the radiation dose. A natural bone human skull phantom and an image quality phantom were used to evaluate the images produced after changing the exposure parameters (kVp and mA). A 10 × 5 cm 2 field of view was selected for average adult. Five scans were taken with varying kVp (70–90 kVp) first at fixed 4 mA. After assessment of the scans and selecting the best kVp, nine scans were taken with 2–12 mA, and the kVp was fixed at the optimal value. A clinical assessment of the implant‐related anatomical landmarks was done in random order by two blinded examiners. Quantitative image quality was assessed for noise/uniformity, artifact added value, contrast‐to‐noise ratio, spatial resolution, and geometrical distortion. A dosimetry index phantom and thimble ion chamber were used to measure the absorbed dose for each scan setting. The anatomical landmarks of the maxilla had good image quality at all kVp settings. To produce good quality images, the mandibular landmarks demanded higher exposure parameters than the maxillary landmarks. The quantitative image quality values were acceptable at all selected exposure settings. Changing the exposure parameters does not necessarily produce higher image quality outcomes but does affect the radiation dose to the patient. The image quality could be optimized for implant treatment planning at lower exposure settings and dose than the default settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".