A novel alignment device for cone beam computed tomography: principle and application
Bibliographic record
Abstract
OBJECTIVES: the aim of this investigation was to optimize the positioning and size of the scanned field of view (FOV) in cone beam CT (CBCT) scanners using a practical external alignment device fitted in the patient's mouth in order to train radiographers and reduce radiation dose to the patient. This is particularly challenging when using small FOVs to cover small volumes of interest. METHODS: test objects were positioned and scanned using the aligner to show that the design and geometry were correct and help the radiographer to superimpose the scanner and the volume of interest axis of rotation. An in vivo study was then undertaken comparing the accuracy of patient positioning when using the aligner, instead of scouts, to position the patient for small FOV (cylinders of 4-8 cm height and 4-8 cm diameter) dental scans. The scanners used were the Accuitomo F170 CBCT scanner (Morita, Kyoto, Japan) and the iCAT Next Gen CBCT scanner (Imaging Sciences, Hatfield, PA). RESULTS: there was no significant difference in positioning the patient when using the aligner compared with the scout images. CONCLUSIONS: it is possible to rely on the aligner for patient positioning for a small volume scan and therefore spare the radiation dose associated with scout imaging.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".