Cupping correction for partial rotation dental conebeam CT
Bibliographic record
Abstract
Dental cone beam computed tomography (CBCT) offers a variety of fields of view (FOV), ranging from the entire craniofacial complex down to an individual tooth. To manage the radiation dose to the patient, partial rotation scanning is often used. However, the images suffer from non-uniformities related to attenuation of the beam as it crosses the patient anatomy and the uneven radiation field due in part to scattered radiation. We have modelled the cupping artifact in the maxillofacial field of view and applied a correction to improve uniformity in the image. We applied this correction to smaller fields of view. Images were obtained using a Carestream 9300 CBCT system of the SEDENTEX image quality phantom and 2 anthropomorphic skull phantoms. The maxillofacial FOV (17x11 cm2 ) was used with clinical settings for an average adult (90 kVp, 4 mA, 6.4 s) and reconstructed with 0.25 mm voxel spacing. In MATLAB, we modeled the 2D surface across a uniform section of the SEDENTEX phantom with a polynomial fit to find an offset that could be added to reduce the cupping artefact. This offset was then applied to the images of the anthropomorphic and SEDENTEX phantoms for 17x11 cm2 , 10x10 cm2 , 10x5 cm2 and 8x8 cm2 FOVs. The offset was cropped for the 10x10 cm2 , 10x5 cm2 and 8x8 cm2 FOVs. For all images, the uniformity was improved. From this study, we conclude that a single correction matrix can be used for all 4 FOVs on this machine to improve image quality.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".