An Efficient Quality Enhancement Method for Low-Dose CBCT Imaging
Bibliographic record
Abstract
Cone-Beam Computed Tomography (CBCT) is a widely used imaging technique in medical and dental applications. However, low-dose radiation CBCT images are prone to aliasing artifacts, which introduce artifacts in microstructures, degrade image quality, and as a result affect diagnostic accuracy. Existing CBCT image enhancement approaches tend to focus on noise reduction and higher resolution but they fail to address aliasing artifacts. This paper introduces a unique anti-aliasing method specifically designed for low-dose CBCT images. The proposed approach utilizes a Butterworth filter to remove aliasing artifacts in high frequencies, while speckle noise is reduced by a Non-Local Means (NLM) filter. Finally, the overall visual quality is improved by a Laplacian filter which enhances edges while steps are taken to adjust brightness and contrast. Subjective evaluations show that our approach outperforms existing methods by an average of 98.63%, effectively mitigating aliasing without compromising resolution or introducing additional noise, thereby improving the diagnostic reliability of CBCT images and addressing a critical gap in current clinical practice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".