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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".