Learning Beyond Generated Targets: A Modified SFNet for CBCT Image Enhancement
Bibliographic record
Abstract
Despite significant advancements in denoising techniques for low-dose Cone-Beam Computed Tomography (CBCT) images, developing robust Artificial Intelligence (AI)-based approaches remains a critical challenge due to the unavailability of real paired CBCT datasets. While various methods have been proposed to enhance low-dose CBCT images, their effectiveness is limited by the absence of paired datasets and the use of CT images as substitutes for high-dose CBCTs, leaving room for further improvement. In this paper, we first introduce a novel CBCT dataset in which high-dose target images are synthetically generated from low-dose scans using an advanced enhancement method. Additionally, we propose a modified Selective Frequency Network (SFNet) that better captures CBCT-specific features by integrating Convolutional Block Attention Modules (CBAMs) after Residual Blocks (ResBlocks) and features an improved hierarchical shallow layer for enhanced feature extraction. Objective evaluations using full-reference and no-reference metrics demonstrate that our modified SFNet surpasses the state-of-the-art CBCT denoising approach. More importantly, our model produces CBCT images of even higher quality than the realistic high-dose targets generated from low-dose CBCT for training, as it learns a more refined mapping that reduces residual artifacts present in generated and real high-dose images. This advancement significantly improves CBCT image quality while minimizing radiation exposure, enhancing diagnostic reliability, and broadening clinical applications.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".