SGAMARN: A GAN Framework for Metal Artifact Reduction in CT Imaging
Bibliographic record
Abstract
Metallic objects cause severe artifacts degrading CT image quality, complicating interpretation, and influencing clinical diagnosis in radiation therapy. This paper introduces a novel Generative Adversarial Network (GAN) framework developed for metal artifact reduction (MAR), incorporating a transformer generator and a conditional discriminator, jointly optimized to improve artifact removal while maintaining anatomical accuracy. We adopt a modified U-Net structure for the generator, featuring hierarchical encoder stages, a bottleneck, and an asymmetric decoder. The encoder utilizes transformer blocks and self-attention mechanisms to extract multi-scale features, while pooling cascading facilitates dense connectivity and gradient flow. The decoder, optimized for computational efficiency, reconstructs artifact reduced images. The discriminator, on the other hand, leverages convolutional blocks with instance normalization to detect residual artifacts and guide the generator towards structural fidelity and artifact suppression.Ablation experiments were performed to determine the effectiveness of the module. Evaluation of the proposed network has been conducted for training using the Deeplesion synthasized Dataset. The Structural Similarity Index Method (SSIM) and the Peak Signal to Noise Ratio (PSNR) were measured for quantitative results which demonstrate that SGAMARN outperforms traditional and deep learning MAR techniques in artifact suppression, structural fidelity, and robustness achieving 7.6 percent improvement in PSNR.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".