Unsupervised Metal Artifact Reduction in Dental CBCT using Fine-tuned Cycle-Consistent Adversarial Networks

2026-07-23

AI Summary

This study addresses the severe metal artifacts induced by dental implants in cone-beam computed tomography (CBCT), which significantly impair anatomical visualization and diagnostic accuracy. The authors propose an unsupervised deep learning framework based on a fine-tuned CycleGAN to achieve high-fidelity artifact removal without requiring paired training data. This approach represents the first successful application of unpaired CBCT data for high-quality metal artifact reduction, effectively suppressing hallucinatory structures while preserving dental morphology. Employing a U-Net generator and PatchGAN discriminator architecture trained on approximately 4,000 unpaired images, the method achieves a 34.6% improvement in BRISQUE score, reduces the Fréchet Inception Distance (FID) from 207.03 to 157.04, attains an SSIM of 0.9105, and requires only 3.03 milliseconds per slice for inference—demonstrating strong potential for clinical human-AI collaborative decision-making.

Abstract

Metal artifacts generated by dental implants significantly degrade cone-beam computed tomography (CBCT) volumes, obscuring critical anatomical structures and compromising diagnostic precision. To address this, an unsupervised deep learning framework has been proposed for Metal Artifact Reduction (MAR) utilizing a Cycle-Consistent Adversarial Network (CycleGAN) optimized for high-fidelity restoration. Unlike supervised methods that rely on unattainable voxel-aligned paired datasets, the proposed approach leverages an unpaired dataset of approximately 4,000 images, curated from the public ToothFairy dataset. The architecture integrates U-Net-based generators and PatchGAN discriminators, specifically tuned to mitigate generative hallucinations and preserve morphological integrity. Quantitative benchmarking on a held-out test set demonstrates a 34.6\% improvement in the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) score, a substantial reduction in Fréchet Inception Distance (FID) from 207.03 to 157.04, and a superior Structural Similarity Index Measure (SSIM) of 0.9105. The framework achieves real-time efficiency with a 3.03 ms inference time per slice, effectively suppressing artifacts while preserving anatomical detail. Expert validation confirms high fidelity; however, to ensure reliability in extreme cases, the architecture is recommended as a clinical decision-support tool under human-in-the-loop oversight. By enhancing diagnostic clarity via a scalable software pipeline, this study provides a robust solution for high-fidelity dental implant imaging.

Problem

• Metal Artifact Reduction
• Dental CBCT
• Image Quality Degradation
• Metal Implants
• Diagnostic Precision

Innovation

• Unsupervised Metal Artifact Reduction
• CycleGAN
• Dental CBCT
• U-Net Generator
• High-Fidelity Image Restoration