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Improving resolution of panoramic radiographs: super-resolution concept
Mahmut Emin Çelik1,2, Mahsa Mikaeili2, Berrin Çelik3
1Electrical Electronics Engineering Department, Faculty of Engineering, Gazi University, Ankara, Eti Mh. Yükselis sk. No:5, 06570, Turkey.
Dento Maxillo Facial Radiology
|March 14, 2024
Summary
Deep learning super-resolution enhances dental panoramic radiographs, improving diagnostic accuracy. The Super-Resolution Convolutional Neural Network (SRCNN) model demonstrated superior performance in reconstructing high-resolution dental images.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Dentistry
Background:
- Dental imaging is crucial for diagnosis and treatment.
- Limitations in radiograph quality can impede precise analysis.
- Deep learning super-resolution offers a novel approach to enhance image resolution.
Purpose of the Study:
- To enhance the resolution of dental panoramic radiographs using deep learning.
- To enable more accurate diagnoses and treatment planning through improved image quality.
Main Methods:
- Utilized 1714 panoramic radiographs from open datasets for training and testing.
- Evaluated four deep learning models: SRCNN, Efficient Sub-Pixel Convolutional Neural Network, Super-Resolution Generative Adversarial Network, and Autoencoder.
- Assessed performance using Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) across scales of 2, 4, and 8.
Main Results:
- Achieved SSIM scores between 0.82-0.98 and PSNR values between 28.7-40.2.
- The Super-Resolution Convolutional Neural Network (SRCNN) model exhibited the best performance.
- Image quality enhancement decreased with higher scaling factors.
Conclusions:
- Deep learning super-resolution significantly improves dental panoramic radiograph quality and detail.
- Enhanced image interpretability aids in more accurate dental diagnoses and treatment planning.
