Digital Health and AI: Improving CBCT Image Quality
Description
The objective of this project is to enhance the visual quality of CBCT images captured with low radiation levels, ultimately advancing the dental diagnosis and treatment planning process. This improvement will be achieved through the application of state-of-the-art image processing and Artificial Intelligence (AI) techniques. The primary goals are to eliminate noise resulting from low-dose imaging, enhance spatial resolution, and elevate contrast and brightness to optimize the diagnostic and planning capabilities in dentistry. In this phase of the research, our team will focus on developing innovative, commercially viable technologies that span the entire process of enhancing, rendering, and displaying CBCT images, which will have broad-ranging applications within the dental industry. Denoising techniques designed for CBCT can be adapted for CT and MRI imaging due to the shared challenges these modalities face, such as noise, resolution limitations, and the need for optimal brightness and contrast adjustments, especially in low-dose or low-SNR settings.
Researchers
Selected Publications
- S. Mirzaei, H. R. Tohidypour, and P. Nasiopoulos, “A Hybrid Technique for Visual Quality Enhancement of Noisy Low-Dose CBCT Images”, IEEE Transactions on Consumer Electronics, under final review.
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A Novel Anti-Aliasing and Quality Enhancement Method for Low-Dose CBCT Imaging Using Combined Frequency and Spatial Domain Techniques. International Conference on Computing in Natural Sciences, Biomedicine and Engineering, 2024.