Cone Beam Computed Tomography Image

Cone beam computed tomography (CBCT) images are crucial in dentistry and other medical fields, providing detailed 3D anatomical information. Current research heavily focuses on improving image quality by mitigating artifacts (e.g., metal artifacts, motion blur) using deep learning techniques, particularly generative adversarial networks (GANs), transformers, and U-Net architectures, often incorporating physics-based simulation for data augmentation. These advancements aim to enhance diagnostic accuracy and treatment planning by improving the reliability and precision of CBCT-based analyses, impacting various applications from dental implant placement to radiation therapy.