Cone Beam Computed Tomography
Cone beam computed tomography (CBCT) is a medical imaging technique providing detailed 3D images, primarily used in dentistry and radiation therapy, with applications expanding to other fields. Current research heavily focuses on improving CBCT image quality through artifact reduction (e.g., motion artifacts, metal artifacts) and enhancing reconstruction accuracy using deep learning methods, including diffusion models, generative adversarial networks (GANs), and transformers. These advancements aim to improve diagnostic accuracy, treatment planning, and reduce radiation exposure, ultimately impacting clinical workflows and patient care.
Papers
Metal-conscious Embedding for CBCT Projection Inpainting
Fuxin Fan, Yangkong Wang, Ludwig Ritschl, Ramyar Biniazan, Marcel Beister, Björn Kreher, Yixing Huang, Steffen Kappler, Andreas Maier
Enhanced artificial intelligence-based diagnosis using CBCT with internal denoising: Clinical validation for discrimination of fungal ball, sinusitis, and normal cases in the maxillary sinus
Kyungsu Kim, Chae Yeon Lim, Joong Bo Shin, Myung Jin Chung, Yong Gi Jung