Cone Beam Computed Tomography Data

Cone-beam computed tomography (CBCT) data analysis focuses on improving image quality and extracting clinically relevant information from this widely used, but often artifact-ridden, imaging modality. Current research emphasizes deep learning approaches, particularly employing convolutional neural networks (CNNs) and transformers (like Swin Transformers and U-Nets), to address challenges such as metal artifact reduction, limited field-of-view, and improved segmentation of anatomical structures (e.g., teeth, root canals, liver). These advancements aim to enhance the accuracy and efficiency of diagnoses and treatment planning in various medical fields, including radiation oncology, interventional surgery, and dentistry. The ultimate goal is to leverage CBCT's speed and accessibility while mitigating its inherent limitations for improved patient care.