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
Accurate Patient Alignment without Unnecessary Imaging Dose via Synthesizing Patient-specific 3D CT Images from 2D kV Images
Yuzhen Ding, Jason M. Holmes, Hongying Feng, Baoxin Li, Lisa A. McGee, Jean-Claude M. Rwigema, Sujay A. Vora, Daniel J. Ma, Robert L. Foote, Samir H. Patel, Wei Liu
Prior Frequency Guided Diffusion Model for Limited Angle (LA)-CBCT Reconstruction
Jiacheng Xie, Hua-Chieh Shao, Yunxiang Li, You Zhang
T-Mamba: A unified framework with Long-Range Dependency in dual-domain for 2D & 3D Tooth Segmentation
Jing Hao, Yonghui Zhu, Lei He, Moyun Liu, James Kit Hon Tsoi, Kuo Feng Hung