Deep learning based approaches have been used to improve image quality in cone-beam computed tomography (CBCT), a medical imaging technique often used in applications such as image-guided radiation therapy, implant dentistry or orthopaedics. In particular, while deep learning methods have been applied to reduce various types of CBCT image artifacts arising from motion, metal objects, or low-dose acquisition, a comprehensive review summarizing the successes and shortcomings of these approaches, with a primary focus on the type of artifacts rather than the architecture of neural networks, is lacking in the literature. In this review, the data generation and simulation pipelines, and artifact reduction techniques are specifically investigated for each type of artifact. We provide an overview of deep learning techniques that have successfully been shown to reduce artifacts in 3D, as well as in time-resolved (4D) CBCT through the use of projection- and/or volume-domain optimizations, or by introducing neural networks directly within the CBCT reconstruction algorithms. Research gaps are identified to suggest avenues for future exploration. One of the key findings of this work is an observed trend towards the use of generative models including GANs and score-based or diffusion models, accompanied with the need for more diverse and open training datasets and simulations.

翻译：基于深度学习的方法已被用于提高锥束计算机断层扫描（CBCT）的图像质量，CBCT是一种常用于图像引导放射治疗、种植牙科或骨科等领域的医学成像技术。尽管深度学习方法已被应用于减少由运动、金属物体或低剂量采集引起的各种CBCT图像伪影，但文献中缺乏一篇全面总结这些方法的成功与不足的综述，且该综述主要关注伪影类型而非神经网络架构。本综述针对每种伪影类型，专门研究了数据生成与模拟管线以及伪影减少技术。我们概述了通过投影域和/或体积域优化，或直接在CBCT重建算法中引入神经网络，已被成功证明能够减少3D以及时间分辨（4D）CBCT伪影的深度学习方法。同时，本文指出了研究空白，为未来探索提供了方向。本研究的核心发现之一是，观察到一种使用生成模型（包括GAN、基于分数或扩散模型）的趋势，同时需要更多样化和开放的训练数据集及模拟工具。