Cone-beam computed tomography (CBCT) is widely used in dentistry, surgery, radiotherapy and other medical fields. However, repeated CBCT scans expose patients to additional radiation doses, increasing the risk of secondary malignant tumors. Low-dose CBCT image reconstruction technology, which employs advanced algorithms to reduce radiation dose while enhancing image quality, has emerged as a focal point of recent research. This review systematically examined deep learning-based methods for low-dose CBCT reconstruction. It compared different network architectures in terms of noise reduction, artifact removal, detail preservation, and computational efficiency, covering three approaches: image-domain, projection-domain, and dual-domain techniques. The review also explored how emerging technologies like multimodal fusion and self-supervised learning could enhance these methods. By summarizing the strengths and weaknesses of current approaches, this work provides insights to optimize low-dose CBCT algorithms and support their clinical adoption.
锥形束计算机断层扫描（CBCT）在牙科、外科、放射治疗等领域应用广泛，但反复CBCT扫描会给患者带来额外的辐射剂量，增加继发性恶性肿瘤的发病风险。低剂量CBCT图像重建技术通过先进算法在减少辐射剂量的同时提升图像质量，成为近年研究热点。本文系统综述了基于深度学习的低剂量CBCT重建方法，从图像域、投影域及图像-投影域协同三个方向，对比分析不同网络架构在噪声抑制、伪影消除、细节保留及计算效率等方面的性能差异，并探讨与多模态融合、自监督学习等新兴技术结合的应用前景。通过总结现有方法的优势与局限性，本文为优化低剂量CBCT重建算法、推动其临床转化提供了理论依据与技术参考。.
Keywords: Deep learning; Image reconstruction; Low-dose cone-beam computed tomography.