Cone Beam Computed Tomography (CBCT) is widely used in dentistry for diagnostics and treatment planning. CBCT Imaging has a long acquisition time and consequently, the patient is likely to move. This motion causes significant artifacts in the reconstructed data which may lead to misdiagnosis. Existing motion correction algorithms only address this issue partially, struggling with inconsistencies due to truncation, accuracy, and execution speed. On the other hand, a short-scan reconstruction using a subset of motion-free projections with appropriate weighting methods can have a sufficient clinical image quality for most diagnostic purposes. Therefore, a framework is used in this study to extract the motion-free part of the scanned projections with which a clean short-scan volume can be reconstructed without using correction algorithms. Motion artifacts are detected using deep learning with a slice-based prediction scheme followed by volume averaging to get the final result. A realistic motion simulation strategy and data augmentation has been implemented to address data scarcity. The framework has been validated by testing it with real motion-affected data while the model was trained only with simulated motion data. This shows the feasibility to apply the proposed framework to a broad variety of motion cases for further research.

翻译：锥束计算机断层扫描（CBCT）广泛应用于牙科诊断与治疗规划。由于CBCT成像采集时间较长，患者容易发生运动。这种运动会在重建数据中引发显著伪影，可能导致误诊。现有运动校正算法仅能部分解决该问题，在截断、精度和运行速度导致的伪影处理方面仍存在困难。另一方面，采用无运动投影子集并通过适当加权方法进行短扫描重建，可生成满足多数诊断需求的临床图像质量。因此，本研究采用一种框架提取扫描投影中的无运动部分，无需校正算法即可重建清洁的短扫描体积。基于深度学习的运动伪影检测采用切片级预测方案，随后通过体积平均获得最终结果。为应对数据稀缺问题，实施了真实运动仿真策略与数据增强方法。该框架通过真实运动影响数据进行测试验证，而模型仅使用仿真运动数据进行训练。结果表明，所提框架能够适用于多种运动场景的进一步研究。