Metal artifact correction is a challenging problem in cone beam computed tomography (CBCT) scanning. Metal implants inserted into the anatomy cause severe artifacts in reconstructed images. Widely used inpainting-based metal artifact reduction (MAR) methods require segmentation of metal traces in the projections as a first step, which is a challenging task. One approach is to use a deep learning method to segment metals in the projections. However, the success of deep learning methods is limited by the availability of realistic training data. It is laborious and time consuming to get reliable ground truth annotations due to unclear implant boundaries and large numbers of projections. We propose to use X-ray simulations to generate synthetic metal segmentation training dataset from clinical CBCT scans. We compare the effect of simulations with different numbers of photons and also compare several training strategies to augment the available data. We compare our model's performance on real clinical scans with conventional region growing threshold-based MAR, moving metal artifact reduction method, and a recent deep learning method. We show that simulations with relatively small number of photons are suitable for the metal segmentation task and that training the deep learning model with full size and cropped projections together improves the robustness of the model. We show substantial improvement in the image quality affected by severe motion, voxel size under-sampling, and out-of-FOV metals. Our method can be easily integrated into the existing projection-based MAR pipeline to get improved image quality. This method can provide a novel paradigm to accurately segment metals in CBCT projections.

翻译：金属伪影校正是锥束计算机断层扫描（CBCT）中的一项挑战性难题。植入解剖结构的金属植入物会在重建图像中引发严重伪影。广泛应用的基于修补的金属伪影抑制（MAR）方法需要首先在投影中分割金属迹线，这原本就是一项困难任务。一种方法采用深度学习技术对投影中的金属进行分割，但深度学习方法的成功受限于真实训练数据的可用性。由于植入物边界不清晰且投影数量庞大，获取可靠的真实标注数据既费时又费力。我们提出利用X射线模拟从临床CBCT扫描生成合成金属分割训练数据集。我们比较了不同光子数模拟的效果，并对比了多种增强可用数据的训练策略。我们将模型在真实临床扫描中的性能与传统区域生长阈值MAR、移动金属伪影抑制方法以及最新深度学习方法进行比较。研究表明，使用相对较少光子数的模拟适用于金属分割任务，且同时采用全尺寸和裁剪投影训练深度学习模型可提高其鲁棒性。我们展示了该方法在严重运动伪影、体素尺寸欠采样及视场外金属影响下的图像质量显著提升。该方法可轻松集成至现有基于投影的MAR流水线中以获得更优图像质量，为精确分割CBCT投影中的金属提供了新型范式。