Hegazy Mohamed A A, Cho Myung Hye, Cho Min Hyoung, Lee Soo Yeol
Department of Biomedical Engineering, Kyung Hee University, Yongin, Korea.
Biomed Eng Lett. 2019 Apr 29;9(3):375-385. doi: 10.1007/s13534-019-00110-2. eCollection 2019 Aug.
Unlike medical computed tomography (CT), dental CT often suffers from severe metal artifacts stemming from high-density materials employed for dental prostheses. Despite the many metal artifact reduction (MAR) methods available for medical CT, those methods do not sufficiently reduce metal artifacts in dental CT images because MAR performance is often compromised by the enamel layer of teeth, whose X-ray attenuation coefficient is not so different from that of prosthetic materials. We propose a deep learning-based metal segmentation method on the projection domain to improve MAR performance in dental CT. We adopted a simplified U-net for metal segmentation on the projection domain without using any information from the metal-artifacts-corrupted CT images. After training the network with the projection data of five patients, we segmented the metal objects on the projection data of other patients using the trained network parameters. With the segmentation results, we corrected the projection data by applying region filling inside the segmented region. We fused two CT images, one from the corrected projection data and the other from the original raw projection data, and then we forward-projected the fused CT image to get the fused projection data. To get the final corrected projection data, we replaced the metal regions in the original projection data with the ones in the fused projection data. To evaluate the efficacy of the proposed segmentation method on MAR, we compared the MAR performance of the proposed segmentation method with a conventional MAR method based on metal segmentation on the CT image domain. For the MAR performance evaluation, we considered the three primary MAR performance metrics: the relative error (REL), the sum of square difference (SSD), and the normalized absolute difference (NAD). The proposed segmentation method improved MAR performances by around 5.7% for REL, 6.8% for SSD, and 8.2% for NAD. The proposed metal segmentation method on the projection domain showed better MAR performance than the conventional segmentation on the CT image domain. We expect that the proposed segmentation method can improve the performance of the existing MAR methods that are based on metal segmentation on the CT image domain.
与医学计算机断层扫描（CT）不同，牙科CT常常受到源自牙科修复体所用高密度材料的严重金属伪影的影响。尽管有许多用于医学CT的金属伪影减少（MAR）方法，但这些方法在牙科CT图像中并不能充分减少金属伪影，因为MAR性能常常受到牙齿釉质层的影响，其X射线衰减系数与修复材料的衰减系数差异不大。我们提出一种基于深度学习的投影域金属分割方法，以提高牙科CT中的MAR性能。我们采用了一种简化的U-net在投影域进行金属分割，而不使用来自金属伪影损坏的CT图像的任何信息。在用五名患者的投影数据训练网络后，我们使用训练好的网络参数对其他患者的投影数据中的金属物体进行分割。根据分割结果，我们通过在分割区域内应用区域填充来校正投影数据。我们融合了两张CT图像，一张来自校正后的投影数据，另一张来自原始的原始投影数据，然后我们对融合后的CT图像进行前向投影以获得融合后的投影数据。为了获得最终校正后的投影数据，我们用融合后的投影数据中的金属区域替换原始投影数据中的金属区域。为了评估所提出的分割方法对MAR的有效性，我们将所提出的分割方法的MAR性能与基于CT图像域金属分割的传统MAR方法进行了比较。对于MAR性能评估，我们考虑了三个主要的MAR性能指标：相对误差（REL）、平方差之和（SSD）和归一化绝对差（NAD）。所提出的分割方法在REL方面将MAR性能提高了约5.7%，在SSD方面提高了6.8%，在NAD方面提高了8.2%。所提出的投影域金属分割方法在MAR性能上比CT图像域的传统分割方法表现更好。我们期望所提出的分割方法能够提高基于CT图像域金属分割的现有MAR方法的性能。
