College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China.
J Appl Clin Med Phys. 2020 Dec;21(12):166-177. doi: 10.1002/acm2.13084. Epub 2020 Nov 2.
Cone beam computed tomography (CBCT) offers advantages such as high ray utilization rate, the same spatial resolution within and between slices, and high precision. It is one of the most actively studied topics in international computed tomography (CT) research. However, its application is hindered owing to scatter artifacts. This paper proposes a novel scatter artifact removal algorithm that is based on a convolutional neural network (CNN), where contextual loss is employed as the loss function.
In the proposed method, contextual loss is added to a simple CNN network to correct the CBCT artifacts in the pelvic region. The algorithm aims to learn the mapping from CBCT images to planning CT images. The 627 CBCT-CT pairs of 11 patients were used to train the network, and the proposed algorithm was evaluated in terms of the mean absolute error (MAE), average peak signal-to-noise ratio (PSNR) and so on. The proposed method was compared with other methods to illustrate its effectiveness.
The proposed method can remove artifacts (including streaking, shadowing, and cupping) in the CBCT image. Furthermore, key details such as the internal contours and texture information of the pelvic region are well preserved. Analysis of the average CT number, average MAE, and average PSNR indicated that the proposed method improved the image quality. The test results obtained with the chest data also indicated that the proposed method could be applied to other anatomies.
Although the CBCT-CT image pairs are not completely matched at the pixel level, the method proposed in this paper can effectively correct the artifacts in the CBCT slices and improve the image quality. The average CT number of the regions of interest (including bones, skin) also exhibited a significant improvement. Furthermore, the proposed method can be applied to enhance the performance on such applications as dose estimation and segmentation.
锥形束计算机断层扫描（CBCT）具有射线利用率高、切片内和切片间空间分辨率相同、精度高等优点。它是国际计算机断层扫描（CT）研究中最活跃的研究课题之一。然而，由于散射伪影的存在，其应用受到了阻碍。本文提出了一种基于卷积神经网络（CNN）的新型散射伪影去除算法，其中使用上下文损失作为损失函数。
在提出的方法中，上下文损失被添加到一个简单的 CNN 网络中，以纠正骨盆区域的 CBCT 伪影。该算法旨在学习从 CBCT 图像到计划 CT 图像的映射。该方法使用 11 名患者的 627 对 CBCT-CT 数据对网络进行训练，并从平均绝对误差（MAE）、平均峰值信噪比（PSNR）等方面对所提出的算法进行评估。将所提出的方法与其他方法进行了比较，以说明其有效性。
所提出的方法可以去除 CBCT 图像中的伪影（包括条纹、阴影和杯状）。此外，骨盆区域的内部轮廓和纹理信息等关键细节也得到了很好的保留。对平均 CT 值、平均 MAE 和平均 PSNR 的分析表明，该方法提高了图像质量。使用胸部数据获得的测试结果也表明，该方法可应用于其他解剖结构。
尽管 CBCT-CT 图像对在像素级上不完全匹配，但本文提出的方法可以有效地纠正 CBCT 切片中的伪影，提高图像质量。感兴趣区域（包括骨骼、皮肤）的平均 CT 值也有显著提高。此外，该方法可应用于增强剂量估计和分割等应用的性能。
