Shipeng Xie1, Yingjuan Liang1, Tao Yang1, Zhenrong Song1. 1. College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China.
Abstract
PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Authors: A Sisniega; W Zbijewski; J Xu; H Dang; J W Stayman; J Yorkston; N Aygun; V Koliatsos; J H Siewerdsen Journal: Phys Med Biol Date: 2015-01-22 Impact factor: 3.609
Authors: Joseph Harms; Yang Lei; Tonghe Wang; Rongxiao Zhang; Jun Zhou; Xiangyang Tang; Walter J Curran; Tian Liu; Xiaofeng Yang Journal: Med Phys Date: 2019-07-17 Impact factor: 4.071
Authors: Yuan Xu; Ti Bai; Hao Yan; Luo Ouyang; Arnold Pompos; Jing Wang; Linghong Zhou; Steve B Jiang; Xun Jia Journal: Phys Med Biol Date: 2015-04-10 Impact factor: 3.609