A Comparison Study Between CNN-Based Deformed Planning CT and CycleGAN-Based Synthetic CT Methods for Improving iCBCT Image Quality

Frontiers in Oncology

Oncology

Zhiqun Wang¹  Yongguang Liang¹  Bo Yang¹  Jie Qiu¹  Yankui Chang²  Xie George Xu³  Xi Pei⁴ 

[1] Department of Radiation Oncology, Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, China;School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, China;School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, China;Department of Radiation Oncology, First Affiliated Hospital of University of Science and Technology of China, Hefei, China;School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, China;Technology Development Department, Anhui Wisdom Technology Co., Ltd., Hefei, China;

关键词: iCBCT;    registration;    sCT generation;    pelvic;    CycleGAN;   

DOI  :  10.3389/fonc.2022.896795

 received in 2022-03-15, accepted in 2022-04-27,  发布年份 2022

来源: Frontiers

【 摘 要 】

PurposeThe aim of this study is to compare two methods for improving the image quality of the Varian Halcyon cone-beam CT (iCBCT) system through the deformed planning CT (dpCT) based on the convolutional neural network (CNN) and the synthetic CT (sCT) generation based on the cycle-consistent generative adversarial network (CycleGAN).MethodsA total of 190 paired pelvic CT and iCBCT image datasets were included in the study, out of which 150 were used for model training and the remaining 40 were used for model testing. For the registration network, we proposed a 3D multi-stage registration network (MSnet) to deform planning CT images to agree with iCBCT images, and the contours from CT images were propagated to the corresponding iCBCT images through a deformation matrix. The overlap between the deformed contours (dpCT) and the fixed contours (iCBCT) was calculated for purposes of evaluating the registration accuracy. For the sCT generation, we trained the 2D CycleGAN using the deformation-registered CT-iCBCT slicers and generated the sCT with corresponding iCBCT image data. Then, on sCT images, physicians re-delineated the contours that were compared with contours of manually delineated iCBCT images. The organs for contour comparison included the bladder, spinal cord, femoral head left, femoral head right, and bone marrow. The dice similarity coefficient (DSC) was used to evaluate the accuracy of registration and the accuracy of sCT generation.ResultsThe DSC values of the registration and sCT generation were found to be 0.769 and 0.884 for the bladder (p < 0.05), 0.765 and 0.850 for the spinal cord (p < 0.05), 0.918 and 0.923 for the femoral head left (p > 0.05), 0.916 and 0.921 for the femoral head right (p > 0.05), and 0.878 and 0.916 for the bone marrow (p < 0.05), respectively. When the bladder volume difference in planning CT and iCBCT scans was more than double, the accuracy of sCT generation was significantly better than that of registration (DSC of bladder: 0.859 vs. 0.596, p < 0.05).ConclusionThe registration and sCT generation could both improve the iCBCT image quality effectively, and the sCT generation could achieve higher accuracy when the difference in planning CT and iCBCT was large.

【 授权许可】

Unknown   
Copyright © 2022 Yang, Chang, Liang, Wang, Pei, Xu and Qiu