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基于卷积神经网络的变形规划CT与基于循环生成对抗网络的合成CT方法在改善锥束CT图像质量方面的比较研究

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

作者信息

Yang Bo, Chang Yankui, Liang Yongguang, Wang Zhiqun, Pei Xi, Xu Xie George, Qiu Jie

机构信息

Department of Radiation Oncology, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.

School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, China.

出版信息

Front Oncol. 2022 May 30;12:896795. doi: 10.3389/fonc.2022.896795. eCollection 2022.

Abstract

PURPOSE

The 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).

METHODS

A 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.

RESULTS

The DSC values of the registration and sCT generation were found to be 0.769 and 0.884 for the bladder ( < 0.05), 0.765 and 0.850 for the spinal cord ( < 0.05), 0.918 and 0.923 for the femoral head left ( > 0.05), 0.916 and 0.921 for the femoral head right ( > 0.05), and 0.878 and 0.916 for the bone marrow ( < 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, < 0.05).

CONCLUSION

The 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.

摘要

目的

本研究旨在比较基于卷积神经网络(CNN)的变形规划CT(dpCT)和基于循环一致生成对抗网络(CycleGAN)的合成CT(sCT)生成这两种提高瓦里安Halcyon锥束CT(iCBCT)系统图像质量的方法。

方法

本研究共纳入190对盆腔CT和iCBCT图像数据集,其中150对用于模型训练,其余40对用于模型测试。对于配准网络,我们提出了一种三维多阶段配准网络(MSnet),以使规划CT图像变形以与iCBCT图像匹配,并且通过变形矩阵将CT图像的轮廓传播到相应的iCBCT图像。计算变形轮廓(dpCT)与固定轮廓(iCBCT)之间的重叠度,以评估配准精度。对于sCT生成,我们使用变形配准的CT-iCBCT切片训练二维CycleGAN,并利用相应的iCBCT图像数据生成sCT。然后,在sCT图像上,医生重新勾画轮廓,并与手动勾画的iCBCT图像轮廓进行比较。用于轮廓比较的器官包括膀胱、脊髓、左侧股骨头、右侧股骨头和骨髓。采用骰子相似系数(DSC)评估配准精度和sCT生成精度。

结果

膀胱的配准和sCT生成的DSC值分别为0.769和0.884(P<0.05),脊髓的分别为0.765和0.850(P<0.05),左侧股骨头的分别为0.918和0.923(P>0.05),右侧股骨头的分别为0.916和0.921(P>0.05),骨髓的分别为0.878和0.916(P<0.05)。当规划CT和iCBCT扫描中的膀胱体积差异超过两倍时,sCT生成的精度明显优于配准(膀胱的DSC:0.859对0.596,P<0.05)。

结论

配准和sCT生成均能有效提高iCBCT图像质量,当规划CT与iCBCT差异较大时,sCT生成可实现更高的精度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b041/9189355/aa7da661e7d8/fonc-12-896795-g001.jpg

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