利用无监督三维深度学习网络计算锥形束计算机断层扫描的质子剂量

IF 3.4 Q2 ONCOLOGY
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引用次数: 0

摘要

背景和目的锥束计算机断层扫描(CBCT)图像质量差会妨碍质子剂量计算,从而无法评估解剖结构变化的影响。本研究旨在评估使用无监督三维深度学习网络从 CBCT 生成的合成 CT 的图像质量和质子剂量计算的准确性。材料与方法 共有 102 名头颈部癌症患者被用于训练(N=82)和测试(N=20)i) 循环一致性生成对抗网络,ii) 对比非配对翻译,iii) 两者的融合(CycleCUT)。对于测试集中的患者,将重复 CT 与当天的 CBCT 进行变形注册,以创建地面实况 CT 进行比较。在地面实况 CT 和合成 CT 上重新计算质子计划。合成 CT 的图像质量通过峰值信噪比、结构相似性指数测量、平均误差和平均绝对误差 (MAE) 进行评估。结果所有合成 CT 都准确保留了 CBCT 的解剖结构(通过目测验证),同时提高了图像质量。与其他网络相比,CycleCUT 网络的图像质量略有提高(体部 MAE:53 Hounsfield 单位 (HU) 对 54/55 HU)。所有网络的质子剂量计算准确度相似,伽马通过率均在 97% 以上。结论所有三个评估网络生成的合成 CT 图像的剂量分布与传统扇形束 CT 的剂量分布相当。合成 CT 生成速度快,使所有网络都能用于自适应质子治疗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Proton dose calculation on cone-beam computed tomography using unsupervised 3D deep learning networks

Background and Purpose

Poor image quality of cone-beam computed tomography (CBCT) images can hinder proton dose calculation to assess the influence of anatomy changes. The aim of this study was to evaluate image quality and proton dose calculation accuracy of synthetic CTs generated from CBCT using unsupervised 3D deep-learning networks.

Materials and methods

A total of 102 head-and-neck cancer patients were used to train (N=82) and test (N=20) i) a cycle-consistent generative adversarial network, ii) a contrastive unpaired translation, and iii) a fusion of the two (CycleCUT). For patients in the test set, a repeat CT was deformably registered to a same-day CBCT to create a ground-truth CT for comparison. The proton plan was re-calculated on the ground-truth CT and synthetic CTs. The image quality of the synthetic CTs was evaluated using peak signal-to-noise ratio, structural similarity index measure, mean error, and mean absolute error (MAE). Proton dose calculation accuracy was assessed through 3D gamma analysis and dose-volume-histogram parameters.

Results

All synthetic CTs accurately preserved the CBCT anatomy (verified by visual inspection) while improving the image quality. The CycleCUT network had slightly improved image quality compared to the other networks (MAE in body: 53 Hounsfield units (HU) vs. 54/55 HU). All networks had similar proton dose calculation accuracy with gamma passing rate above 97%.

Conclusions

All three evaluated networks generated synthetic CT images with dose distributions comparable to those of conventional fan-beam CT. The synthetic CT generation was fast, making all networks feasible for adaptive proton therapy.
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来源期刊
Physics and Imaging in Radiation Oncology
Physics and Imaging in Radiation Oncology Physics and Astronomy-Radiation
CiteScore
5.30
自引率
18.90%
发文量
93
审稿时长
6 weeks
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