arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.12908physics.med-pheess.IV
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基于学习的单能CT合成双能CT成像用于质子放射治疗的阻止功率比计算

Learning-Based Synthetic Dual Energy CT Imaging from Single Energy CT for Stopping Power Ratio Calculation in Proton Radiation Therapy

Serdar Charyyev, Tonghe Wang, Yang Lei, Beth Ghavidel, Jonathan J. Beitler, Mark McDonald, Walter J. Curran, Tian Liu, Jun Zhou, Xiaofeng Yang

摘要:

目的:双能CT(DECT)通过获得光子相互作用的能量依赖性,可以获得比传统单能CT(SECT)更高精度的阻止功率比(SPR)图。然而,DECT在质子放射治疗模拟中没有像SECT那样广泛实施。本文提出了一种基于学习的方法来合成用于质子放射治疗的SECT DECT图像。方法:该方法使用了一个剩余注意生成对抗网络。使用带有注意门的剩余块来迫使模型聚焦于DECT图和SECT图像之间的差异。为了评估该方法的准确性,我们回顾性研究了20例头颈部癌症患者的DECT和SECT扫描。在SECT数据集的训练过程中,从DECT获得的高能量和低能量CT图像作为学习目标,并使用留一交叉验证策略对所提出的方法的结果进行评估。为了评估我们的方法在实际应用的背景下,我们使用基于物理的双能量化学计量方法从sDECT生成SPR图,并将这些图与从DECT生成的图进行比较。结果:合成的DECT图像显示出在整个身体体积上的平均绝对误差约为30 Hounsfield单位(HU)。合成DECT生成的相应SPR图显示平均归一化均方误差约为1%,与原始DECT相比,噪音水平和伪影减少。结论:通过我们的基于机器学习的方法合成的DECT图像的准确性进行了评估的头部和颈部患者,质子治疗计划和剂量计算的潜在可行性,通过生成SPR地图使用合成的DECT。

英文摘要:

Purpose: Dual-energy CT (DECT) has been shown to derive stopping power ratio (SPR) map with higher accuracy than conventional single energy CT (SECT) by obtaining the energy dependence of photon interactions. However, DECT is not as widely implemented as SECT in proton radiation therapy simulation. This work presents a learning-based method to synthetize DECT images from SECT for proton radiation therapy. Methods: The proposed method uses a residual attention generative adversarial network. Residual blocks with attention gates were used to force the model focus on the difference between DECT maps and SECT images. To evaluate the accuracy of the method, we retrospectively investigated 20 head-and-neck cancer patients with both DECT and SECT scans available. The high and low energy CT images acquired from DECT acted as learning targets in the training process for SECT datasets and were evaluated against results from the proposed method using a leave-one-out cross-validation strategy. To evaluate our method in the context of a practical application, we generated SPR maps from sDECT using physics-based dual-energy stoichiometric method and compared the maps to those generated from DECT. Results: The synthesized DECT images showed an average mean absolute error around 30 Hounsfield Unit (HU) across the whole-body volume. The corresponding SPR maps generated from synthetic DECT showed an average normalized mean square error of about 1% with reduced noise level and artifacts than those from original DECT. Conclusions: The accuracy of the synthesized DECT image by our machine-learning-based method was evaluated on head and neck patient, and potential feasibility for proton treatment planning and dose calculation was shown by generating SPR map using the synthesized DECT.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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