Spatiotemporal denoising of low-dose cardiac CT image sequences using RecycleGAN.
Spatiotemporal denoising of low-dose cardiac CT image sequences using RecycleGAN.
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DOI：
10.1088/2057-1976/acf223
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发表时间：
2023-09-12
影响因子：
1.4
通讯作者：
中科院分区：
文献类型：
--
作者：
心电门控多时相CT血管成像(MP-CTA)常用于冠心病的诊断。辐射剂量可能成为一个潜在的问题，因为扫描需要覆盖心脏周期中广泛的心脏时相。减少辐射的一种常见方法是将全剂量采集限制在预定义的阶段范围内，同时降低其余阶段的辐射剂量。我们在这项研究中的目标是开发一种时空深度学习方法来提高在降低辐射剂量下获取的低剂量CTA图像的质量。最近，我们证明了一种深度学习方法-循环一致生成对抗网络(CycleGAN)，可以通过空间图像转换有效地去除低剂量CT图像的噪声，而不需要在低剂量和全剂量图像域使用标记图像对。由于CycleGAN在去噪机制中没有利用时间信息，我们建议使用RecycleGAN，它可以通过一个额外的递归网络将一系列在时间上排序的图像从低剂量域转换到全剂量域。为了评估RecycleGAN，我们使用XCAT Pantom程序，一个基于真实患者数据的高度逼真的模拟工具，为18名患者生成MP-CTA图像序列(14名用于训练，2名用于验证，2名用于测试)。我们的仿真结果表明，无论是视觉检测还是定量度量，RecycleGAN都能获得比CycleGAN更好的去噪性能。我们使用50名患者的临床MP-CTA图像进一步证明了RecycleGAN优越的去噪性能。
Electrocardiogram (ECG)-gated multi-phase computed tomography angiography (MP-CTA) is frequently used for diagnosis of coronary artery disease. Radiation dose may become a potential concern as the scan needs to cover a wide range of cardiac phases during a heart cycle. A common method to reduce radiation is to limit the full-dose acquisition to a predefined range of phases while reducing the radiation dose for the rest. Our goal in this study is to develop a spatiotemporal deep learning method to enhance the quality of low-dose CTA images at phases acquired at reduced radiation dose. Recently, we demonstrated that a deep learning method, Cycle-Consistent generative adversarial networks (CycleGAN), could effectively denoise low-dose CT images through spatial image translation without labeled image pairs in both low-dose and full-dose image domains. As CycleGAN does not utilize the temporal information in its denoising mechanism, we propose to use RecycleGAN, which could translate a series of images ordered in time from the low-dose domain to the full-dose domain through an additional recurrent network. To evaluate RecycleGAN, we use the XCAT phantom program, a highly realistic simulation tool based on real patient data, to generate MP-CTA image sequences for 18 patients (14 for training, 2 for validation and 2 for test). Our simulation results show that RecycleGAN can achieve better denoising performance than CycleGAN based on both visual inspection and quantitative metrics. We further demonstrate the superior denoising performance of RecycleGAN using clinical MP-CTA images from 50 patients.
影响因子：
1.4
作者：
Zhou S;Chi Y;Wang J;Jin M
通讯作者：
Jin M