Khaleghi Goli, Hosntalab Mohammad, Sadeghi Mahdi, Reiazi Reza, Mahdavi Seied Rabi
Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
J Med Signals Sens. 2022 Nov 10;12(4):269-277. doi: 10.4103/jmss.jmss_159_21. eCollection 2022 Oct-Dec.
This study evaluated the performances of neural networks in terms of denoizing metal artifacts in computed tomography (CT) images to improve diagnosis based on the CT images of patients.
First, head-and-neck phantoms were simulated (with and without dental implants), and CT images of the phantoms were captured. Six types of neural networks were evaluated for their abilities to reduce the number of metal artifacts. In addition, 40 CT patients' images with head-and-neck cancer (with and without teeth artifacts) were captured, and mouth slides were segmented. Finally, simulated noisy and noise-free patient images were generated to provide more input numbers (for training and validating the generative adversarial neural network [GAN]).
Results showed that the proposed GAN network was successful in denoizing artifacts caused by dental implants, whereas more than 84% improvement was achieved for images with two dental implants after metal artifact reduction (MAR) in patient images.
The quality of images was affected by the positions and numbers of dental implants. The image quality metrics of all GANs were improved following MAR comparison with other networks.
本研究评估了神经网络在去除计算机断层扫描（CT）图像中的金属伪影方面的性能，以基于患者的CT图像改善诊断。
首先，模拟了带有和不带有牙种植体的头颈部体模，并采集了体模的CT图像。评估了六种类型的神经网络减少金属伪影数量的能力。此外，采集了40例患有头颈癌的患者的CT图像（有和没有牙齿伪影），并对头口滑动进行了分割。最后，生成了模拟的有噪声和无噪声的患者图像，以提供更多的输入数据（用于训练和验证生成对抗神经网络[GAN]）。
结果表明，所提出的GAN网络成功地去除了由牙种植体引起的伪影，在患者图像中，对于有两颗牙种植体的图像，金属伪影减少（MAR）后改善超过84%。
图像质量受牙种植体的位置和数量影响。与其他网络相比，MAR后所有GAN的图像质量指标均得到改善。
