Sun Hongfei, Lu Zhengda, Fan Rongbo, Xiong Wenjun, Xie Kai, Ni Xinye, Yang Jianhua
School of Automation, Northwestern Polytechnical University, Xi'an, China.
Department of Radiotherapy, Second People's Hospital of Changzhou, Nanjing Medical University, Changzhou, China.
Quant Imaging Med Surg. 2021 May;11(5):1983-2000. doi: 10.21037/qims-20-1019.
To investigate the feasibility of using a stacked generative adversarial network (sGAN) to synthesize pseudo computed tomography (CT) images based on ultrasound (US) images.
The pre-radiotherapy US and CT images of 75 patients with cervical cancer were selected for the training set of pseudo-image synthesis. In the first stage, labeled US images were used as the first conditional GAN input to obtain low-resolution pseudo CT images, and in the second stage, a super-resolution reconstruction GAN was used. The pseudo CT image obtained in the first stage was used as an input, following which a high-resolution pseudo CT image with clear texture and accurate grayscale information was obtained. Five cross validation tests were performed to verify our model. The mean absolute error (MAE) was used to compare each pseudo CT with the same patient's real CT image. Also, another 10 cases of patients with cervical cancer, before radiotherapy, were selected for testing, and the pseudo CT image obtained using the neural style transfer (NSF) and CycleGAN methods were compared with that obtained using the sGAN method proposed in this study. Finally, the dosimetric accuracy of pseudo CT images was verified by phantom experiments.
The MAE metric values between the pseudo CT obtained based on sGAN, and the real CT in five-fold cross validation are 66.82±1.59 HU, 66.36±1.85 HU, 67.26±2.37 HU, 66.34±1.75 HU, and 67.22±1.30 HU, respectively. The results of the metrics, namely, normalized mutual information (NMI), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR), between the pseudo CT images obtained using the sGAN method and the ground truth CT (CTgt) images were compared with those of the other two methods via the paired t-test, and the differences were statistically significant. The dice similarity coefficient (DSC) measurement results showed that the pseudo CT images obtained using the sGAN method were more similar to the CTgt images of organs at risk. The dosimetric phantom experiments also showed that the dose distribution between the pseudo CT images synthesized by the new method was similar to that of the CTgt images.
Compared with NSF and CycleGAN methods, the sGAN method can obtain more accurate pseudo CT images, thereby providing a new method for image guidance in radiotherapy for cervical cancer.
探讨使用堆叠生成对抗网络（sGAN）基于超声（US）图像合成伪计算机断层扫描（CT）图像的可行性。
选取75例宫颈癌患者放疗前的US和CT图像作为伪图像合成的训练集。在第一阶段，将标记的US图像作为第一个条件GAN输入，以获得低分辨率伪CT图像，在第二阶段，使用超分辨率重建GAN。将第一阶段获得的伪CT图像作为输入，从而获得具有清晰纹理和准确灰度信息的高分辨率伪CT图像。进行了五次交叉验证测试以验证我们的模型。使用平均绝对误差（MAE）将每个伪CT与同一患者的真实CT图像进行比较。此外，另选10例宫颈癌患者放疗前进行测试，将使用神经风格迁移（NSF）和循环一致对抗网络（CycleGAN）方法获得的伪CT图像与本研究提出的sGAN方法获得的伪CT图像进行比较。最后，通过体模实验验证伪CT图像的剂量学准确性。
在五折交叉验证中，基于sGAN获得的伪CT与真实CT之间的MAE度量值分别为66.82±1.59 HU、66.36±1.85 HU、67.26±2.37 HU、66.34±1.75 HU和67.22±1.30 HU。通过配对t检验比较了使用sGAN方法获得的伪CT图像与真实CT（CTgt）图像之间的归一化互信息（NMI）、结构相似性指数（SSIM）和峰值信噪比（PSNR）等度量结果与其他两种方法的结果，差异具有统计学意义。骰子相似系数（DSC）测量结果表明，使用sGAN方法获得的伪CT图像与危及器官的CTgt图像更相似。剂量学体模实验还表明，新方法合成的伪CT图像之间的剂量分布与CTgt图像相似。
与NSF和CycleGAN方法相比，sGAN方法能够获得更准确的伪CT图像，从而为宫颈癌放疗中的图像引导提供了一种新方法。
