College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310023, China.
Hangzhou Kaiyuan Business Vocational School, Hangzhou 310000, China.
Ultrasonics. 2022 Dec;126:106819. doi: 10.1016/j.ultras.2022.106819. Epub 2022 Jul 29.
Standing X-ray radiograph with Cobb's method is the gold standard for scoliosis diagnosis. However, radiation hazard restricts its application, especially for close follow-up of adolescent patients. Compared with X-ray, ultrasound imaging has advantages of being radiation-free and real-time. To combine advantages of the above two imaging modalities, an ultrasound to X-ray synthesis generative attentional network (UXGAN) was proposed to synthesize ultrasound images into X-ray-like images. In this network, a cyclically consistent network was adopted and was trained end-to-end. An attention module was added and different residual blocks were designed. The quantitative comparison results demonstrated the superiority of our method to the state-of-the-art CycleGAN methods. We further compared the Cobb angle values measured on synthesized images and the real X-ray images, respectively. A good linear correlation (r = 0.95) was demonstrated between the two methods. The above results proved that the proposed method is of great significance for providing both X-ray images and ultrasound images based on the radiation-free ultrasound scanning.
站立位 X 射线射线照相 Cobb 法是脊柱侧凸诊断的金标准。然而，辐射危害限制了其应用，尤其是对青少年患者的密切随访。与 X 射线相比，超声成像具有无辐射和实时的优势。为了结合这两种成像方式的优势，提出了一种超声到 X 射线综合生成注意力网络（UXGAN），将超声图像合成到 X 射线样图像中。在该网络中，采用了循环一致网络并进行了端到端训练。添加了注意力模块，并设计了不同的残差块。定量比较结果表明，我们的方法优于最先进的 CycleGAN 方法。我们进一步比较了合成图像和真实 X 射线图像上测量的 Cobb 角值，两种方法之间表现出很好的线性相关性（r=0.95）。上述结果证明，该方法对于提供基于无辐射超声扫描的 X 射线图像和超声图像具有重要意义。
