Ultrasound to X-ray synthesis generative attentional network (UXGAN) for adolescent idiopathic scoliosis.

• This work is designed to provide both ultrasound and X-ray images for scoliosis patients based on ultrasound scanning. • A generative attention network for synthesizing X-ray images from ultrasound images is first put forward in this paper. • The image synthesis quality is improved by designing attention mechanism and introducing adaptive instance normalization. • The feasibility of the generated X-ray images is verified through qualitative and quantitative evaluation indicators. 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. [ABSTRACT FROM AUTHOR]