基于多域解耦的CT扫描多视角X射线图像合成
Multi-view X-ray Image Synthesis with Multiple Domain Disentanglement from CT Scans
AI总结：
提出CT2X-GAN方法，通过内容和风格解耦及姿态注意力模块，从CT扫描端到端合成多视角X射线图像，在CTSpine1K数据集上表现优于3D感知方法。
摘要：
X射线图像因其高分辨率和快速成像速度在术中过程中发挥着至关重要的作用，并极大地促进了后续的分割、配准和重建。然而，过量的X射线在一定程度上给人类健康带来了潜在风险。从体数据扫描到X射线图像的数据驱动算法受限于成对X射线和体数据的稀缺性。现有方法主要通过建模整个X射线成像过程来实现。在本研究中，我们提出了一种基于学习的方法，称为CT2X-GAN，利用来自三个不同图像域的内容和风格解耦，以端到端的方式合成X射线图像。我们的方法通过一系列解耦编码器，将来自CT扫描的解剖结构信息与来自未配对真实X射线图像/数字重建放射图像（DRR）的风格信息分离开来。此外，我们引入了一种新的一致性正则化项，以提高合成X射线图像与真实X射线图像之间的风格相似度。同时，我们还通过计算计算出的真实DRR与合成DRR图像之间的相似度来施加监督过程。我们进一步开发了一个姿态注意力模块，以充分增强来自CT扫描的解耦内容码中的综合信息，从而在低维2D空间中实现高质量的多视角图像合成。在公开的CTSpine1K数据集上进行了大量实验，在FID、KID和定义的用户评分X射线相似度方面分别取得了97.8350、0.0842和3.0938的成绩。与3D感知方法（$π$-GAN、EG3D）相比，CT2X-GAN在提高合成质量和逼真度方面更具优势，更接近真实X射线图像。
英文摘要：
X-ray images play a vital role in the intraoperative processes due to their high resolution and fast imaging speed and greatly promote the subsequent segmentation, registration and reconstruction. However, over-dosed X-rays superimpose potential risks to human health to some extent. Data-driven algorithms from volume scans to X-ray images are restricted by the scarcity of paired X-ray and volume data. Existing methods are mainly realized by modelling the whole X-ray imaging procedure. In this study, we propose a learning-based approach termed CT2X-GAN to synthesize the X-ray images in an end-to-end manner using the content and style disentanglement from three different image domains. Our method decouples the anatomical structure information from CT scans and style information from unpaired real X-ray images/ digital reconstructed radiography (DRR) images via a series of decoupling encoders. Additionally, we introduce a novel consistency regularization term to improve the stylistic resemblance between synthesized X-ray images and real X-ray images. Meanwhile, we also impose a supervised process by computing the similarity of computed real DRR and synthesized DRR images. We further develop a pose attention module to fully strengthen the comprehensive information in the decoupled content code from CT scans, facilitating high-quality multi-view image synthesis in the lower 2D space. Extensive experiments were conducted on the publicly available CTSpine1K dataset and achieved 97.8350, 0.0842 and 3.0938 in terms of FID, KID and defined user-scored X-ray similarity, respectively. In comparison with 3D-aware methods ($π$-GAN, EG3D), CT2X-GAN is superior in improving the synthesis quality and realistic to the real X-ray images.
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