Title: Projection-to-Projection Translation for Hybrid X-ray and Magnetic Resonance Imaging Show Chinese title

Title: 投影到投影的翻译用于混合X射线和磁共振成像

Abstract: Hybrid X-ray and magnetic resonance (MR) imaging promises large potential in interventional medical imaging applications due to the broad variety of contrast of MRI combined with fast imaging of X-ray-based modalities. To fully utilize the potential of the vast amount of existing image enhancement techniques, the corresponding information from both modalities must be present in the same domain. For image-guided interventional procedures, X-ray fluoroscopy has proven to be the modality of choice. Synthesizing one modality from another in this case is an ill-posed problem due to ambiguous signal and overlapping structures in projective geometry. To take on these challenges, we present a learning-based solution to MR to X-ray projection-to-projection translation. We propose an image generator network that focuses on high representation capacity in higher resolution layers to allow for accurate synthesis of fine details in the projection images. Additionally, a weighting scheme in the loss computation that favors high-frequency structures is proposed to focus on the important details and contours in projection imaging. The proposed extensions prove valuable in generating X-ray projection images with natural appearance. Our approach achieves a deviation from the ground truth of only $6$% and structural similarity measure of $0.913\,\pm\,0.005$. In particular the high frequency weighting assists in generating projection images with sharp appearance and reduces erroneously synthesized fine details. Show Chinese abstract

Abstract: 混合X射线和磁共振（MR）成像由于MRI的多种对比度与基于X射线的模态的快速成像相结合，有望在介入医学成像应用中发挥巨大潜力。 为了充分利用现有大量图像增强技术的潜力，两种模态的相应信息必须存在于同一域中。 对于图像引导的介入手术，X射线透视已被证明是首选的模态。 在这种情况下，从一种模态合成另一种模态是一个病态问题，因为投影几何中的信号模糊和结构重叠。 为了解决这些挑战，我们提出了一种基于学习的解决方案，用于MR到X射线投影到投影的转换。 我们提出了一种图像生成网络，该网络在更高分辨率层中注重高表示能力，以允许在投影图像中准确合成细节。 此外，提出了一种在损失计算中侧重高频结构的加权方案，以关注投影成像中的重要细节和轮廓。 所提出的扩展在生成具有自然外观的X射线投影图像方面证明是有价值的。 我们的方法仅偏离真实值$6$%，结构相似性度量为$0.913\,\pm\,0.005$。 特别是高频加权有助于生成具有清晰外观的投影图像，并减少错误合成的细小细节。