Fluoroscopy is an imaging technique that uses X-ray to obtain a real-time 2D video of the interior of a 3D object, helping surgeons to observe pathological structures and tissue functions especially during intervention. However, it suffers from heavy noise that mainly arises from the clinical use of a low dose X-ray, thereby necessitating the technology of fluoroscopy denoising. Such denoising is challenged by the relative motion between the object being imaged and the X-ray imaging system. We tackle this challenge by proposing a self-supervised, three-stage framework that exploits the domain knowledge of fluoroscopy imaging. (i) Stabilize: we first construct a dynamic panorama based on optical flow calculation to stabilize the non-stationary background induced by the motion of the X-ray detector. (ii) Decompose: we then propose a novel mask-based Robust Principle Component Analysis (RPCA) decomposition method to separate a video with detector motion into a low-rank background and a sparse foreground. Such a decomposition accommodates the reading habit of experts. (iii) Denoise: we finally denoise the background and foreground separately by a self-supervised learning strategy and fuse the denoised parts into the final output via a bilateral, spatiotemporal filter. To assess the effectiveness of our work, we curate a dedicated fluoroscopy dataset of 27 videos (1,568 frames) and corresponding ground truth. Our experiments demonstrate that it achieves significant improvements in terms of denoising and enhancement effects when compared with standard approaches. Finally, expert rating confirms this efficacy.
翻译:荧光镜是一种成像技术,它使用X光获得3D对象内部的实时 2D 视频,帮助外科医生观察病理结构和组织功能,特别是在干预期间;然而,它由于临床使用低剂量X光,主要由于临床使用低剂量X光而产生很大噪音,因此需要使用含氟光分解技术。这种脱色受到图像对象与X光成像系统之间的相对运动的挑战。我们通过提出一个自我监督的、三阶段的框架来应对这一挑战,利用含氟镜成像的域知识。 (一) 稳定化:我们首先根据光学流计算建立一个动态全景,以稳定非静止背景,这主要是由X光探测器运动引起的。 (二) 脱色化:我们然后提出一个新的基于面具的robust 原则组件分析(RPCA) 脱色方法,将一个带有探测器运动的视频分解成一个低等级的背景,以及一个稀薄的地面。 这样的脱色定位适应了专家的阅读习惯。 (三) 我们首先根据光流流计算, 最终通过直径分析结果,然后通过我们从地面上进行自我评估。