Stabilize, Decompose, and Denoise: Self-Supervised Fluoroscopy Denoising

TL;DR

This paper introduces a self-supervised three-stage framework for fluoroscopy denoising that stabilizes, decomposes, and denoises videos to improve image quality despite motion and low-dose noise, validated on a new dataset.

Contribution

It proposes a novel self-supervised method combining stabilization, RPCA decomposition, and separate denoising of background and foreground for fluoroscopy videos.

Findings

Significant denoising improvements over standard methods.

Effective stabilization of non-stationary backgrounds.

Expert ratings confirm enhanced image quality.

Abstract

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.