Diffusion X-ray image denoising

Daniel Sanderson, Pablo M. Olmos, Carlos Fernández Del Cerro, Manuel Desco, Mónica Abella

Keywords: X-ray, planar radiography, poisson noise, denoising, diffusion model, generative model

Abstract:

X-ray imaging is a cornerstone in medical diagnosis, constituting a significant portion of the radiation dose encountered by patients. Excessive radiation poses health risks, particularly for pediatric patients, but despite the imperative to reduce radiation doses, conventional image processing methods for X-ray denoising often struggle with heuristic parameter calibration and prolonged execution times. Deep Learning solutions have emerged as promising alternatives, but their effectiveness varies, and challenges persist in preserving image quality.
This paper presents an exploration of diffusion models for planar X-ray image denoising, a novel approach that to our knowledge has not been yet investigated in this domain. We perform real time denoising of Poisson noise while preserving image resolution and structural similarity. The results indicate that diffusion models show promise for planar X-ray image denoising, offering a potential improvement in the optimization of diagnostic utility amid dose reduction efforts.

Submission Number: 33