BeFOI: A Novel Method Based on Conditional Diffusion Model for Medical Image Denoising

Abstract

The progress in medical imaging technology highlights the importance of image quality for effective diagnosis and treatment. Yet, noise during capture and transmission can compromise image accuracy and reliability, complicating clinical decisions. The rising interest in diffusion models has led to their exploration of denoising images. We present Be-FOI (Better Fluoro Images), a weakly supervised model that uses cine images to denoise fluoroscopic images, both DR types. Trained through precise noise estimation and simulation, BeFOI employs Markov chains to denoise using only the fluoroscopic image as guidance. Our tests show that BeFOI outperforms other methods, reducing noise and enhancing clarity and diagnostic utility, making it an effective post-processing tool for medical images.