Rician Denoising Diffusion Probabilistic Models For Sodium Breast MRI Enhancement

TL;DR

This paper introduces RDDPM, an improved diffusion model tailored for sodium MRI denoising, effectively converting Rician noise to Gaussian noise, leading to superior image quality over existing methods.

Contribution

The paper develops RDDPM, a novel diffusion model that adapts DDPM for sodium MRI by handling Rician noise, enhancing denoising performance.

Findings

RDDPM outperforms DDPM and CNN-based methods in quality metrics.

RDDPM effectively converts Rician noise to Gaussian noise during denoising.

Enhanced sodium MRI images facilitate better biological and clinical analysis.

Abstract

Sodium MRI is an imaging technique used to visualize and quantify sodium concentrations in vivo, playing a role in many biological processes and potentially aiding in breast cancer characterization. Sodium MRI, however, suffers from inherently low signal-to-noise ratios (SNR) and spatial resolution, compared with conventional proton MRI. A deep-learning method, the Denoising Diffusion Probabilistic Models (DDPM), has demonstrated success across a wide range of denoising tasks, yet struggles with sodium MRI's unique noise profile, as DDPM primarily targets Gaussian noise. DDPM can distort features when applied to sodium MRI. This paper advances the DDPM by introducing the Rician Denoising Diffusion Probabilistic Models (RDDPM) for sodium MRI denoising. RDDPM converts Rician noise to Gaussian noise at each timestep during the denoising process. The model's performance is evaluated using three non-reference image quality assessment metrics, where RDDPM consistently outperforms DDPM and other CNN-based denoising methods.