Universal Denoising for MRI DICOM Images Across Diverse Clinical Conditions Through Variational Diffusion Model

Motivation: MRI DICOM images can be noisy under high acceleration factors. Denoising highly accelerated DICOM images becomes crucial for disease analysis, and allows faster MR imaging. Goal(s): We aim to build a universal denoising model, which is applicable to diverse clinical conditions without compromising diagnostic information. Approach: We propose a sharpness-enhanced variational diffusion model by combining an elaborately designed degradation model with a pre-trained diffusion model. Results: Our method was trained on an in-house large-scale real-world noisy and clean DICOM image pairs from three hospitals. It outperforms the SOTA methods by 1.58% in SSIM and 0.09 in LPIPS across multi-organ, multi-contrast, and multi-vendor conditions. Impact: This study provides a novel solution to the over-smoothness issue for diffusion models when dealing with diverse and complex real-world data. Our model shows promising denoising performance on real-world clinical images scanned with 2x or 3x acceleration factor.