MEDL: Unsupervised Multi-Stage Ensemble Deep Learning with Diffusion Models for Denoising MRI Scans

Motivation: Traditional MRI scans, necessary for high SNR and clear images, are time-consuming and discomfort for patients. Shorter scans, meant to improve the patient experience, often compromise image quality and SNR. New deep learning techniques provide a solution to denoise MRI scans, even with limited data availability. Goal(s): We aim to create an unsupervised MRI denoising method for real-world clinical settings, eliminating the need for clean or paired noisy images ensuring versatility and practicality. Approach: We use an unsupervised diffusion-based denoising approach to denoise MRI scans. Results: We achieve unsupervised denoising for MRI scans, outperforming previous methods and reducing time to 6 seconds. Impact: Our approach denoises general MRI scans without extra clean or noisy data. It's suitable for real-world clinics, reducing patient MRI time. It enhances imaging quality, ensuring accurate diagnoses and faster clinical practices for patients and doctors.