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

Sahil Vora, Riti Paul, Pak Lun Kevin Ding, Ameet Patel, Leland Hu, Richard Sharpe, Yuxiang Zhou, Baoxin Li

Abstract: Image denoising is a persistent challenge, often relying on paired datasets or known noise distributions. While deep neural networks are effective, they are impractical for medical applications like Magnetic Resonance Imaging (MRI) due to their dependency on paired datasets and struggle with unknown noise distributions. Recent advances in self-supervised techniques and diffusion models allow denoising from a single noisy image, but current methods struggle with over-smoothing and hallucination, limiting clinical applicability. To address these issues, we propose a Multi-stage Ensemble Deep Learning framework that capitalizes on the power of diffusion models and operates independently of signal or noise priors. By rescaling multi-stage reconstructions, it balances smoothing and hallucination. Evaluations show a 1dB and 2.5% improvement in PSNR and SSIM over the best existing methods, with promising results for real clinical MRI scans.

External IDs:dblp:conf/isbi/VoraPDPHSZL25