Explore a cutting-edge approach to denoising diffusion MRI scans in this 40-minute lecture by Tiange Xiang from Stanford University. Delve into the innovative Denoising Diffusion Models for Denoising Diffusion MRI (DDM^2) framework, which addresses the challenges of acquiring high-quality MRI scans without increasing scan times or patient discomfort. Learn how this self-supervised method integrates statistic-based denoising theory with diffusion models to perform conditional generation for MRI denoising. Discover the three-stage process and its application to noisy measurements during inference. Gain insights into the quantitative and qualitative analysis of the results, as well as ablation studies that demonstrate the effectiveness of this approach. The lecture concludes with a Q&A session, providing an opportunity to engage with the speaker and explore the potential implications of this research for medical imaging and patient care.