Denoising Diffusion Models for Denoising Diffusion MRI - Tiange Xiang

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.

Introduction
Generating CVI
Generator AI
Diffusion Model
Diffusion Process
Intuitive Demonstration
Diffusion Models
MRI Acquisition
Alternative Solution
Natural Images vs MRI
Multiple observations
Problem definition
Three secretion stages
Denoising results
Noise residual
Successful match
Determining if two distributions are closed
Results
Quantitative Analysis
Qualitative Analysis
Ablation Studies
Questions
Sequence
Question
Comments
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