Denoising 7T Structural MRI with Conditional Generative Diffusion Models

Abstract

Motivation: 7T MRI offers ultra-high resolution, but the commonly used long acquisitions are challenging, especially for elderly subjects. Goal(s): Efficiently denoising 7T MRI images from a short acquisition without sacrificing image quality. Approach: We introduced the 7T Conditional Diffusion Model (7TCDM), a conditional diffusion model derived from generative AI that is trained on raw acquisitions and references high-quality low-noise images to guide the denoising process and reconstruct high-quality images. Results: 7TCDM significantly reduced noise and artifacts, improving image quality over each acquisition and outperforming the Convolutional Neural Network (CNN)-based model in maintaining image details. Impact: Our newly introduced 7T Conditional Diffusion Model (7TCDM) enables faster MRI acquisition by providing high-quality denoised images from shorter scans, increasing the feasibility of scanning patients in shorter times while preserving essential anatomical details.