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Abstract #3220

High Resolution TSE Image Synthesis Using Denoising Diffusion Models Trained on 7T Image Pairs: Application for Hippocampal Subfield Analysis

Jinghang Li1, Andrea Sajewski1, Tales Santini1, and Tamer S Ibrahim1
1Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States

Synopsis

Keywords: AI Diffusion Models, AI/ML Image Reconstruction

Motivation:Severe motion artifacts on high resolution TSE images disrupt the image processing pipeline, leading to failed or erroneous segmentation outputs and forcing subject exclusion from studies.

Goal(s):To address motion artifacts' impact on hippocampus subfield segmentation by developing an alternative solution.

Approach:We implemented a denoising diffusion model for MR image translation, training it to synthesize TSE-like contrast from motion-resistant sequences (MPRAGE and MP2RAGE), and compared different image sampling strategies.

Results:The synthesized images successfully enabled hippocampus subfield segmentation through the ASHS pipeline, demonstrating diffusion models' effectiveness in providing alternatives for motion-corrupted TSE images.

Impact: This work introduces an alternative solution for salvaging motion-corrupted TSE images, potentially reducing patient exclusion rates and improving the statistical power of neuroimaging studies through diffusion model based image translation.

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Keywords