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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