(ISMRM 2024) Cine Cardiac MRI Motion Correction using Denoising Diffusion Probabilistic Models
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Abstract #4493

Cine Cardiac MRI Motion Correction using Denoising Diffusion Probabilistic Models

Yang Liu1, Jiameng Diao1, Zijian Zhou1, Haikun Qi1,2,3, and Peng Hu1,2,3
1ShanghaiTech University, Shanghai, China, 2Shanghai Clinical Research and Trial Center, Shanghai, China, 3United Imaging Healthcare, Shanghai, China

Synopsis

Keywords: AI Diffusion Models, Motion Correction

Motivation: Cine cardiac MRI is used to evaluate cardiac functions and vascular abnormalities.However, MRI requires a long scan time, which inevitably induces motion artifacts.

Goal(s): Develop a cine cardiac MR image motion correction technique to reduce both the scan time and motion artifacts.

Approach: We trained a diffusion-based model with simulated data from a public ACDC dataset to reduce the cine cardiac MRI motion artifacts.

Results: The proposed method was compared with GAN and U-Net methods in removing motion artifacts. It produced results that closely approach the ground-truth, achieving the highest SSIM and PSNR scores among all the evaluated methods.

Impact: Our method demonstrates improvements in motion compensation compared with GAN and U-Net.

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Keywords

motiondatasetthetacardiaccineproposedprocessmodelcorrupteddiffusion