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Abstract #4199

Structural Low-Rank Regularized Diffusion Model for Even-Odd Echo Separation in EPI Reconstruction

Chen Luo1, Ting Zhao2, Jing Cheng2, Guoqing Chen1, Qiyu Jin1, Zhuo-Xu Cui2, and Dong Liang2
1School of Mathematical Sciences, Inner Mongolia University, Hohhot, China, 2Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

Synopsis

Keywords: Diffusion Reconstruction, Diffusion Reconstruction, Diffusion, structural low-rankness, $$$k$$$-space interpolation, EPI sequence, separation reconstruction

Motivation: In accelerated EPI, besides aliasing patterns caused by undersampling, eddy artifacts often arise, significantly degrading image quality.

Goal(s): We aim to construct a method that can accelerate EPI (effectively suppress aliasing pattern) while suppressing eddy artifacts.

Approach: Diffusion models effectively suppress aliasing patterns caused by accelerated imaging. Given that eddy artifacts arise from odd-even echo switching, we construct a diffusion model for separate reconstruction of odd-even echo acquisition signals. Additionally, we incorporate Structural Low-Rank prior into proposed diffusion model to ensure robustness.

Results: Taking DWI data as an example, our method works well in removing both eddy artifacts and aliasing artifacts.

Impact: We propose a diffusion model with Structural Low-Rank prior that couples odd-even echo acquisition signals, effectively suppressing eddy artifacts while reconstructing magnetic resonance images from undersampled $$$k$$$-space data.

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