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Abstract #1874

Dynamic Latent Variable Modeling for Improved Cardiac MRI Reconstruction

Shuo Zhou1,2, Sen Jia1,2, Jing Cheng1,2, Zhuoxv Cui1,2, Yanjie Zhu1,2, Dong Liang1,2, Haifeng Wang1,2, and Yihang Zhou1,2
1Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences, Shenzhen, China, 2Chinese Academy of Sciences, Beijing, China

Synopsis

Keywords: Image Reconstruction, Heart

Motivation: The beating of the heart is predictable, and existing methods mainly focus on sparsity and low rank, ignore the predictability.

Goal(s): Our goal is to improve the quality of dMRI reconstruction through predictability.

Approach: Introduced a method to extract predictability latent vectors and reconstruct images based on it.

Results: We used highly undersampled data for reconstruction and compared it with L+S,the experimental results indicate that we have achieved better reconstruction results than L+S.

Impact: This work use predictability for dMRI reconstruction without the use of sparsity and low rank.It is possible to introduce a new perspective for the reconstruction of dynamic images.

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Keywords