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Abstract #0385

Robust Multi-Contrast MR Reconstruction Based on Disentangled Representation Learning-Embedded Deep Unrolling

Zhihao Xue1 and Chenxi Hu1
1National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, Data Acquisition, Image Reconstruction, Preclinical

Motivation: Multi-contrast MR imaging is common in clinical practice, creating a need for a universal reconstruction model that can effectively handle various contrasts in MRI.

Goal(s): We aim to develop a reconstruction model that performs well on multiple contrasts.

Approach: We developed a model that leverages disentangled representation learning to disentangle the MR image into contrast-specific style and contrast-invariant content representations during reconstruction.

Results: The proposed model successfully generates disentangled style representations and aligned content representations, demonstrating superior performance compared to other methods in our reconstruction experiments.

Impact: This DRL-assisted reconstruction approach has the potential to serve as a universal model for multi-contrast MR data.

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Keywords