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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