Keywords: AI/ML Image Reconstruction, Cardiomyopathy
Motivation: Current cardiac T1 mapping technique suffers from long acquisition time and its sensitivity to noise or motion artifacts.
Goal(s): To reduce the acquisition time and to improve the robustness against motion or noise artifacts in cardiac T1 mapping.
Approach: A deep learning framework integrated with Trans-Unet and a fully connected network was developed to realize T1 mapping with T1 weighted images acquired within three cardiac cycles.
Results: The method combined the advantages of current deep learning methods and achieved comparable accuracy to MOLLI, with shortened image acquisitions and enhanced robustness.
Impact: The approach achieves cardiac T1 mapping within three cardiac cycles, clinically reducing the breath-hold time and causes less discomfort to patients. Meanwhile, the integrated network takes advantages of current deep learning methods and significantly improves the reconstruction quality.
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