Keywords: Machine Learning/Artificial Intelligence, Image Reconstruction, Strain
Motivation: Accelerated cardiac cine MRI is prone to motion artifacts and underestimation of imaging biomarkers. Furthermore, many approaches lack prospective evaluation.
Goal(s): Improve data-driven reconstruction of cardiac cine MRI and enable inline reconstruction with improved estimation of strain parameters.
Approach: Training a neural network based on a Variational Network combined with intermediate conjugate gradient optimizations and evaluation on retrospectively undersampled data. Inline integration into scanner software using the FIRE framework and prospective evaluation in terms of image quality and cardiac strain parameters.
Results: The proposed network outperformed established compressed sensing approaches both retrospectively and prospectively, and in both image quality and cardiac strain estimation.
Impact: Our research enables inline reconstruction of highly accelerated (up to real-time) cardiac cine MRI with high motion fidelity and improved strain estimation compared to well-established compressed sensing approaches.
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