Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, dynamic MRI, golden-angle radial MRI, self-supervised learning
Motivation: There is a need for self-supervised reconstruction methods for dynamic MRI, where acquiring a fully-sampled reference is impractical. Current self-supervised reconstructions do not exploit the rich temporal properties of dynamic imaging.
Goal(s): To develop a self-supervised reconstruction method for dynamic MRI with high spatial and temporal resolution.
Approach: The proposed SELFIE technique leverages the sampling properties of golden-angle radial acquisition to auto-extract anatomical and temporal representations directly from the acquired data and the inherent sparsity of dynamic images to self-supervise network training.
Results: SELFIE achieves similar performance to supervised learning for DCE-MRI and free-breathing motion-resolved MRI without the need for compressed sensing references.
Impact: SELFIE can achieve comparable performance to supervised deep learning without the limitation of using a compressed sensing reference, which is promising for challenging clinical applications where acquiring a reference is impractical.
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