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Abstract #1188

SELFIE: SElf-supervised Learning for Fast dynamIc golden-anglE radial MRI reconstruction with auto-extracted representations

Melanie Schellenberg1, Anthony Mekhanik1, Victor Murray1, and Ricardo Otazo1,2
1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States, 2Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States

Synopsis

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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Keywords