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Abstract #5156

Parallel Imaging Reconstruction from Randomly Undersampled Data with k-space Variant Sparsity Constraints

Yu Y. Li1,2

1Cardiac Diagnostic Imaging, St. Francis Hospital, Roslyn, NY, United States, 2Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States

A new parallel imaging reconstruction framework is proposed to accelerate MRI using both coil sensitivity and data sparsity. This framework uses random undersampling and performs parallel imaging reconstruction with a k-space variant constraint. No calibration data are needed. It is demonstrated that this new approach offers a gain over conventional parallel imaging in imaging acceleration.

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