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Abstract #2261

Strategies for the Implementation of Compressed Sensing on Arterial Spin Labeled Data: A Retrospective Study

Stanislas Rapacchi1, Robert X. Smith2, Danny J.J. Wang2, Peng Hu1

1Radiology, UCLA, Los Angeles, CA, United States; 2Neurology, UCLA, Los Angeles, CA, United States

We propose to investigate the potential implementation of compressed sensing (CS) to accelerate ASL acquisitions by undersampling using two different strategies: 1) To under-sample each acquisition and apply CS individually then subtract the reconstructed images. 2) To undersample both acquisitions identically in order to perform the subtraction in the k-space domain. ASL data were acquired on the brain of one volunteer with fully sampled k-space then under-sampled retrospectively. CS reconstruction was compared to reference images. Separated CS reconstruction of the 2 acquisitions seems more appropriate for ASL although it prevents CS to benefit from the sparsity of images after subtraction.