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Abstract #3591

Highly Sparse Spiral fMRI Reconstructed with Compressed Sensing: Trajectory Optimization for BOLD Contrast

Daniel Holland1, Careesa Liu2, Chris V. Bowen2,3, Andy Sederman1, Lynn Gladden1, Steven D. Beyea2,3

1Department of Chemical Engineering & Biotechnology, University of Cambridge, Cambridge, United Kingdom; 2Institute for Biodiagnostics (Atlantic), National Research Council Canada, Halifax, Nova Scotia, Canada; 3Departments of Physics & Radiology, Dalhousie University, Halifax, Nova Scotia, Canada

The use of compressed sensing (CS) introduces the possibility of increased k-space sparsity without the increase in artifact, which widens the range of variable density (VD) spiral trajectories for improving fMRI sensitivity. We have explored a variety of heavily undersampled VD spiral trajectories in order to optimize the fMRI. By combining CS with VD spiral, we demonstrated that optimal fMRI acquisitions are achieved using significantly sparser data than was previously reported for non-CS reconstructed VD spiral data. A representative CS-VD acquisition with 35% undersampling exhibited significantly improved fMRI sensitivity (e.g. 60% more active voxels and 13% increase in CNR).