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Abstract #2376

An Efficient Scheme of Trajectory Optimization for Both Parallel Imaging and Compressed Sensing

Enhao Gong1, Feng Huang2, Kui Ying3, Xuening Liu4, George Randy Duensing5

1Electrical Engineering, Stanford University, Stanford, CA, United States; 2Philips Healthcare, Shanghai, China; 3Department of Engineering Physics, Tsinghua University, Beijing, China; 4Department of Automation, Tsinghua University, Beijing, China; 5Philips Healthcare, Gainesville, FL, United States

Undersampling of k-space is a widely adopted approach for fast imaging. Instead of using a fixed sampling trajectory, trajectory optimization has been proposed for both Parallel Imaging and Compressed Sensing to achieve significantly improved reconstruction. Here we present an efficient scheme for clinically applicable trajectory optimization by using one scan in the exam as references and fast pseudo-reconstruction. Experiments on in-vivo datasets illustrated the proposed scheme can results in great improvement of reconstruction using Parallel Imaging and Compressed Sensing.