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.