Tao Zhang1, Michael Lustig1,2, Shreyas Vasanawala3, John Pauly1
1Electrical Engineering, Stanford University, Stanford, CA, United States; 2Electrical Engineering & Computer Science, University of California Berkeley, Berkeley, CA, United States; 3Radiology, Stanford University, Stanford, CA, United States
Array compression is a technique to reduce data size and reconstruction computation for large coil arrays. In this work, a data-driven array compression for 3D Cartesian sampling is proposed. A slice-by-slice array compression method with autocalibrating parallel reconstruction using 3D synthesis kernels is designed. Faster reconstruction and similar image quality is achieved compared with reconstruction results using the original large arrays.