Abstract #2857

Array Compression for 3D Cartesian Sampling

Tao Zhang¹, Michael Lustig^1,2, 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.