Abstract #0068

Improving Compressed Sensing Parallel Imaging using Autocalibrating Parallel Imaging Initialization with Variable Density Tiled Random k-Space Sampling

Peng Lai¹, Tao Zhang², Michael Lustig^2,3, Shreyas S. Vasanawala⁴, Anja C. S. Brau¹

¹Global Applied Science Laboratory, GE Healthcare, Menlo Park, CA, USA; ²Electrical Engineering, Stanford University, Stanford, CA, USA; ³Electrical Engineering & Computer Science, University of California, Berkeley, CA, USA; ⁴Radiology, Stanford University, Stanford, CA, USA

Compressed sensing (CS) parallel imaging (PI) is computationally intensive due to its need for iterative reconstruction. Autocalibrating PI can improve the initial solution and largely reduce the number of iterations needed. However, random sampling needed for CS generates a huge number of synthesis patterns making PI initialization extremely slow. Also, uniform density k-space sampling currently used for CS-PI is not optimal in terms of reconstruction accuracy. The purpose of this work was to develop a new tiled-random k-space sampling strategy with the desirable features of 1. incoherent k-space sampling with a small number of synthesis patterns and 2. variable density k-space sampling providing more accurate center k-space reconstruction. Based on our evaluations on 4 invivo datasets, the proposed sampling scheme can improve image quality and reconstruction accuracy compared to conventional sampling schemes and meanwhile enables fast PI initialization for CS-PI.