Abstract #4907

Generalized PRUNO Kernel Selection by Using Singular Value Decomposition (SVD)

Jian Zhang¹, Chunlei Liu², Michael Moseley³

¹Department of Electrical Engineering, Stanford University, Stanford, CA, United States; ²Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, United States; ³Department of Radiology, Stanford University, Stanford, CA, United States

Parallel Reconstruction Using Null Operations (PRUNO) is an iterative k-space based reconstruction method for Cartesian parallel imaging. One particular challenge in PRUNO is to select a set of proper nulling kernels. In this work, we demonstrate an improved kernel selection strategy to create generalized PRUNO kernels from the Singular Value Decomposition (SVD) of calibration data. Furthermore, by introducing composite kernels prior to the conjugate-gradient (CG) reconstruction, the reconstruction time wouldnt increase much when a large number of kernels are used. These new strategies boost the robustness of PRUNO with faster algorithm convergence and lower noise sensitivity.