Il Yong Chun1,
Thomas Talavage1, 2
1School
of Electrical and Computer Engineering, Purdue University, West Lafayette,
IN, United States; 2Weldon School of Biomedical Engineering,
Purdue University, West Lafayette, IN, United States
Here, we present a pre-computation-allowable sparse Tikhonov-regularized SENSE MRI reconstruction technique based on QR decomposition, fast regularization parameter estimation using a new L-curve , and sparse matrix representation. The simulation results show that it significantly reduces residual aliasing artifacts and noise amplification for ill-posed cases.