Abstract #2656

Sparse Tikhonov-Regularized SENSE MRI Reconstruction

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.