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Abstract #2655

Edge-Preserving Non-Iterative MAP 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

We propose two pre-computation-allowable and non-iterative MAP SENSE reconstruction algorithms based on 1) a Gaussian Random Field (GRF) with non-zero mean and 2) a Huber-Markov Random Field (HMRF) with non-zero mean. Simulation results show that the non-iterative HMRF MAP regularization technique is more effective for edge preservation and residual aliasing artifact reduction than non-iterative GRF MAP and Tikhonov-type regularization methods.

Keywords

acceleration accurately acquisition additive aliasing allow allowable although amplification analytical approaches appropriate approximations around array artifact artifacts assume assumption become becomes better biomedical calibration channel coil collected complex computation computational compute computed computer computing conjugate consider convolution correlated cost covariance degraded deliver denotes downloaded edge edges effective electrical encoding energy engineering enhanced error estimation field finite folded folding form formulated full head human introduced iterative linear maintains maps matrix minimization model noise observation observe operator parallel particular particularly permit posed potentially preservation preserve preserved preserving prior priors problems product promise propose proposed quadratic random real reasonable reconstruct reconstructed reconstruction reduce reduced reduces reduction reeves regularization regularize residual resolution resolve scanning school sense sensitivity serious simply simulation smoothed solution solves space stationary statistical substantially successfully summation system table target temperature trans transposed treated type vector version west wide zero