Meeting Banner
Abstract #2886

Noise Weighted T2*-IDEAL Reconstruction for Non-Uniformly Under-Sampled k-Space Acquisitions

Curtis Nathan Wiens1, Shawn Joseph Kisch2, Catherine D. G. Hines3, Huanzhou Yu4, Angel R. Pineda5, Philip M. Robson6, Jean H. Brittain7, Scott B. Reeder, 38, Charles A. McKenzie1,2

1Department of Physics and Astronomy, University of Western Ontario, London, Ont, Canada; 2Department of Medical Biophysics, University of Western Ontario, London, Ont, Canada; 3Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States; 4Applied Science Laboratory, GE Healthcare, Menlo Park, CA, United States; 5Department of Mathematics, California State University, Fullerton, CA, United States; 6Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, United States; 7Applied Science Laboratory, GE Healthcare, Madison, WI, United States; 8Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States

Using different undersampling patterns for the non-calibration and calibration echoes has been shown to improve SNR per unit time of Parallel Imaging accelerated IDEAL reconstructions by up to 40%. The different acceleration factors and k-space undersampling patterns result in different noise enhancement in the non-calibration and calibration echoes. In this work the T2*-IDEAL reconstruction is modified to include noise weighting and demonstrate that SNR improves with the modified reconstruction. For 14.2 fold accelerated phantom data, an 11.9% increase in mean SNR for all phantoms and a maximum 27% increase in SNR over a single phantom was measured.