Rodrigo A Lobos1,2, W Scott Hoge3,4, Ahsan Javed1,2, Congyu Liao4,5, Kawin Setsompop4,5, Krishna S Nayak1,2,6, Justin P Haldar1,2,6. 1. Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA. 2. Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA. 3. Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA. 4. Department of Radiology, Harvard Medical School, Boston, MA, USA. 5. Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA. 6. Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA.
Abstract
PURPOSE: We propose and evaluate a new structured low-rank method for echo-planar imaging (EPI) ghost correction called Robust Autocalibrated LORAKS (RAC-LORAKS). The method can be used to suppress EPI ghosts arising from the differences between different readout gradient polarities and/or the differences between different shots. It does not require conventional EPI navigator signals, and is robust to imperfect autocalibration data. METHODS: Autocalibrated LORAKS is a previous structured low-rank method for EPI ghost correction that uses GRAPPA-type autocalibration data to enable high-quality ghost correction. This method works well when the autocalibration data are pristine, but performance degrades substantially when the autocalibration information is imperfect. RAC-LORAKS generalizes Autocalibrated LORAKS in two ways. First, it does not completely trust the information from autocalibration data, and instead considers the autocalibration and EPI data simultaneously when estimating low-rank matrix structure. Second, it uses complementary information from the autocalibration data to improve EPI reconstruction in a multi-contrast joint reconstruction framework. RAC-LORAKS is evaluated using simulations and in vivo data, including comparisons to state-of-the-art methods. RESULTS: RAC-LORAKS is demonstrated to have good ghost elimination performance compared to state-of-the-art methods in several complicated EPI acquisition scenarios (including gradient-echo brain imaging, diffusion-encoded brain imaging, and cardiac imaging). CONCLUSIONS: RAC-LORAKS provides effective suppression of EPI ghosts and is robust to imperfect autocalibration data.
PURPOSE: We propose and evaluate a new structured low-rank method for echo-planar imaging (EPI) ghost correction called Robust Autocalibrated LORAKS (RAC-LORAKS). The method can be used to suppress EPI ghosts arising from the differences between different readout gradient polarities and/or the differences between different shots. It does not require conventional EPI navigator signals, and is robust to imperfect autocalibration data. METHODS: Autocalibrated LORAKS is a previous structured low-rank method for EPI ghost correction that uses GRAPPA-type autocalibration data to enable high-quality ghost correction. This method works well when the autocalibration data are pristine, but performance degrades substantially when the autocalibration information is imperfect. RAC-LORAKS generalizes Autocalibrated LORAKS in two ways. First, it does not completely trust the information from autocalibration data, and instead considers the autocalibration and EPI data simultaneously when estimating low-rank matrix structure. Second, it uses complementary information from the autocalibration data to improve EPI reconstruction in a multi-contrast joint reconstruction framework. RAC-LORAKS is evaluated using simulations and in vivo data, including comparisons to state-of-the-art methods. RESULTS: RAC-LORAKS is demonstrated to have good ghost elimination performance compared to state-of-the-art methods in several complicated EPI acquisition scenarios (including gradient-echo brain imaging, diffusion-encoded brain imaging, and cardiac imaging). CONCLUSIONS: RAC-LORAKS provides effective suppression of EPI ghosts and is robust to imperfect autocalibration data.
Authors: Mark A Griswold; Peter M Jakob; Robin M Heidemann; Mathias Nittka; Vladimir Jellus; Jianmin Wang; Berthold Kiefer; Axel Haase Journal: Magn Reson Med Date: 2002-06 Impact factor: 4.668
Authors: Chitresh Bhushan; Justin P Haldar; Soyoung Choi; Anand A Joshi; David W Shattuck; Richard M Leahy Journal: Neuroimage Date: 2015-03-27 Impact factor: 6.556