Liangjie Lin1,2, Michal Považan1, Adam Berrington1, Zhong Chen2, Peter B Barker1,3. 1. Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland. 2. Department of Electronic Science, Xiamen University, Xiamen, China. 3. F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland.
Abstract
PURPOSE: An L2-regularization based postprocessing method is proposed and tested for removal of residual or unsuppressed water signals in proton MR spectroscopic imaging (MRSI) data recorded from the human brain at 3T. METHODS: Water signals are removed by implementation of the L2 regularization using a synthesized water-basis matrix that is orthogonal to metabolite signals of interest in the spectral dimension. Simulated spectra with variable water amplitude and in vivo brain MRSI datasets were used to demonstrate the proposed method. Results were compared with two commonly-used postprocessing methods for removing water signals. RESULTS: The L2 method yielded metabolite signals that were close to true values for the simulated spectra. Residual/unsuppressed water signals in human brain short- and long-echo time MRSI datasets were efficiently removed by the proposed method allowing good quality metabolite maps to be reconstructed with minimized contamination from water signals. Significant differences of the creatine signal were observed between brain long-echo time MRSI without and with water saturation, attributable to the previously described magnetization transfer effect. CONCLUSIONS: With usage of a synthesized water matrix generated based on reasonable prior knowledge about water and metabolite resonances, the L2 method is shown to be an effective way to remove water signals from MRSI of the human brain.
PURPOSE: An L2-regularization based postprocessing method is proposed and tested for removal of residual or unsuppressed water signals in proton MR spectroscopic imaging (MRSI) data recorded from the human brain at 3T. METHODS:Water signals are removed by implementation of the L2 regularization using a synthesized water-basis matrix that is orthogonal to metabolite signals of interest in the spectral dimension. Simulated spectra with variable water amplitude and in vivo brain MRSI datasets were used to demonstrate the proposed method. Results were compared with two commonly-used postprocessing methods for removing water signals. RESULTS: The L2 method yielded metabolite signals that were close to true values for the simulated spectra. Residual/unsuppressed water signals in human brain short- and long-echo time MRSI datasets were efficiently removed by the proposed method allowing good quality metabolite maps to be reconstructed with minimized contamination from water signals. Significant differences of the creatine signal were observed between brain long-echo time MRSI without and with water saturation, attributable to the previously described magnetization transfer effect. CONCLUSIONS: With usage of a synthesized water matrix generated based on reasonable prior knowledge about water and metabolite resonances, the L2 method is shown to be an effective way to remove water signals from MRSI of the human brain.
Authors: Bharath Halandur Nagaraja; Otto Debals; Diana M Sima; Uwe Himmelreich; Lieven De Lathauwer; Sabine Van Huffel Journal: IEEE Trans Biomed Eng Date: 2018-07-05 Impact factor: 4.538
Authors: Gülin Oz; Jeffry R Alger; Peter B Barker; Robert Bartha; Alberto Bizzi; Chris Boesch; Patrick J Bolan; Kevin M Brindle; Cristina Cudalbu; Alp Dinçer; Ulrike Dydak; Uzay E Emir; Jens Frahm; Ramón Gilberto González; Stephan Gruber; Rolf Gruetter; Rakesh K Gupta; Arend Heerschap; Anke Henning; Hoby P Hetherington; Franklyn A Howe; Petra S Hüppi; Ralph E Hurd; Kantarci Kantarci; Dennis W J Klomp; Roland Kreis; Marijn J Kruiskamp; Martin O Leach; Alexander P Lin; Peter R Luijten; Malgorzata Marjańska; Andrew A Maudsley; Dieter J Meyerhoff; Carolyn E Mountford; Sarah J Nelson; M Necmettin Pamir; Jullie W Pan; Andrew C Peet; Harish Poptani; Stefan Posse; Petra J W Pouwels; Eva-Maria Ratai; Brian D Ross; Tom W Scheenen; Christian Schuster; Ian C P Smith; Brian J Soher; Ivan Tkáč; Daniel B Vigneron; Risto A Kauppinen Journal: Radiology Date: 2014-03 Impact factor: 11.105
Authors: Lihong Tang; Yibo Zhao; Yudu Li; Rong Guo; Bryan Clifford; Georges El Fakhri; Chao Ma; Zhi-Pei Liang; Jie Luo Journal: Magn Reson Med Date: 2020-07-29 Impact factor: 4.668