PURPOSE: To establish a baseline of phase differences between tissues in a number of regions of the human brain as a means of detecting iron abnormalities using magnetic resonance imaging (MRI). MATERIALS AND METHODS: A fully flow-compensated, three-dimensional (3D), high-resolution, gradient-echo (GRE) susceptibility-weighted imaging (SWI) sequence was used to collect magnitude and phase data at 1.5 T. The phase images were high-pass-filtered and processed region by region with hand-drawn areas. The regions evaluated included the motor cortex (MC), putamen (PUT), globus pallidus (GP), caudate nucleus (CN), substantia nigra (SN), and red nucleus (RN). A total of 75 subjects, ranging in age from 55 to 89 years, were analyzed. RESULTS: The phase was found to have a Gaussian-like distribution with a standard deviation (SD) of 0.046 radians on a pixel-by-pixel basis. Most regions of interest (ROIs) contained at least 100 pixels, giving a standard error of the mean (SEM) of 0.0046 radians or less. In the MC, phase differences were found to be roughly 0.273 radians between CSF and gray matter (GM), and 0.083 radians between CSF and white matter (WM). The difference between CSF and the GP was 0.201 radians, and between CSF and the CN (head) it was 0.213 radians. For CSF and the PUT (the lower outer part) the difference was 0.449 radians, and between CSF and the RN (third slice vascularized region) it was 0.353 radians. Finally, the phase difference between CSF and SN was 0.345 radians. CONCLUSION: The Gaussian-like distributions in phase make it possible to predict deviations from normal phase behavior for tissues in the brain. Using phase as an iron marker may be useful for studying absorption of iron in diseases such as Parkinson's, Huntington's, neurodegeneration with brain iron accumulation (NBIA), Alzheimer's, and multiple sclerosis (MS), and other iron-related diseases. The phases quoted here will serve as a baseline for future studies that look for changes in iron content. (c) 2007 Wiley-Liss, Inc.
PURPOSE: To establish a baseline of phase differences between tissues in a number of regions of the human brain as a means of detecting iron abnormalities using magnetic resonance imaging (MRI). MATERIALS AND METHODS: A fully flow-compensated, three-dimensional (3D), high-resolution, gradient-echo (GRE) susceptibility-weighted imaging (SWI) sequence was used to collect magnitude and phase data at 1.5 T. The phase images were high-pass-filtered and processed region by region with hand-drawn areas. The regions evaluated included the motor cortex (MC), putamen (PUT), globus pallidus (GP), caudate nucleus (CN), substantia nigra (SN), and red nucleus (RN). A total of 75 subjects, ranging in age from 55 to 89 years, were analyzed. RESULTS: The phase was found to have a Gaussian-like distribution with a standard deviation (SD) of 0.046 radians on a pixel-by-pixel basis. Most regions of interest (ROIs) contained at least 100 pixels, giving a standard error of the mean (SEM) of 0.0046 radians or less. In the MC, phase differences were found to be roughly 0.273 radians between CSF and gray matter (GM), and 0.083 radians between CSF and white matter (WM). The difference between CSF and the GP was 0.201 radians, and between CSF and the CN (head) it was 0.213 radians. For CSF and the PUT (the lower outer part) the difference was 0.449 radians, and between CSF and the RN (third slice vascularized region) it was 0.353 radians. Finally, the phase difference between CSF and SN was 0.345 radians. CONCLUSION: The Gaussian-like distributions in phase make it possible to predict deviations from normal phase behavior for tissues in the brain. Using phase as an iron marker may be useful for studying absorption of iron in diseases such as Parkinson's, Huntington's, neurodegeneration with brain iron accumulation (NBIA), Alzheimer's, and multiple sclerosis (MS), and other iron-related diseases. The phases quoted here will serve as a baseline for future studies that look for changes in iron content. (c) 2007 Wiley-Liss, Inc.
Authors: P H Lai; H C Chang; T C Chuang; H W Chung; J Y Li; M J Weng; J H Fu; P C Wang; S C Li; H B Pan Journal: AJNR Am J Neuroradiol Date: 2012-01-26 Impact factor: 3.825
Authors: Muhammad Ayaz; Alexander S Boikov; E Mark Haacke; Daniel K Kido; Wolff M Kirsch Journal: J Magn Reson Imaging Date: 2010-01 Impact factor: 4.813