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Objective: To analyze the texture of T2-weighted magnetic resonance imaging (MRI) of postmortem multiple sclerosis (MS) brain, and to determine whether and how MRI texture correlates with tissue pathology., Methods: Ten brain samples from 3 subjects with MS were examined. Areas of complete, partial, or no loss of Luxol fast blue (myelin) and Bielschowsky (axons) staining were marked on histological images, and matched on corresponding MRI as lesions, diffusely abnormal white matter (DAWM), and normal-appearing white matter (NAWM). The number of CD45(+) cells (inflammation) was also counted. MRI texture was computed using polar Stockwell transform and compared to histology., Results: Thirty-four lesions, 17 DAWM regions, and 36 NAWM regions were identified. After mixed effects modeling, MRI texture heterogeneity was greater in lesions than in DAWM (p < 0.001) and NAWM (p < 0.001), and was greater in DAWM than in NAWM (p < 0.001); the number of CD45+ cells was greater in both lesions (p < 0.001) and DAWM (p = 0.005) than in NAWM. In MRI, a gradient of texture heterogeneity was detected in lesions, with gradual tapering toward perilesional NAWM. Moreover, besides univariate correlation with histological markers, texture heterogeneity correlated independently with normalized myelin density (p < 0.01) when random effects were considered. Within sample, MRI texture correlated with myelin and axonal density in 7 of 10 samples (p < 0.01)., Interpretation: Texture analysis performed on routine clinical magnetic resonance images may be a potential measure of tissue integrity. Tissues with more severe myelin and axonal pathology are associated with greater texture heterogeneity., (Copyright © 2013 American Neurological Association.)

Typically, magnetic resonance imaging (MRI) analysis is performed on magnitude data, and multiple echo T2 data consist of numerous images of the same slice taken with different echo spacing, giving voxel-wise temporal sampling of the noise as the signals decay according to T2 relaxation. Magnitude T2 decay data has Rician distributed noise which is characterized by a change in the noise distribution from Gaussian, through a transitional region, to Rayleigh as the signal to noise ratio decreases with increasing echo time. Non-Gaussian noise distributions may produce errors in the commonly applied non-negative least squares (NNLS) algorithm that is used to assess multiple echo decays for compartmentalized water environments through the creation of T2 distributions. Typically, Gaussian noise is sought by performing spatial-based phase correction on the MRI data however, these methods cannot capitalize on the temporal information available from multiple echo T2 acquisitions. Here we describe a temporal phase correction (TPC) algorithm that utilizes the temporal noise information available in multiple echo T2 acquisitions to put the relevant decay information in the Real portion of the decay data and leave only noise in the Imaginary portion. We apply this TPC algorithm to create real-valued multiple echo T2 data from human subjects measured at 1.5 T. We show that applying TPC causes changes in the T2 distribution estimates; notably the possible resolution of separate extracellular and intracellular water environments, and the disappearance of the commonly labeled cerebrospinal fluid peak, which might be an artefact observed in many previously published multiple echo T2 analyses., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Exponential decays are fundamental to magnetic resonance imaging, yet adequately sampling and analyzing multiexponential decays is rarely attempted. The advantage of multiexponential analysis is the quantification of sub-voxel structure caused by water compartmentalization, with application as a non-invasive imaging biomarker for myelin. We have developed AnalyzeNNLS, software designed specifically for multiexponential decay image analysis that has a user-friendly graphical user interface and can analyze data from many MR manufacturers. AnalyzeNNLS is a simple, platform independent analysis tool that was created using the extensive mathematical and visualization libraries in Matlab, and released as open source code allowing scientists to evaluate, scrutinize, improve, and expand., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Typical quantitative T2 (qT2) analysis involves creating T2 distributions using a regularized algorithm from region-of-interest averaged decay data. This study uses qT2 analysis of simulated and experimental decay signals to determine how (a) noise-type, (b) regularization, and (c) region-of-interest versus multivoxel analyses affect T2 distributions. Our simulations indicate that regularization causes myelin water fraction and intra/extracellular water geometric mean T2 underestimation that worsens as the signal-to-noise ratio decreases. The underestimation was greater for intra/extracellular water geometric mean T2 measures using Rician noise. Simulations showed significant differences between myelin water fractions determined using region-of-interest and multivoxel approaches compared to the true value. The nonregularized voxel-based approach gave the most accurate measure of myelin water fraction and intra/extracellular water geometric mean T2 for a given signal-to-noise ratio and noise type. Additionally, multivoxel analysis provides important information about the variability of the analysis. Results obtained from in vivo rat data were similar to our simulation results. In each case, a nonregularized, multivoxel analysis provided myelin water fractions significantly different from the regularized approaches and obtained the largest myelin water fraction. We conclude that quantitative T2 analysis is best performed using a nonregularized, multivoxel approach., (Copyright (c) 2009 Wiley-Liss, Inc.)

Multiecho T2 relaxation measurements to determine geometric mean T2 (GMT2) and myelin water fraction (MWF) are lengthy, resulting in increased motion artefacts from patient discomfort and reduced patient compliance. The goal of this study was to shorten the acquisition time for multiecho T2 measurements without affecting T1 weighting by varying TR across k-space. Six phantoms and 10 healthy volunteers were imaged with both a constant TR and a variable TR multiecho T2 sequence. T1 weighting was determined by TR at the center of k-space; for variable TR measurement, TR was shortened linearly from the center to the edges of k-space. Phantoms showed excellent agreement for proton density and GMT2 between constant and variable TR measurements. No significant differences were found in proton density or MWF for any of the brain structures between the two measurements. The average GMT2 over all structures between the two experiments was not significantly different. In summary, with the variable TR approach, scan time was reduced by >20%, with minimal loss of image resolution and no significant affect on proton density, MWF or GMT2.

In vitro experiments on 15 white matter samples from five bovine brains were performed on a 1H-NMR spectrometer at 24 degrees C and 37 degrees C. The average myelin water fractions (MWFs) were 10.9% and 11.8% for samples at 24 degrees C and 37 degrees C, respectively. The T1 relaxation time at 37 degrees C was found to be 830 ms, exhibiting monoexponential behavior. A four-pool model including intra/extracellular (IE) water, myelin water, nonmyelin tissue, and myelin tissue was proposed to simulate the NMR behavior of bovine white matter. A cross-relaxation correction was introduced to compensate for shifting of the measured data points and T2 times over the duration of the Carr-Purcell-Meiboom-Gill (CPMG) measurement due to cross relaxation. This correction was found to be slight, providing evidence that MWFs measured using a multiecho technique are near physical values. At 24 degrees C the cross-relaxation times between myelin tissue and myelin water, myelin water and IE water, and IE water and nonmyelin tissue were found to be approximately 227, 2064, and 402 ms, respectively. At 37 degrees C these same cross-relaxation times were 158, 1021, and 170 ms, respectively. The exchange rate between myelin water and myelin was found to be 11.8 s-1 at 37 degrees C, while the exchange rate between IE water and nonmyelin tissue was found to be 6.8 s-1. These exchange rates are of similar magnitude, which indicates that the interaction between IE water and nonmyelin tissue cannot be ignored., ((c) 2005 Wiley-Liss, Inc.)