Insights into brain microstructure from the T2 distribution

MacKay, Alex L.; Laule, Cornelia; Vavasour, Irene M.; Bjarnason, Thorarin A.; Kolind, Shannon; Mädler, Burkhard

doi:10.1016/j.mri.2005.12.037

Cited by 329 publications

(379 citation statements)

References 49 publications

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“…[2], where the exponential terms are matrix exponentials (19)(20)(21). By sequentially combining time blocks of constant conditions, pulse sequences were simulated in the four-pool model.…”

Section: Simulationsmentioning

confidence: 99%

“…The simulated IR signal was computed by combining cases of Eq. [2] for free-precession and constant B 1 irradiation, assuming hard pulses for inversion and excitation. Semisolid saturation was also incorporated into the IR simulations, in the same manner as the refocusing pulses of the spin echo simulations.…”

Section: Simulationsmentioning

confidence: 99%

“…In response, investigators have turned to quantitative MRI techniques to identify putative indicators of specific pathologic features, such as myelin loss. Quantitative analysis of spin-spin relaxation data using T 2 distributions (QT2), also known as myelin water imaging, can notably distinguish two major T 2 components in human WM (1,2). In particular, QT2 yields an estimate of the myelin water fraction (MWF), the fraction of water contained between the layers of myelin in WM.…”

mentioning

confidence: 99%

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Characterizing healthy and diseased white matter using quantitative magnetization transfer and multicomponent T₂ relaxometry: A unified view via a four‐pool model

Levesque

Pike

2009

Magnetic Resonance in Med

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Nuclear magnetic resonance relaxation and magnetization transfer in cerebral white matter can be described using a four-pool model: two for water protons (in separate myelin and intra/extracellular compartments) and two for protons associated with the lipids and proteins of biologic membranes (of myelin and nonmyelin semisolids). This model was used to gain insight into the observations from multicomponent quantitative T 2 relaxometry and quantitative magnetization transfer imaging, both based on simplified white matter models and experimentally feasible in vivo. The sensitivity of MRI to white matter (WM) damage in diseases such as multiple sclerosis (MS) has made it indispensable in diagnosis, but its lack of pathologic specificity remains problematic. In response, investigators have turned to quantitative MRI techniques to identify putative indicators of specific pathologic features, such as myelin loss. Quantitative analysis of spin-spin relaxation data using T 2 distributions (QT2), also known as myelin water imaging, can notably distinguish two major T 2 components in human WM (1,2). In particular, QT2 yields an estimate of the myelin water fraction (MWF), the fraction of water contained between the layers of myelin in WM. Magnetization transfer (MT) imaging (3,4), most often measured as the MT ratio, is a widely available technique that is sensitive to the semisolid constituents of tissue (e.g., lipids, proteins), and in WM the MT effect is believed to be dominated by myelin (5). The MT ratio is a semiquantitative index that presents a limited view of the MT effect. More detailed analysis of pulsed, off-resonance MT data using the two-pool model of tissue (6,7) has been extended to in vivo imaging, in particular in human WM (8-10). The resulting method, termed quantitative MT imaging (QMTI), allows mapping of the parameters of the two-pool (liquid and semisolid) model of tissue: the ratio of semisolid to liquid protons F, the first-order forward and reverse exchange rate constants k f and k r (where k r ϭ k f /F), the spin-lattice relaxation rate R 1f of the free pool (R 1f ϭ 1/T 1f ), the spin-spin relaxation time constant of the free pool T 2f , and the "T 2 " of the restricted pool, T 2r (inversely related to the width of the restricted pool resonance). Separating the fundamental parameters of the two-pool model requires an additional measurement of the "long" observed T 1 (T 1obs ), from which the free pool R 1f can then be computed (6). The MWF, MT ratio, and certain QMTI parameters have respectively been shown to correlate with myelin content and demyelination (11-13).QT2 and QMTI techniques each present a different limited view of WM characteristics. QT2 imaging relies on the fundamental assumptions that water exists in isolated compartments and that variations in the estimated MWF are equal to variations in myelin. On the other hand, the two-pool model for MT neglects the existence of multiple water pools. A more comprehensive model for WM that includes four communicating proton pools (myelin soli...

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“…[2], where the exponential terms are matrix exponentials (19)(20)(21). By sequentially combining time blocks of constant conditions, pulse sequences were simulated in the four-pool model.…”

Section: Simulationsmentioning

confidence: 99%

Section: Simulationsmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Characterizing healthy and diseased white matter using quantitative magnetization transfer and multicomponent T₂ relaxometry: A unified view via a four‐pool model

Levesque

Pike

2009

Magnetic Resonance in Med

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“…Typically, multiple echo T 2 data acquisition consists of measuring the signal intensity of the same anatomical slice with different echo times using a multiple echo spin-echo pulse sequence [3,4]. The signal intensities of the voxels in the image data will decay exponentially based on the inherent T 2 times, and a T 2 distribution can be created by fitting a sum of exponential decays to the multiple echo decay curve [5].…”

Section: Introductionmentioning

confidence: 99%

“…Multiple echo fitting, using non-negative least squares (NNLS) [5], can be performed to determine the relative proportions of the micro-structural dependent T 2 times [6,7,8,9,10]. In some cases, such as healthy [3,4,11,12] and pathological [13,14,15,10,16] central nervous system tissue, different water environments with separate T 2 times are present within a given voxel, resulting in a decay curve from the voxel being the summation of exponential decays from each constituent T 2 time. Four regions are of particular interest: 1) one with a short T 2 time attributed to water trapped between myelin bilayers, called myelin water; 2) one with an intermediate T 2 time attributed to intra-and extracellular water; 3) one with a prolonged T 2 time longer than intra-and extracellular water, which has been noted in pathology [13,14] and in the corticospinal tract of healthy volunteers [13,17]; and 4) one with the longest T 2 time in the distribution, or near that of pure water, that is attributed to cerebrospinal fluid.…”

Section: Introductionmentioning

confidence: 99%

Temporal phase correction of multiple echo T2 magnetic resonance images

Bjarnason

Laule

Bluman

et al. 2013

Journal of Magnetic Resonance

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Typically, magnetic resonance imaging (MRI) analysis is performed on magnitude data, and multiple echo T 2 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 T 2 relaxation. Magnitude T 2 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 T 2 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 T 2 acquisitions. Here we describe a temporal phase correction (TPC) algorithm that utilizes the temporal noise information available in multiple echo T 2 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 T 2 data from human subjects measured at 1.5 T. We show that applying TPC causes changes in the T 2 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 T 2 analyses.

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Diffusely Abnormal White Matter in Chronic Multiple Sclerosis

Seewann¹,

Vrenken²,

Valk³

et al. 2009

Arch Neurol

156

171

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Background: Diffuse abnormalities in the white matter (WM), ie, the so-called diffusely abnormal WM (DAWM), as observed on magnetic resonance imaging (MRI), may contribute to the development of clinical disability in multiple sclerosis (MS). Underlying pathologic and MRI characteristics of DAWM are largely unknown.Objectives: To explore and describe the histopathologic and radiologic characteristics of DAWM in chronic MS.Design: An MRI and histopathologic postmortem correlative study. Methods:We analyzed 17 formalin-fixed hemispheric brain slices from 10 patients with chronic MS using histopathologic analysis and qualitative and quantitative MRI. A region-of-interest approach was applied to compare radiologically defined DAWM, normal-appearing WM, and focal WM lesions and to correlate quantitative MRI measures with histopathologic findings. Main Outcome Measures:The DAWM consisted of extensive axonal loss, decreased myelin density, and chronic fibrillary gliosis, all of which were substantially abnormal compared with normal-appearing WM and significantly different from focal WM lesion pathology. Increased T1-and T2-relaxation times and decreased fractional anisotropy values were found in DAWM regions of interest, in association with extensive axonal loss and reduced myelin density. Increased T1-and T2relaxation times were associated with chronic gliosis.Conclusions: This study classifies DAWM in chronic MS as an abnormality that is different from normalappearing WM and focal WM lesions, most likely resulting from the cumulative effects of ongoing inflammation and axonal pathology. As such, DAWM is likely to substantially contribute to disease progression and may prove to be an important new disease marker in clinical trials focusing on the neurodegenerative aspects of MS.

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Insights into brain microstructure from the T2 distribution

Cited by 329 publications

References 49 publications

Characterizing healthy and diseased white matter using quantitative magnetization transfer and multicomponent T₂ relaxometry: A unified view via a four‐pool model

Characterizing healthy and diseased white matter using quantitative magnetization transfer and multicomponent T₂ relaxometry: A unified view via a four‐pool model

Temporal phase correction of multiple echo T2 magnetic resonance images

Diffusely Abnormal White Matter in Chronic Multiple Sclerosis

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Insights into brain microstructure from the T2 distribution

Cited by 329 publications

References 49 publications

Characterizing healthy and diseased white matter using quantitative magnetization transfer and multicomponent T2 relaxometry: A unified view via a four‐pool model

Characterizing healthy and diseased white matter using quantitative magnetization transfer and multicomponent T2 relaxometry: A unified view via a four‐pool model

Temporal phase correction of multiple echo T2 magnetic resonance images

Diffusely Abnormal White Matter in Chronic Multiple Sclerosis

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Product

Resources

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Characterizing healthy and diseased white matter using quantitative magnetization transfer and multicomponent T₂ relaxometry: A unified view via a four‐pool model

Characterizing healthy and diseased white matter using quantitative magnetization transfer and multicomponent T₂ relaxometry: A unified view via a four‐pool model