Diffusion tensor magnetic resonance imaging in multiple sclerosis

Filippi, Massimo; Cercignani, Mara; Inglese, Matilde; Horsfield, Mark A.; Comi, Giancarlo

doi:10.1212/wnl.56.3.304

Cited by 480 publications

(376 citation statements)

References 40 publications

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“…Using this information, investigation of the white matter characteristics and anatomic connections between different regions could be carried out noninvasively [Catani et al, 2003;Peled et al, 1998]. Using DTI, much progress has been made in the studies of brain development, normal aging, brain asymmetry, brain function connectivity, and neurologic and psychiatric diseases [Filippi et al, 2001;Kubicki et al, 2002a,b]. Most results with DTI were found either by comparison of some indexes in ROIs or voxel-based analysis of registered image datasets [Lim et al, 1999].…”

Section: Discussionmentioning

confidence: 99%

Asymmetry analysis of cingulum based on scale‐invariant parameterization by diffusion tensor imaging

Gong

Jiang

Zhu

et al. 2004

Human Brain Mapping

144

100

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Current analysis of diffusion tensor imaging (DTI) is based mostly on a region of interest (ROI) in an image dataset, which is specified by users. This method is not always reliable, however, because of the uncertainty of manual specification. We introduce an improved fiber-based scheme rather than an ROI-based analysis to study in DTI datasets of 31 normal subjects the asymmetry of the cingulum, which is one of the most prominent white matter fiber tracts of the limbic system. The present method can automatically extract the quantitative anisotropy properties along the cingulum bundles from tractography. Moreover, statistical analysis was carried out after anatomic correspondence specific to the cingulum across subjects was established, rather than the traditional whole-brain registration. The main merit of our method compared to existing counterparts is that to find such anatomic correspondence in cingulum, a scale-invariant parameterization method by arc-angle was proposed. It can give a continuous and exact description on any segment of cingulum. More interestingly, a significant left-greater-than-right asymmetry pattern was obtained in most segments of cingulum bundle (Ϫ50 -25 degrees), except in the most posterior portion of cingulum (25-50 degrees). Hum Brain Mapp 24:92-98, 2005.

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Section: Discussionmentioning

confidence: 99%

Asymmetry analysis of cingulum based on scale‐invariant parameterization by diffusion tensor imaging

Gong

Jiang

Zhu

et al. 2004

Human Brain Mapping

144

100

View full text Add to dashboard Cite

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“…We investigated the impact of variations in the intercompartmental water exchange rate on multicomponent T 2 , and on two-pool parameter estimates from pulsed MT. Since diffusivity increases of up to 50% are reported in the MS literature (29)(30)(31), the effect of increased exchange from potential pathologic changes was simulated by increases in the water exchange rate (R D ) of 25, 50, and 100%. Simulations were also performed at the fast-and slow-exchange limits (R D 3 0 and R D ϭ 10 4 sec -1 , respectively).…”

Section: Modification Of the Modelmentioning

confidence: 99%

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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“…An iterative Newton's method approach was used to solve for A from the fifth degree polynomial (Eq. [5]), starting with an initial seed value of 0.3 and repeating the algorithm until the difference between successive iterations was less than 0.0001. The modified Gong-Hornak algorithm was used to solve for A from Eq.…”

Section: Methodsmentioning

confidence: 99%

Quantitative diffusion imaging with steady‐state free precession

Deoni

Peters

Rutt

2004

Magnetic Resonance in Med

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The addition of a single, unbalanced diffusion gradient to the steady-state free precession (SSFP) imaging sequence sensitizes the resulting signal to free diffusion. Unfortunately, the confounding influence of both longitudinal (T 1 ) and transverse (T 2 ) relaxation on the diffusion-weighted SSFP (dwSSFP) signal has made it difficult to quantitatively determine the apparent diffusion coefficient (ADC). Here, a multistep method in which the T 1 , T 2 , and spin density (M o ) constants are first determined using a rapid mapping technique described previously is presented. Quantitative ADC can then be determined through a novel inversion of the appropriate signal model. The accuracy and precision of our proposed method (which we term DES-POD) was determined by comparing resulting ADC values from phantoms to those calculated from traditional diffusion-weighted echo planar imaging (dwEPI) images. Error within the DESPODderived ADC maps was found to be less than 3%, with good precision over a biologically Key words: apparent diffusion coefficient; steady-state free precession; diffusion imaging; rapid volumetric imaging Diffusion-weighted imaging (DWI) has been shown to provide diagnostically useful information about the brain following stroke (1,2), as well as prostate and other cancers (3,4). Increasingly, the ability to measure the apparent diffusion coefficient (ADC) with high resolution, precision, and accuracy has become an important addition to qualitative DWI. Quantitatively determining the ADC along at least six encoding directions makes it possible to describe the local diffusion tensor and is useful in the study of various central nervous system disorders (5).When a single, unbalanced diffusion gradient is added to the steady-state free precession (SSFP) imaging sequence, the resulting signal is strongly sensitized to intravoxel incoherent motion and free molecular diffusion (6). The diffusion sensitivity of diffusion-weighted SSFP (dwSSFP) has previously been investigated by several authors (6 -10), starting with Kaiser et al. (6), who examined the effect of adding a constant unbalanced diffusion gradient to the SSFP sequence in NMR spectroscopy. Wu and Buxton (7) extended this analysis to the pulsed gradients more commonly employed in imaging applications. By solving the Bloch equations using a Fourier series approximation, Wu and Buxton derived a general equation for the signal immediately following and preceding the RF pulse (S ϩ and S -, respectively) with the addition of a diffusion gradient of strength G and duration ␦. LeBihan et al. (8) derived an alternative derivation of S ϩ and S -by modifying the diffusion-free Bloch equations and treating the diffusion attenuation as an effective T 2 shortening and looking specifically at the case where 90°flip angles are used. Despite the ability to characterize the S ϩ and Ssignals, dwSSFP has not been presented as a means of quantitatively determining ADC due to the confounding influence of longitudinal (T 1 ) and transverse (T 2 ) relaxation and spin de...

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Diffusion tensor magnetic resonance imaging in multiple sclerosis

Cited by 480 publications

References 40 publications

Asymmetry analysis of cingulum based on scale‐invariant parameterization by diffusion tensor imaging

Asymmetry analysis of cingulum based on scale‐invariant parameterization by diffusion tensor imaging

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

Quantitative diffusion imaging with steady‐state free precession

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Diffusion tensor magnetic resonance imaging in multiple sclerosis

Cited by 480 publications

References 40 publications

Asymmetry analysis of cingulum based on scale‐invariant parameterization by diffusion tensor imaging

Asymmetry analysis of cingulum based on scale‐invariant parameterization by diffusion tensor imaging

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

Quantitative diffusion imaging with steady‐state free precession

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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