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Table of Contents    
Year : 2018  |  Volume : 66  |  Issue : 3  |  Page : 669-670

Dynamic contrast-enhanced MRI perfusion in multiple sclerosis: Is this the way to go?

1 Department of Neuroradiology, University of Iowa Health and Clinic, Iowa, USA
2 Department of Radiology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

Date of Web Publication15-May-2018

Correspondence Address:
Dr. Neetu Soni
Department of Neuroradiology, University of Iowa Health and Clinic, Iowa-52246, IA
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0028-3886.232318

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How to cite this article:
Soni N, Kumar S. Dynamic contrast-enhanced MRI perfusion in multiple sclerosis: Is this the way to go?. Neurol India 2018;66:669-70

How to cite this URL:
Soni N, Kumar S. Dynamic contrast-enhanced MRI perfusion in multiple sclerosis: Is this the way to go?. Neurol India [serial online] 2018 [cited 2022 Nov 28];66:669-70. Available from: https://www.neurologyindia.com/text.asp?2018/66/3/669/232318

Yin et al., (study published in this issue of Neurology India), have measured MR imaging biomarkers of perfusion and permeability using Dynamic Contrast-Enhanced (DCE) technique with extended Tofts linear model in relapsing-remitting multiple sclerosis (RRMS) and correlated these biomarkers with expanded disability status scale (EDSS) scores.[1]

Multiple sclerosis (MS) is a chronic demyelinating disease of the central nervous system (CNS) in which white matter (WM) lesions are visualized on T2 weighted (W) MRI in most patients. However, a weak correlation of the T2 lesion volume with clinical impairment has encouraged efforts to identify quantitative imaging measures to better monitor pathological processes of MS lesions. While contrast enhancement of the lesions on T1W scans is the most striking aspect of acute inflammation in MS, many studies have shown that the abnormalities of blood brain barrier (BBB) in MS and perfusion changes are known to precede the development of BBB leakage, which may form an early step in the development of a new lesion. Perfusion studies using either DSC (dynamic susceptibility contrast) or DCE MR imaging found increased cerebral blood volume (CBV) and cerebral blood flow (CBF) in contrast-enhancing lesions compared with normal-appearing white matter (NAWM).[2] It is also increasingly recognized that a more widespread and subtle form of inflammation occurs within the NAWM leading to reduced perfusion in patients with MS.[3],[4]

DCE uses T1-shortening property of the contrast agent and quantitatively measures permeability in areas of disrupted blood-brain barrier. High-temporal-resolution DCE can also provide perfusion information such as volume transfer constant (K trans), volume of the extravascular extracellular space per unit volume of tissue (Ve), fractional plasma volume (Vp), cerebral blood flow (CBF), and cerebral blood volume (CBV). Only a few DCE studies have focused both on the permeability and perfusion abnormality in MS.[5],[6] Different pharmacokinetic models are used to extract hemodynamic quantitative parameters of lesion biology and there is no consensus on the best model yet. Hence, the results published may not be directly comparable but in general the trend can be depended upon. Ingrisch et al.,[6] used a two-compartment Tofts model to analyze the permeability and perfusion parameters of MS lesions. Cramer et al.,[5] compared three different models namely Patlak, extended Tofts and Tikhonov 2 compartment (Tik2CM) and concluded that Patlak model was suited for assessing lower permeability, while Tik2CM performed better in higher permeability values. They suggested that eToft model may underestimate higher permeablility. In this study, the authors have used extended Tofts linear model.

Yin et al.,[1] have applied the histogram analysis in the present study. Mean and median quantitative values are not always significantly sensitive to small changes or treatment effects. The histogram shows the number of pixels in the whole image having the same intensity. During the past decade, the number of studies using histogram approaches to improve assessment despite the heterogeneity of the tumor have increased drastically. These studies demonstrated that the histogram parameters (skewness and kurtosis) were significantly more sensitive to small changes or treatment effects.[7] Yin et al., have found that the histogram patterns of contrast enhancing (CE) lesions and non-enhancing (NE) lesions in MS were different from each other, which indicated that the histogram can be helpful to distinguish the pathology of MS lesions.[1]

The limitations of the study have been well described by the authors.[1] These include the small sample size, which may not be an accurate reflection of the microvascular changes prevalent in MS; inclusion of less number of patients in an active phase, leading to no significant difference of permeability and perfusion parameters between the NAWM regions close to and far away from the MS lesions; the use of the less sensitive Expanded Disability Status Scale (EDSS) scoring system for measuring disability, which mostly reflects gait disability related to spinal cord disease and has been criticized for its variability among examiners.[8]

There are more factors in the DCE-MRI data acquisition and analysis that can affect accuracy and precision of perfusion parameters and, subsequently, its clinical application. The DCE-MRI derived parameters can be affected by the MRI scanner platform (vendor and field strength), data acquisition details (pulse sequence and parameters), contrast dose and/or injection rate, personnel skills, errors in quantification of pre-contrast T1W imaging, determination of the arterial input function (AIF), inadequate temporal resolution or signal-to-noise ratio, as well as selection of models to fit the data. Furthermore, the commercialization of software tools for DCE-MRI data analysis is a required step for the widespread clinical use of this quantitative technique and this requires standardization, thorough comparison and validation of algorithms/software tools.[9]

In conclusion, the authors have made a good attempt at demonstrating the feasibility of three-dimensional T1-weighted DCE-MRI in quantification of perfusion and permeability imaging biomarkers in MS lesions and correlating these imaging biomarkers with EDSS. In the future, quantitative measurements of MS lesion hemodynamics may contribute to the prediction of development of newer lesions; however, more data needs to be collected to investigate this potential.

  References Top

Yin P, Xiong H, Liu Y, Sah SK, Zeng C, Wang J, et al. Measurement of the permeability, perfusion, and histogram characteristics in relapsing-remitting multiple sclerosis using dynamic contrast-enhanced MRI with extended Tofts linear model. Neurol India 2018;66:709-15.  Back to cited text no. 1
  [Full text]  
Wuerfel J, Bellmann-Strobl J, Brunecker P, Aktas O, McFarland H, Villringer A, et al. Changes in cerebral perfusion precede plaque formation in multiple sclerosis: A longitudinal perfusion MRI study. Brain 2004;127:111.  Back to cited text no. 2
Lapointe E, Li DK, Traboulsee AL, Rauscher A. What have we learned from mr imaging in multiple sclerosis? Am J Neuroradiol 2018. Available from: http://dx.doi.org/10.3174/ajnr.A5504. [Last accessed on 2018 May 05].  Back to cited text no. 3
Bester M, Forkert ND, Stellmann JP, Stürner K, Aly L, Drabik A, et al. Increased perfusion in normal appearing white matter in high inflammatory multiple sclerosis patients. PLoS One 2015;10:e0119356.  Back to cited text no. 4
Cramer SP, Larsson HBW. Accurate determination of blood-brain barrier permeability using dynamic contrast-enhanced T1-weighted MRI: A simulation and in vivo study on healthy subjects and multiple sclerosis patients. J Cereb Blood Flow Metab 2014;126:1-11.  Back to cited text no. 5
Ingrisch M, Sourbron S, Morhard D, Ertl-Wagner B, Kümpfel T, Hohlfeld R, et al. Quantification of perfusion and permeability in multiple sclerosis dynamic contrast-enhanced MRI in 3D at 3T. Invest Radiol 2012;47:252-8.  Back to cited text no. 6
Just N. Improving tumour heterogeneity MRI assessment with histograms. Br J Cancer 2014;111:2205-13.  Back to cited text no. 7
Hyland M, Rudick RA. Challenges to clinical trials in multiple sclerosis: Outcome measures in the era of disease-modifying drugs. Curr Opin Neurol2011;24:255-61.  Back to cited text no. 8
Bartoš M, Jiřík R, Kratochvíla J, Standara M, Starčuk Z, Taxt T. The precision of DCE-MRI using the tissue homogeneity model with continuous formulation of the perfusion parameters. Magn Reson Imaging. 2014;32:505-13.  Back to cited text no. 9


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