Thrippleton, Michael Jonathan

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MRI measurement of blood–brain barrier leakage: minding the gaps

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     Edinburgh Research Explorer                                      MRI measurement of blood-brain barrier leakage: filling the gapsCitation for published version:Thrippleton, MJ 2018, 'MRI measurement of blood-brain barrier leakage: filling the gaps' Journal ofPhysiology. DOI: 10.1113/JP277425Digital Object Identifier (DOI):10.1113/JP277425Link:Link to publication record in Edinburgh Research ExplorerDocument Version:Publisher's PDF, also known as Version of recordPublished In:Journal of PhysiologyGeneral rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)and / or other copyright owners and it is a condition of accessing these publications that users recognise andabide by the legal requirements associated with these rights.Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorercontent complies with UK legislation. If you believe that the public display of this file breaches copyright pleasecontact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately andinvestigate your claim.Download date: 05. Apr. 2019 This is an Accepted Article that has been peer-reviewed and approved for publication in the The Journal of Physiology, but has yet to undergo copy-editing and proof correction. Please cite this article as an 'Accepted Article'; doi: 10.1113/JP277425. This article is protected by copyright. All rights reserved. Title: MRI measurement of blood-brain barrier leakage: filling the gaps Authors: Michael Jonathan Thrippleton Address: Edinburgh Imaging and the Centre for Clinical Brain Science, University of Edinburgh, 49 Little France Crescent, Edinburgh EH16 4SB, UK; telephone 0131 537 2943; email: m.j.thrippleton@ed.ac.uk Determination of blood-brain barrier (BBB) integrity is of substantial interest to researchers in several fields, including multiple sclerosis (MS), ageing, cerebral small vessel disease, stroke and dementia. A validated, reliable and minimally-invasive technique for in-vivo quantification is therefore a highly desirable goal. Although several imaging and biochemical techniques exist, dynamic contrast-enhanced (DCE-) magnetic resonance imaging (MRI) is considered the most useful for clinical research applications at present (Heye et al., 2014), since it provides quantitative estimates of contrast agent leakage at moderate spatial resolution, and is relatively convenient and safe if relevant guidance is followed. Briefly, a solution of paramagnetic gadolinium complex is injected intravenously, which enters the brain’s capillaries and from there may leak across the BBB into the extracellular extravascular space. This uptake is detected via an increase in the T1-weighted MRI signal intensity, typically measured over a period of several minutes or longer. Image analysis methods can be used to estimate vascular properties including the cerebral blood volume (CBV or vB) and contrast agent leakage rate across the vessel wall (reported variously as KTrans, Ki and PS) from the time-signal profile. Despite widespread application of DCE-MRI in neuro-oncology and body imaging, measurement of leakage across relatively intact BBB is less common and is acknowledged to be difficult: leakage is slow, resulting in very small signal changes – typically five percent or lower over twenty minutes. The precision and accuracy of quantification are limited by noise, artefact, the temporal stability of MRI scanners and by assumptions inherent in the data modelling. In the language of quantitative imaging biomarkers, there is growing  (e.g. (Wong et al., 2017)) but limited evidence supporting the technical and biological validity; such evidence is difficult to obtain due to the need to administer repeat doses of contrast agent and the lack of suitable reference methods. The article by Varatharaj et al. published in the current issue of Journal of Physiology therefore contributes welcome proof-of-principle evidence of biological validity. They performed DCE-MRI in MS subjects and in healthy controls. Specifically, they report a grey matter- (GM) white matter (WM) CBV ratio of around 2, greater BBB leakage rate in contrast-enhancing MS lesions versus normal-appearing WM and greater leakage rate in the normal-appearing WM of MS subjects versus that of healthy controls – findings that are consistent with reasonable biological expectation. The authors also report faster leakage in GM compared with WM in healthy controls, plausibly suggesting that the greater CBV in GM corresponds to higher vascular surface area and therefore more overall leakage per unit tissue volume, assuming equal permeability and vessel radius in the two tissues. Whether or not this is the actual reason, it emphasises the important subtlety that DCE-MRI at best quantifies PS, the capillary surface area per unit volume multiplied by the permeability, not the permeability per se. Vascular surface area is also difficult to measure in-vivo, and the potential influence of vascularity on PS may be especially relevant in studies of cerebrovascular diseases and in cohorts spanning a wide age range. The difficulty of probing low-level BBB leakage is also nicely  This article is protected by copyright. All rights reserved. illustrated in the article: signal changes that are small in relation to the temporal noise level (Figure 3) will be familiar to fellow researchers in the field, as will the phenomenon of negative Ki estimates (Figure 4). An additional challenge to obtaining and interpreting BBB leakage data is the methodological heterogeneity found in the literature, with regard to both data acquisition and image analysis methods, which range from qualitative visual evaluation of permeability to various quantitative and semi-quantitative methods. Partly as a consequence, leakage values reported by different groups are often highly dissimilar or simply impossible to compare. Nevertheless, there is a growing consensus in favour of the approach exemplified by Varatharaj et al., which included pre-injection measurement of the T1 relaxation time and pharmacokinetic analysis of the data using the Patlak model. It is an approach that has been developed and tested by the authors and by other groups over many years (Larsson et al., 2009; Barnes et al., 2016; Heye et al., 2016), and has advantages over simpler methods (e.g. signal enhancement at a single time point or area-under-curve calculation) since it aims to correct for protocol-dependent factors and to distinguish between intra- and extra-vascular contrast agent. On some aspects, however, such as measurement of contrast agent concentration in blood (the “vascular input function”), and temporal resolution, practice remains varied. The time is arguably now ripe for development of consensus-based multi-vendor MRI protocols for more standardised measurement of BBB leakage, coupled with further efforts towards quantitative imaging biomarker validation. One such harmonisation initiative is currently underway in the context of cerebral small vessel disease (https://harness-neuroimaging.org). This should help facilitate the larger, multi-centre studies required to investigate BBB pathophysiology. Parallel development of DCE-MRI and of other methods for probing the BBB, including those measuring transvascular water exchange, is also eagerly anticipated. Barnes SR, Ng TS, Montagne A, Law M, Zlokovic BV & Jacobs RE. (2016). Optimal acquisition and modeling parameters for accurate assessment of low Ktrans blood-brain barrier permeability using dynamic contrast-enhanced MRI. Magn Reson Med 75, 1967-1977. Heye AK, Culling RD, Valdes Hernandez Mdel C, Thrippleton MJ & Wardlaw JM. (2014). Assessment of blood-brain barrier disruption using dynamic contrast-enhanced MRI. A systematic review. Neuroimage Clin 6, 262-274. Heye AK, Thrippleton MJ, Armitage PA, Valdes Hernandez MDC, Makin SD, Glatz A, Sakka E & Wardlaw JM. (2016). Tracer kinetic modelling for DCE-MRI quantification of subtle blood-brain barrier permeability. Neuroimage 125, 446-455. Larsson HB, Courivaud F, Rostrup E & Hansen AE. (2009). Measurement of brain perfusion, blood volume, and blood-brain barrier permeability, using dynamic contrast-enhanced T(1)-weighted MRI at 3 tesla. Magn Reson Med 62, 1270-1281. Wong SM, Jansen JFA, Zhang CE, Staals J, Hofman PAM, van Oostenbrugge RJ, Jeukens C & Backes WH. (2017). Measuring subtle leakage of the blood-brain barrier in cerebrovascular disease with DCE-MRI: Test-retest reproducibility and its influencing factors. J Magn Reson Imaging 46, 159-166. Additional Information  This article is protected by copyright. All rights reserved. Competing interests: The author has no competing interests. Funding: MJT acknowledges financial support from the NHS Lothian Research and Development Office.  

MRI measurement of blood-brain barrier leakage: filling the gaps

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MRI measurement of blood-brain barrier leakage: filling the gaps

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