Tracer kinetic modelling for DCE-MRI quantification of subtle blood–brain barrier permeability

Abstract

There is evidence that subtle breakdown of the blood–brain barrier (BBB) is a pathophysiological component of several diseases, including cerebral small vessel disease and some dementias. Dynamic contrast-enhanced MRI (DCE-MRI) combined with tracer kinetic modelling is widely used for assessing permeability and perfusion in brain tumours and body tissues where contrast agents readily accumulate in the extracellular space. However, in diseases where leakage is subtle, the optimal approach for measuring BBB integrity is likely to differ since the magnitude and rate of enhancement caused by leakage are extremely low; several methods have been reported in the literature, yielding a wide range of parameters even in healthy subjects. We hypothesised that the Patlak model is a suitable approach for measuring low-level BBB permeability with low temporal resolution and high spatial resolution and brain coverage, and that normal levels of scanner instability would influence permeability measurements. DCE-MRI was performed in a cohort of mild stroke patients (n = 201) with a range of cerebral small vessel disease severity. We fitted these data to a set of nested tracer kinetic models, ranking their performance according to the Akaike information criterion. To assess the influence of scanner drift, we scanned 15 healthy volunteers that underwent a “sham” DCE-MRI procedure without administration of contrast agent. Numerical simulations were performed to investigate model validity and the effect of scanner drift. The Patlak model was found to be most appropriate for fitting low-permeability data, and the simulations showed vp and KTrans estimates to be reasonably robust to the model assumptions. However, signal drift (measured at approximately 0.1% per minute and comparable to literature reports in other settings) led to systematic errors in calculated tracer kinetic parameters, particularly at low permeabilities. Our findings justify the growing use of the Patlak model in low-permeability states, which has the potential to provide valuable information regarding BBB integrity in a range of diseases. However, absolute values of the resulting tracer kinetic parameters should be interpreted with extreme caution, and the size and influence of signal drift should be measured where possible.

Application of Texture Analysis to Study Small Vessel Disease and Blood–Brain Barrier Integrity

“…The predictors of this textural change were SVD indicators previously related to subtle BBB dysfunction: age, hypertension and WMH burden represented by Fazekas scores (6, 33, 35, 53). Given the limitations of the existing DCE-MRI protocols for assessing BBB permeability without knowledge of vessel surface area (8, 9) alternative methods for assessing pre–post gadolinium differences as markers of leakage are worth pursuing. The fact that GLCM of N  = 16, 32, and 64 yielded essentially the same results suggests that the method could be implemented much faster by quantizing the data with little change to the results.…”

Neurovascular unit impairment in early Alzheimer's disease measured with magnetic resonance imaging

“…Several studies have investigated the effect of temporal resolution, the choice of the pharmacokinetic model, the effect of signal drift, and the total scan time on the ability to detect subtle BBB leakage (Barnes et al, 2016;Heye et al, 2016). Although the effect of drift on the DCE-MRI measures in the current study was relatively small compared to other studies (Barnes et al, 2016;Heye et al, 2016), future studies may have to correct for effects of drift. It is also important to use a modified acquisition sequence to detect subtle BBB leakage.…”

STROKOG (stroke and cognition consortium): An international consortium to examine the epidemiology, diagnosis, and treatment of neurocognitive disorders in relation to cerebrovascular disease

“…(2005) [37]Mild Stroke Study II (MSS–II)UK2010–20152641–3m, 1y, 3y1–3 moInitial, 1–3 mo, 1 yACER; MoCA equivalent; NARTHeye et al. (2015; 2016) [38], [39], Valdes Hernanadez et al. (2015) [40]Korean-Vascular Cognitive Impairment Harmonization Standards Study (K-VCIHS)Korea2007–20086203m3 moInitialDSM-IVYu et al.…”