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

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

Metric to quantify white matter damage on brain magnetic resonance images

PURPOSE: Quantitative assessment of white matter hyperintensities (WMH) on structural Magnetic Resonance Imaging (MRI) is challenging. It is important to harmonise results from different software tools considering not only the volume but also the signal intensity. Here we propose and evaluate a metric of white matter (WM) damage that addresses this need. METHODS: We obtained WMH and normal-appearing white matter (NAWM) volumes from brain structural MRI from community dwelling older individuals and stroke patients enrolled in three different studies, using two automatic methods followed by manual editing by two to four observers blind to each other. We calculated the average intensity values on brain structural fluid-attenuation inversion recovery (FLAIR) MRI for the NAWM and WMH. The white matter damage metric is calculated as the proportion of WMH in brain tissue weighted by the relative image contrast of the WMH-to-NAWM. The new metric was evaluated using tissue microstructure parameters and visual ratings of small vessel disease burden and WMH: Fazekas score for WMH burden and Prins scale for WMH change. RESULTS: The correlation between the WM damage metric and the visual rating scores (Spearman ρ > =0.74, p  =0.72, p < 0.0001). The repeatability of the WM damage metric was better than WM volume (average median difference between measurements 3.26% (IQR 2.76%) and 5.88% (IQR 5.32%) respectively). The follow-up WM damage was highly related to total Prins score even when adjusted for baseline WM damage (ANCOVA, p < 0.0001), which was not always the case for WMH volume, as total Prins was highly associated with the change in the intense WMH volume (p = 0.0079, increase of 4.42 ml per unit change in total Prins, 95%CI [1.17 7.67]), but not with the change in less-intense, subtle WMH, which determined the volumetric change. CONCLUSION: The new metric is practical and simple to calculate. It is robust to variations in image processing methods and scanning protocols, and sensitive to subtle and severe white matter damage

Lacunar stroke lesion extent and location and white matter hyperintensities evolution 1 year post-lacunar stroke

Lacunar strokes are a common type of ischemic stroke. They are associated with long-term disability, but the factors affecting the dynamic of the infarcted lesion and the brain imaging features associated with them, reflective of small vessel disease (SVD) severity, are still largely unknown. We investigated whether the distribution, volume and 1-year evolution of white matter hyperintensities (WMH), one of these SVD features, relate to the extent and location of these infarcts, accounting for vascular risk factors. We used imaging and clinical data from all patients [n = 118, mean age 64.9 (SD 11.75) years old] who presented to a regional hospital with a lacunar stroke syndrome within the years 2010 and 2013 and consented to participate in a study of stroke mechanisms. All patients had a brain MRI scan at presentation, and 88 had another scan 12 months after. Acute lesions (i.e., recent small subcortical infarcts, RSSI) were identified in 79 patients and lacunes in 77. Number of lacunes was associated with baseline WMH volume (B = 0.370, SE = 0.0939, P = 0.000174). RSSI volume was not associated with baseline WMH volume (B = 3.250, SE = 2.117, P = 0.129), but predicted WMH volume change (B = 2.944, SE = 0.913, P = 0.00184). RSSI location was associated with the spatial distribution of WMH and the pattern of 1-year WMH evolution. Patients with the RSSI in the centrum semiovale (n = 33) had significantly higher baseline volumes of WMH, recent and old infarcts, than patients with the RSSI located elsewhere [median 33.69, IQR (14.37 50.87) ml, 0.001 ≤ P ≤ 0.044]. But patients with the RSSI in the internal/external capsule/lentiform nucleus experienced higher increase of WMH volume after a year [n = 21, median (IQR) from 18 (11.70 31.54) ml to 27.41 (15.84 40.45) ml]. Voxel-wise analyses of WMH distribution in patients grouped per RSSI location revealed group differences increased in the presence of vascular risk factors, especially hypertension and recent or current smoking habit. In our sample of patients presenting to the clinic with lacunar strokes, lacunar strokes extent influenced WMH volume fate; and RSSI location and WMH spatial distribution and dynamics were intertwined, with differential patterns emerging in the presence of vascular risk factors. These results, if confirmed in wider samples, open potential avenues in stroke rehabilitation to be explored further

A four-dimensional computational model of dynamic contrast-enhanced magnetic resonance imaging measurement of subtle blood-brain barrier leakage

Dynamic contrast-enhanced MRI (DCE-MRI) is increasingly used to quantify and map the spatial distribution of blood-brain barrier (BBB) leakage in neurodegenerative disease, including cerebral small vessel disease and dementia. However, the subtle nature of leakage and resulting small signal changes make quantification challenging. While simplified one-dimensional simulations have probed the impact of noise, scanner drift, and model assumptions, the impact of spatio-temporal effects such as gross motion, k-space sampling and motion artefacts on parametric leakage maps has been overlooked. Moreover, evidence on which to base the design of imaging protocols is lacking due to practical difficulties and the lack of a reference method. To address these problems, we present an open-source computational model of the DCE-MRI acquisition process for generating four dimensional Digital Reference Objects (DROs), using a high-resolution brain atlas and incorporating realistic patient motion, extra-cerebral signals, noise and k-space sampling. Simulations using the DROs demonstrated a dominant influence of spatio-temporal effects on both the visual appearance of parameter maps and on measured tissue leakage rates. The computational model permits greater understanding of the sensitivity and limitations of subtle BBB leakage measurement and provides a non-invasive means of testing and optimising imaging protocols for future studies