Towards the automatic computational assessment of enlarged perivascular spaces on brain magnetic resonance images: a systematic review

Rationale, design and methodology of the image analysis protocol for studies of patients with cerebral small vessel disease and mild stroke

RATIONALE

Cerebral small vessel disease (SVD) is common in ageing and patients with dementia and stroke. Its manifestations on magnetic resonance imaging (MRI) include white matter hyperintensities, lacunes, microbleeds, perivascular spaces, small subcortical infarcts, and brain atrophy. Many studies focus only on one of these manifestations. A protocol for the differential assessment of all these features is, therefore, needed.

AIMS

To identify ways of quantifying imaging markers in research of patients with SVD and operationalize the recommendations from the STandards for ReportIng Vascular changes on nEuroimaging guidelines. Here, we report the rationale, design, and methodology of a brain image analysis protocol based on our experience from observational longitudinal studies of patients with nondisabling stroke.

DESIGN

The MRI analysis protocol is designed to provide quantitative and qualitative measures of disease evolution including: acute and old stroke lesions, lacunes, tissue loss due to stroke, perivascular spaces, microbleeds, macrohemorrhages, iron deposition in basal ganglia, substantia nigra and brain stem, brain atrophy, and white matter hyperintensities, with the latter separated into intense and less intense. Quantitative measures of tissue integrity such as diffusion fractional anisotropy, mean diffusivity, and the longitudinal relaxation time are assessed in regions of interest manually placed in anatomically and functionally relevant locations, and in others derived from feature extraction pipelines and tissue segmentation methods. Morphological changes that relate to cognitive deficits after stroke, analyzed through shape models of subcortical structures, complete the multiparametric image analysis protocol.

OUTCOMES

Final outcomes include guidance for identifying ways to minimize bias and confounds in the assessment of SVD and stroke imaging biomarkers. It is intended that this information will inform the design of studies to examine the underlying pathophysiology of SVD and stroke, and to provide reliable, quantitative outcomes in trials of new therapies and preventative strategies.

A priori collaboration in population imaging: The Uniform Neuro-Imaging of Virchow-Robin Spaces Enlargement consortium

Introduction

Virchow-Robin spaces (VRS), or perivascular spaces, are compartments of interstitial fluid enclosing cerebral blood vessels and are potential imaging markers of various underlying brain pathologies. Despite a growing interest in the study of enlarged VRS, the heterogeneity in rating and quantification methods combined with small sample sizes have so far hampered advancement in the field.

Methods

The Uniform Neuro-Imaging of Virchow-Robin Spaces Enlargement (UNIVRSE) consortium was established with primary aims to harmonize rating and analysis (www.uconsortium.org). The UNIVRSE consortium brings together 13 (sub)cohorts from five countries, totaling 16,000 subjects and over 25,000 scans. Eight different magnetic resonance imaging protocols were used in the consortium.

Results

VRS rating was harmonized using a validated protocol that was developed by the two founding members, with high reliability independent of scanner type, rater experience, or concomitant brain pathology. Initial analyses revealed risk factors for enlarged VRS including increased age, sex, high blood pressure, brain infarcts, and white matter lesions, but this varied by brain region.

Discussion

Early collaborative efforts between cohort studies with respect to data harmonization and joint analyses can advance the field of population (neuro)imaging. The UNIVRSE consortium will focus efforts on other potential correlates of enlarged VRS, including genetics, cognition, stroke, and dementia.

The feasibility of quantitative MRI of perivascular spaces at 7T

BACKGROUND

Dilated brain perivascular spaces (PVSs) are found to be associated with many conditions, including aging, dementia, and Alzheimer's disease (AD). Conventionally, PVS assessment is mainly based on subjective observations of the number, size and shape of PVSs in MR images collected at clinical field strengths (≤3T). This study tests the feasibility of imaging and quantifying brain PVS with an ultra-high 7T whole-body MRI scanner.

NEW METHOD

3D high resolution T2-weighted brain images from healthy subjects (n=3) and AD patients (n=5) were acquired on a 7T whole-body MRI scanner. To automatically segment the small hyperintensive fluid-filling PVS structures, we also developed a quantitative program based on algorithms for spatial gradient, component connectivity, edge-detection, k-means clustering, etc., producing quantitative results of white matter PVS volume densities.

RESULTS

The 3D maps of automatically segmented PVS show an apparent increase in PVS density in AD patients compared to age-matched healthy controls due to the PVS dilation (8.0±2.1 v/v% in AD vs. 4.9±1.3 v/v% in controls, p<0.05).

COMPARISON WITH EXISTING METHOD

We demonstrated that 7T provides sufficient SNR and resolution for quantitatively measuring PVSs in deep white matter that is challenging with clinical MRI systems (≤3T). Compared to the conventional visual counting and rating for the PVS assessment, the quantitation method we developed is automatic and objective.

CONCLUSIONS

Quantitative PVS MRI at 7T may serve as a non-invasive and endogenous imaging biomarker for diseases with PVS dilation.

Cerebral perivascular spaces visible on magnetic resonance imaging: development of a qualitative rating scale and its observer reliability

Background

Perivascular spaces (PVS) are an important component of cerebral small vessel disease (SVD), several inflammatory disorders, hypertension and blood-brain barrier breakdown, but are difficult to quantify. A recent international collaboration of SVD experts has highlighted the need for a robust, easy-to-use PVS rating scale for the effective investigation of the diagnostic and prognostic significance of PVS. The purpose of the current study was to develop and extend existing PVS scales to provide a more comprehensive scale for the measurement of PVS in the basal ganglia, centrum semiovale and midbrain, and to test its intra- and inter-rater agreement, assessing reasons for discrepancy.

Methods

We reviewed previously published PVS scales, including site of PVS assessed, rating method, and size and morphological criteria. Retaining key features, we devised a more comprehensive scale in order to improve the reliability of PVS rating. Two neuroradiologists tested the new scale in MRI brain scans of 60 patients from two studies (stroke, ageing population), chosen to represent a full range of PVS, and demonstrating concomitant features of SVD such as lacunes and white matter hyperintensities. We rated basal ganglia, centrum semiovale, and midbrain PVS. Basal ganglia and centrum semiovale PVS were rated 0 (none), 1 (1–10), 2 (11–20), 3 (21–40) and 4 (>40), and midbrain PVS were rated 0 (none visible) or 1 (visible). We calculated kappa statistics for rating, assessed consistency in use of PVS categories (Bhapkar test) and reviewed sources of discrepancy.

Results

Intra- and inter-rater kappa statistics were highest for basal ganglia PVS (range 0.76–0.87 and 0.8–0.9, respectively) than for centrum semiovale PVS (range 0.68–0.75 and 0.61–0.8, respectively) or midbrain PVS (inter-rater range 0.51–0.52). Inter-rater consistency was better for basal ganglia compared to centrum semiovale PVS (Bhapkar statistic 2.49–3.72, compared to 6.79–21.08, respectively). Most inter-rater disagreements were due to very faint PVS, coexisting extensive white matter hyperintensities (WMH) or the presence of lacunes.

Conclusions

We developed a more inclusive and robust visual PVS rating scale allowing rating of all grades of PVS severity on structural brain imaging. The revised PVS rating scale has good observer reliability for basal ganglia and centrum semiovale PVS, best for basal ganglia PVS, and moderate reliability for midbrain PVS. Agreement is influenced by PVS severity and the presence of background features of SVD. The current scale can be used in further studies to assess the clinical implications of PVS.