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

A new family of instance-level loss functions for improving instance-level segmentation and detection of white matter hyperintensities in routine clinical brain MRI

In this study, we introduce "instance loss functions", a new family of loss functions designed to enhance the training of neural networks in the instance-level segmentation and detection of objects in biomedical image data, particularly those of varied numbers and sizes. Intended to be utilized conjointly with traditional loss functions, these proposed functions, prioritize object instances over pixel-by-pixel comparisons. The specific functions, the instance segmentation loss (Linstance), the instance center loss (Lcenter), the false instance rate loss (Lfalse), and the instance proximity loss (Lproximity), serve distinct purposes. Specifically, Linstance improves instance-wise segmentation quality, Lcenter enhances segmentation quality of small instances, Lfalse minimizes the rate of false and missed detections across varied instance sizes, and Lproximity improves detection quality by pulling predicted instances towards the ground truth instances. Through the task of segmenting white matter hyperintensities (WMH) in brain MRI, we benchmarked our proposed instance loss functions, both individually and in combination via an ensemble inference models approach, against traditional pixel-level loss functions. Data were sourced from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the WMH Segmentation Challenge datasets, which exhibit significant variation in WMH instance sizes. Empirical evaluations demonstrate that combining two instance-level loss functions through ensemble inference models outperforms models using other loss function on both the ADNI and WMH Segmentation Challenge datasets for the segmentation and detection of WMH instances. Further, applying these functions to the segmentation of nuclei in histopathology images demonstrated their effectiveness and generalizability beyond WMH, improving performance even in contexts with less severe instance imbalance.

Detectability and accuracy of computational measurements of in-silico and physical representations of enlarged perivascular spaces from magnetic resonance images

BACKGROUND

Magnetic Resonance Imaging (MRI) visible perivascular spaces (PVS) have been associated with age, decline in cognitive abilities, interrupted sleep, and markers of small vessel disease. But the limits of validity of their quantification have not been established.

NEW METHOD

We use a purpose-built digital reference object to construct an in-silico phantom for addressing this need, and validate it using a physical phantom. We use cylinders of different sizes as models for PVS. We also evaluate the influence of 'PVS' orientation, and different sets of parameters of the two vesselness filters that have been used for enhancing tubular structures, namely Frangi and RORPO filters, in the measurements' accuracy.

RESULTS

PVS measurements in MRI are only a proxy of their true dimensions, as the boundaries of their representation are consistently overestimated. The success in the use of the Frangi filter relies on a careful tuning of several parameters. Alpha= 0.5, beta= 0.5 and c= 500 yielded the best results. RORPO does not have these requirements and allows detecting smaller cylinders in their entirety more consistently in the absence of noise and confounding artefacts. The Frangi filter seems to be best suited for voxel sizes equal or larger than 0.4 mm-isotropic and cylinders larger than 1 mm diameter and 2 mm length. 'PVS' orientation did not affect measurements in data with isotropic voxels.

COMPARISON WITH EXISTENT METHODS

Does not apply.

CONCLUSIONS

The in-silico and physical phantoms presented are useful for establishing the validity of quantification methods of tubular small structures.

Influence of threshold selection and image sequence in in-vivo segmentation of enlarged perivascular spaces

BACKGROUND

Growing interest surrounds perivascular spaces (PVS) as a clinical biomarker of brain dysfunction given their association with cerebrovascular risk factors and disease. Neuroimaging techniques allowing quick and reliable quantification are being developed, but, in practice, they require optimisation as their limits of validity are usually unspecified.

NEW METHOD

We evaluate modifications and alternatives to a state-of-the-art (SOTA) PVS segmentation method that uses a vesselness filter to enhance PVS discrimination, followed by thresholding of its response, applied to brain magnetic resonance images (MRI) from patients with sporadic small vessel disease acquired at 3 T.

RESULTS

The method is robust against inter-observer differences in threshold selection, but separate thresholds for each region of interest (i.e., basal ganglia, centrum semiovale, and midbrain) are required. Noise needs to be assessed prior to selecting these thresholds, as effect of noise and imaging artefacts can be mitigated with a careful optimisation of these thresholds. PVS segmentation from T1-weighted images alone, misses small PVS, therefore, underestimates PVS count, may overestimate individual PVS volume especially in the basal ganglia, and is susceptible to the inclusion of calcified vessels and mineral deposits. Visual analyses indicated the incomplete and fragmented detection of long and thin PVS as the primary cause of errors, with the Frangi filter coping better than the Jerman filter.

COMPARISON WITH EXISTING METHODS

Limits of validity to a SOTA PVS segmentation method applied to 3 T MRI with confounding pathology are given.

CONCLUSIONS

Evidence presented reinforces the STRIVE-2 recommendation of using T2-weighted images for PVS assessment wherever possible. The Frangi filter is recommended for PVS segmentation from MRI, offering robust output against variations in threshold selection and pathology presentation.

High perilesional T2-FLAIR signal around anterior temporal perivascular spaces: How can fluid suppressed Amide Proton Transfer weighted imaging further comfort the diagnosis

Areas of marked T2-FLAIR hyperintensity around perivascular spaces can be misdiagnosed as tumor, especially in case of lesion evolution. In this report, we show and describe increased T2-FLAIR signal intensity around anterior temporal perivascular spaces in three patients and shortly review this poorly known entity. In addition, we discuss for the first time the added value of fluid suppressed APTw imaging, an emerging noninvasive molecular technique, in the characterization of this "do not touch" abnormality.

Neuroimaging standards for research into small vessel disease-advances since 2013

Cerebral small vessel disease (SVD) is common during ageing and can present as stroke, cognitive decline, neurobehavioural symptoms, or functional impairment. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive and other symptoms and affect activities of daily living. Standards for Reporting Vascular Changes on Neuroimaging 1 (STRIVE-1) categorised and standardised the diverse features of SVD that are visible on structural MRI. Since then, new information on these established SVD markers and novel MRI sequences and imaging features have emerged. As the effect of combined SVD imaging features becomes clearer, a key role for quantitative imaging biomarkers to determine sub-visible tissue damage, subtle abnormalities visible at high-field strength MRI, and lesion-symptom patterns, is also apparent. Together with rapidly emerging machine learning methods, these metrics can more comprehensively capture the effect of SVD on the brain than the structural MRI features alone and serve as intermediary outcomes in clinical trials and future routine practice. Using a similar approach to that adopted in STRIVE-1, we updated the guidance on neuroimaging of vascular changes in studies of ageing and neurodegeneration to create STRIVE-2.

Brain perivascular space imaging across the human lifespan

Enlarged perivascular spaces (PVS) are considered a biomarker for vascular pathology and are observed in normal aging and neurological conditions; however, research on the role of PVS in health and disease are hindered by the lack of knowledge regarding the normative time course of PVS alterations with age. To this end, we characterized the influence of age, sex and cognitive performance on PVS anatomical characteristics in a large cross-sectional cohort (∼1400) of healthy subjects between 8 and 90 years of age using multimodal structural MRI data. Our results show age is associated with wider and more numerous MRI-visible PVS over the course of the lifetime with spatially-varying patterns of PVS enlargement trajectories. In particular, regions with low PVS volume fraction in childhood are associated with rapid age-related PVS enlargement (e.g., temporal regions), while regions with high PVS volume fraction in childhood are associated with minimal age-related PVS alterations (e.g., limbic regions). PVS burden was significantly elevated in males compared to females with differing morphological time courses with age. Together, these findings contribute to our understanding of perivascular physiology across the healthy lifespan and provide a normative reference for the spatial distribution of PVS enlargement patterns to which pathological alterations can be compared.

Brain Enlarged Perivascular Spaces as Imaging Biomarkers of Cerebrovascular Disease: A Clinical Narrative Review

Perivascular spaces or Virchow-Robin spaces form pathways along the subarachnoid spaces that facilitate the effective clearance of brain metabolic by-products through intracellular exchange and drainage of cerebrospinal fluid. Best seen on magnetic resonance imaging of the brain, enlarged perivascular spaces (EPVSs) are increasingly recognized as potential imaging biomarkers of neurological conditions. EPVSs are an established subtype of cerebral small-vessel disease; however, their associations with other cerebrovascular disorders are yet to be fully understood. In particular, there has been great interest in the association between the various parameters of EPVSs, such as number, size, and topography, and vascular neurological conditions. Studies have identified cross-sectional and longitudinal relationships between EPVS parameters and vascular events, such as ischemic stroke (both clinical and silent), intracerebral hemorrhage, vascular risk factors, such as age and hypertension, and neurodegenerative processes, such as vascular dementia and Alzheimer disease. However, these studies are limited by heterogeneity of data and the lack of consistent results across studied populations. Existing meta-analyses also fail to provide uniformity of results. We performed a qualitative narrative review with an aim to provide an overview of the associations between EPVSs and cerebrovascular diseases, which may help recognize gaps in our knowledge, inform the design of future studies, and advance the role of EPVSs as imaging biomarkers.

A critical guide to the automated quantification of perivascular spaces in magnetic resonance imaging

The glymphatic system is responsible for waste clearance in the brain. It is comprised of perivascular spaces (PVS) that surround penetrating blood vessels. These spaces are filled with cerebrospinal fluid and interstitial fluid, and can be seen with magnetic resonance imaging. Various algorithms have been developed to automatically label these spaces in MRI. This has enabled volumetric and morphological analyses of PVS in healthy and disease cohorts. However, there remain inconsistencies between PVS measures reported by different methods of automated segmentation. The present review emphasizes that importance of voxel-wise evaluation of model performance, mainly with the Sørensen Dice similarity coefficient. Conventional count correlations for model validation are inadequate if the goal is to assess volumetric or morphological measures of PVS. The downside of voxel-wise evaluation is that it requires manual segmentations that require large amounts of time to produce. One possible solution is to derive these semi-automatically. Additionally, recommendations are made to facilitate rigorous development and validation of automated PVS segmentation models. In the application of automated PVS segmentation tools, publication of image quality metrics, such as the contrast-to-noise ratio, alongside descriptive statistics of PVS volumes and counts will facilitate comparability between studies. Lastly, a head-to-head comparison between two algorithms, applied to two cohorts of astronauts reveals how results can differ substantially between techniques.

Assessment of perivascular space filtering methods using a three-dimensional computational model

Growing interest surrounds the assessment of perivascular spaces (PVS) on magnetic resonance imaging (MRI) and their validation as a clinical biomarker of adverse brain health. Nonetheless, the limits of validity of current state-of-the-art segmentation methods are still unclear. Here, we propose an open-source three-dimensional computational framework comprising 3D digital reference objects and evaluate the performance of three PVS filtering methods under various spatiotemporal imaging considerations (including sampling, motion artefacts, and Rician noise). Specifically, we study the performance of the Frangi, Jerman and RORPO filters in enhancing PVS-like structures to facilitate segmentation. Our findings were three-fold. First, as long as voxels are isotropic, RORPO outperforms the other two filters, regardless of imaging quality. Unlike the Frangi and Jerman filters, RORPO's performance does not deteriorate as PVS volume increases. Second, the performance of all "vesselness" filters is heavily influenced by imaging quality, with sampling and motion artefacts being the most damaging for these types of analyses. Third, none of the filters can distinguish PVS from other hyperintense structures (e.g. white matter hyperintensities, stroke lesions, or lacunes) effectively, the area under precision-recall curve dropped substantially (Frangi: from 94.21 [IQR 91.60, 96.16] to 43.76 [IQR 25.19, 63.38]; Jerman: from 94.51 [IQR 91.90, 95.37] to 58.00 [IQR 35.68, 64.87]; RORPO: from 98.72 [IQR 95.37, 98.96] to 71.87 [IQR 57.21, 76.63] without and with other hyperintense structures, respectively). The use of our computational model enables comparing segmentation methods and identifying their advantages and disadvantages, thereby providing means for testing and optimising pipelines for ongoing and future studies.

Topological relationships between perivascular spaces and progression of white matter hyperintensities: A pilot study in a sample of the Lothian Birth Cohort 1936

Enlarged perivascular spaces (PVS) and white matter hyperintensities (WMH) are features of cerebral small vessel disease which can be seen in brain magnetic resonance imaging (MRI). Given the associations and proposed mechanistic link between PVS and WMH, they are hypothesized to also have topological proximity. However, this and the influence of their spatial proximity on WMH progression are unknown. We analyzed longitudinal MRI data from 29 out of 32 participants (mean age at baseline = 71.9 years) in a longitudinal study of cognitive aging, from three waves of data collection at 3-year intervals, alongside semi-automatic segmentation masks for PVS and WMH, to assess relationships. The majority of deep WMH clusters were found adjacent to or enclosing PVS (waves-1: 77%; 2: 76%; 3: 69%), especially in frontal, parietal, and temporal regions. Of the WMH clusters in the deep white matter that increased between waves, most increased around PVS (waves-1-2: 73%; 2-3: 72%). Formal statistical comparisons of severity of each of these two SVD markers yielded no associations between deep WMH progression and PVS proximity. These findings may suggest some deep WMH clusters may form and grow around PVS, possibly reflecting the consequences of impaired interstitial fluid drainage via PVS. The utility of these relationships as predictors of WMH progression remains unclear.

Computer-Aided Extraction of Select MRI Markers of Cerebral Small Vessel Disease: A Systematic Review

Cerebral small vessel disease (CSVD) is a major vascular contributor to cognitive impairment in ageing, including dementias. Imaging remains the most promising method for in vivo studies of CSVD. To replace the subjective and laborious visual rating approaches, emerging studies have applied state-of-the-art artificial intelligence to extract imaging biomarkers of CSVD from MRI scans. We aimed to summarise published computer-aided methods for the examination of three imaging biomarkers of CSVD, namely cerebral microbleeds (CMB), dilated perivascular spaces (PVS), and lacunes of presumed vascular origin. Seventy classical image processing, classical machine learning, and deep learning studies were identified. Transfer learning and weak supervision techniques have been applied to accommodate the limitations in the training data. While good performance metrics were achieved in local datasets, there have not been generalisable pipelines validated in different research and/or clinical cohorts. Future studies could consider pooling data from multiple sources to increase data size and diversity, and evaluating performance using both image processing metrics and associations with clinical measures.

MRI-Visible Perivascular Spaces Associated With Cognitive Impairment in Military Veterans With Traumatic Brain Injury Mediated by CSF P-Tau

Objective

To investigate the association of MRI-visible perivascular spaces (PVS) with cognitive impairment in military veterans with traumatic brain injury (TBI), and whether cerebrospinal fluid (CSF) p-tau and Aβ mediate this effect.

Materials and Methods

We included 55 Vietnam War veterans with a history of TBI and 52 non-TBI Vietnam War veterans from the Department of Defense Alzheimer's Disease Neuroimaging Initiative (ADNI) database. All the subjects had brain MRI, CSF p-tau, Aβ, and neuropsychological examinations. MRI-visible PVS number and grade were rated on MRI in the centrum semiovale (CSO-PVS) and basal ganglia (BG-PVS). Multiple linear regression was performed to assess the association between MRI-visible PVS and cognitive impairment and the interaction effect of TBI. Additionally, mediation effect of CSF biomarkers on the relationship between MRI-visible PVS and cognitive impairment was explored in TBI group.

Results

Compared with military control, TBI group had higher CSO-PVS number (p = 0.001), CSF p-tau (p = 0.022) and poorer performance in verbal memory (p = 0.022). High CSO-PVS number was associated with poor verbal memory in TBI group (β = -0.039, 95% CI -0.062, -0.016), but not in military control group (β = 0.019, 95% CI -0.004, 0.043) (p-interaction = 0.003). Further mediation analysis revealed that CSF p-tau had a significant indirect effect (β = -0.009, 95% CI: -0.022 -0.001, p = 0.001) and mediated 18.75% effect for the relationship between CSO-PVS and verbal memory in TBI group.

Conclusion

MRI-visible CSO-PVS was more common in Vietnam War veterans with a history of TBI and was associated with poor verbal memory, mediated partially by CSF p-tau.

Imaging perivascular space structure and function using brain MRI

In this article, we provide an overview of current neuroimaging methods for studying perivascular spaces (PVS) in humans using brain MRI. In recent years, an increasing number of studies highlighted the role of PVS in cerebrospinal/interstial fluid circulation and clearance of cerebral waste products and their association with neurological diseases. Novel strategies and techniques have been introduced to improve the quantification of PVS and to investigate their function and morphological features in physiological and pathological conditions. After a brief introduction on the anatomy and physiology of PVS, we examine the latest technological developments to quantitatively analyze the structure and function of PVS in humans with MRI. We describe the applications, advantages, and limitations of these methods, providing guidance and suggestions on the acquisition protocols and analysis techniques that can be applied to study PVS in vivo. Finally, we review the human neuroimaging studies on PVS across the normative lifespan and in the context of neurological disorders.

Body mass index, time of day and genetics affect perivascular spaces in the white matter

The analysis of cerebral perivascular spaces (PVS) using magnetic resonance imaging (MRI) allows to explore in vivo their contributions to neurological disorders. To date the normal amount and distribution of PVS in healthy human brains are not known, thus hampering our ability to define with confidence pathogenic alterations. Furthermore, it is unclear which biological factors can influence the presence and size of PVS on MRI. We performed exploratory data analysis of PVS volume and distribution in a large population of healthy individuals (n = 897, age = 28.8 ± 3.7). Here we describe the global and regional amount of PVS in the white matter, which can be used as a reference for clinicians and researchers investigating PVS and may help the interpretation of the structural changes affecting PVS in pathological states. We found a relatively high inter-subject variability in the PVS amount in this population of healthy adults (range: 1.31-14.49 cm3). The PVS volume was higher in older and male individuals. Moreover, we identified body mass index, time of day, and genetics as new elements significantly affecting PVS in vivo under physiological conditions, offering a valuable foundation to future studies aimed at understanding the physiology of perivascular flow.

Quantity and Morphology of Perivascular Spaces: Associations With Vascular Risk Factors and Cerebral Small Vessel Disease

BACKGROUND

Perivascular spaces (PVSs) are important component of the brain glymphatic system. While visual rating has been widely used to assess PVS, computational measures may have higher sensitivity for capturing PVS characteristics under disease conditions.

PURPOSE

To compute quantitative and morphological PVS features and to assess their associations with vascular risk factors and cerebral small vessel disease (CSVD).

STUDY TYPE

Prospective.

POPULATION

One hundred sixty-one middle-aged/later middle-aged subjects (age = 60.4 ± 7.3).

SEQUENCE

3D T1-weighted, T2-weighted and T2-FLAIR sequences, and susceptibility-weighted multiecho gradient-echo sequence on a 3 T scanner.

ASSESSMENT

Automated PVS segmentation was performed on sub-millimeter T2-weighted images. Quantitative and morphological PVS features were calculated in white matter (WM) and basal ganglia (BG) regions, including volume, count, size, length (L_maj ), width (L_min ), and linearity. Visual PVS scores were also acquired for comparison.

STATISTICAL TESTS

Simple and multiple linear regression analyses were used to explore the associations among variables.

RESULTS

WM-PVS visual score and count were associated with hypertension (β = 0.161, P < 0.05; β = 0.193, P < 0.05), as were BG-PVS rating score, volume, count and L_min (β = 0.197, P < 0.05; β = 0.170, P < 0.05; β = 0.200, P < 0.05; β = 0.172, P < 0.05). WM-PVS size was associated with diabetes (β = 0.165, P < 0.05). WM-PVS and BG-PVS were associated with CSVD markers, especially white matter hyperintensities (WMHs) (P < 0.05). Multiple regression analysis showed that WM/BG-PVS quantitative measures were widely associated with vascular risk factors and CSVD markers (P < 0.05). Morphological measures were associated with WMH severity in WM region and also associated with lacunes and microbleeds (P < 0.05) in BG region.

DATA CONCLUSION

These novel PVS measures may capture mild PVS alterations driven by different pathologies.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

Correlation Between Hippocampal Enlarged Perivascular Spaces and Cognition in Non-dementic Elderly Population

Background and aims: The pathophysiology of hippocampal enlarged perivascular spaces (H-EPVS) and its relationship to cognitive impairment is largely unknown. This study aimed to investigate the relationship between H-EPVS and cognition in non-dementic elderly population.

Methods: A total of 109 subjects were prospectively enrolled. The eligibilities for inclusion were age from 55 to 85 years and Mini-Mental Status Examination score of ≥26. The Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), Montreal Cognitive Assessment, transcranial Doppler (TCD), and brain magnetic resonance imaging results were evaluated. H-EPVS was categorized in a three-degree scale: degree 0 (no), degree 1 (1,2), and degree 2 (>2). The associations between H-EPVS and TCD parameters/cognitive test profiles were analyzed.

Results: The mean age was 65.2 years, and 52.3% subjects were men. H-EPVS was found to be associated with age (degree 2 vs. degree 1 vs. degree 0, 69.20 ± 6.93 vs. 65.70 ± 5.75 vs. 63.80 ± 5.43; p = 0.030) and ADAS-Cog memory score (degree 2 vs. degree 1 vs. degree 0, 14.88 ± 4.27 vs. 12.49 ± 4.56 vs. 11.4 ± 4.23; p = 0.037). However, the pulsatility index was not related to the degree of H-EPVS. Multivariate analysis revealed medial temporal atrophy (MTA) scale score was independently associated with ADAS-Cog memory score (MTA scale sum ≥4, p = 0.011) but not with the degree of H-EPVS. MTA scale score showed correlation with H-EPVS (r = 0.273, p = 0.004).

Conclusions: Aging was associated with the development of H-EPVS in non-dementic elderly population. Memory function was found to be associated with MTA but not with the degree of H-EPVS.

Automatic spatial estimation of white matter hyperintensities evolution in brain MRI using disease evolution predictor deep neural networks

Previous studies have indicated that white matter hyperintensities (WMH), the main radiological feature of small vessel disease, may evolve (i.e., shrink, grow) or stay stable over a period of time. Predicting these changes are challenging because it involves some unknown clinical risk factors that leads to a non-deterministic prediction task. In this study, we propose a deep learning model to predict the evolution of WMH from baseline to follow-up (i.e., 1-year later), namely "Disease Evolution Predictor" (DEP) model, which can be adjusted to become a non-deterministic model. The DEP model receives a baseline image as input and produces a map called "Disease Evolution Map" (DEM), which represents the evolution of WMH from baseline to follow-up. Two DEP models are proposed, namely DEP-UResNet and DEP-GAN, which are representatives of the supervised (i.e., need expert-generated manual labels to generate the output) and unsupervised (i.e., do not require manual labels produced by experts) deep learning algorithms respectively. To simulate the non-deterministic and unknown parameters involved in WMH evolution, we modulate a Gaussian noise array to the DEP model as auxiliary input. This forces the DEP model to imitate a wider spectrum of alternatives in the prediction results. The alternatives of using other types of auxiliary input instead, such as baseline WMH and stroke lesion loads are also proposed and tested. Based on our experiments, the fully supervised machine learning scheme DEP-UResNet regularly performed better than the DEP-GAN which works in principle without using any expert-generated label (i.e., unsupervised). However, a semi-supervised DEP-GAN model, which uses probability maps produced by a supervised segmentation method in the learning process, yielded similar performances to the DEP-UResNet and performed best in the clinical evaluation. Furthermore, an ablation study showed that an auxiliary input, especially the Gaussian noise, improved the performance of DEP models compared to DEP models that lacked the auxiliary input regardless of the model's architecture. To the best of our knowledge, this is the first extensive study on modelling WMH evolution using deep learning algorithms, which deals with the non-deterministic nature of WMH evolution.

Perivascular spaces in the brain: anatomy, physiology and pathology

Perivascular spaces include a variety of passageways around arterioles, capillaries and venules in the brain, along which a range of substances can move. Although perivascular spaces were first identified over 150 years ago, they have come to prominence recently owing to advances in knowledge of their roles in clearance of interstitial fluid and waste from the brain, particularly during sleep, and in the pathogenesis of small vessel disease, Alzheimer disease and other neurodegenerative and inflammatory disorders. Experimental advances have facilitated in vivo studies of perivascular space function in intact rodent models during wakefulness and sleep, and MRI in humans has enabled perivascular space morphology to be related to cognitive function, vascular risk factors, vascular and neurodegenerative brain lesions, sleep patterns and cerebral haemodynamics. Many questions about perivascular spaces remain, but what is now clear is that normal perivascular space function is important for maintaining brain health. Here, we review perivascular space anatomy, physiology and pathology, particularly as seen with MRI in humans, and consider translation from models to humans to highlight knowns, unknowns, controversies and clinical relevance.

Quantitative measurements of enlarged perivascular spaces in the brain are associated with retinal microvascular parameters in older community-dwelling subjects

BACKGROUND

Perivascular Spaces (PVS) become increasingly visible with advancing age on brain MRI, yet their relationship to morphological changes in the underlying microvessels remains poorly understood. Retinal and cerebral microvessels share morphological and physiological properties. We compared computationally-derived PVS morphologies with retinal vessel morphologies in older people.

METHODS

We analysed data from community-dwelling individuals who underwent multimodal brain MRI and retinal fundus camera imaging at mean age 72.55 years (SD=0.71). We assessed centrum semiovale PVS computationally to determine PVS total volume and count, and mean per-subject individual PVS length, width and size. We analysed retinal images using the VAMPIRE software suite, obtaining the Central Retinal Artery and Vein Equivalents (CRVE and CRAE), Arteriole-to-Venule ratio (AVR), and fractal dimension (FD) of both eyes. We investigated associations using general linear models, adjusted for age, gender, and major vascular risk factors.

RESULTS

In 381 subjects with all measures, increasing total PVS volume and count were associated with decreased CRAE in the left eye (volume β=-0.170, count β=-0.184, p<0.001). No associations of PVS with CRVE were found. The PVS total volume, individual width and size increased with decreasing FD of the arterioles (a) and venules (v) of the left eye (total volume: FDa β=-0.137, FDv β=-0.139, p<0.01; width: FDa β=-0.144, FDv β=-0.158, p<0.01; size: FDa β=-0.157, FDv β=-0.162, p<0.01).

CONCLUSIONS

Increase in PVS number and size visible on MRI reflect arteriolar narrowing and lower retinal arteriole and venule branching complexity, both markers of impaired microvascular health. Computationally-derived PVS metrics may be an early indicator of failing vascular health and should be tested in longitudinal studies.

Computational quantification of brain perivascular space morphologies: Associations with vascular risk factors and white matter hyperintensities. A study in the Lothian Birth Cohort 1936

BACKGROUND AND PURPOSE

Perivascular Spaces (PVS), also known as Virchow-Robin spaces, seen on structural brain MRI, are important fluid drainage conduits and are associated with small vessel disease (SVD). Computational quantification of visible PVS may enable efficient analyses in large datasets and increase sensitivity to detect associations with brain disorders. We assessed the associations of computationally-derived PVS parameters with vascular factors and white matter hyperintensities (WMH), a marker of SVD.

PARTICIPANTS

Community dwelling individuals (n = 700) from the Lothian Birth Cohort 1936 who had multimodal brain MRI at age 72.6 years (SD = 0.7).

METHODS

We assessed PVS computationally in the centrum semiovale and deep corona radiata on T2-weighted images. The computationally calculated measures were the total PVS volume and count per subject, and the mean individual PVS length, width and size, per subject. We assessed WMH by volume and visual Fazekas scores. We compared PVS visual rating to PVS computational metrics, and tested associations between each PVS measure and vascular risk factors (hypertension, diabetes, cholesterol), vascular history (cardiovascular disease and stroke), and WMH burden, using generalized linear models, which we compared using coefficients, confidence intervals and model fit.

RESULTS

In 533 subjects, the computational PVS measures correlated positively with visual PVS ratings (PVS count r = 0.59; PVS volume r = 0.61; PVS mean length r = 0.55; PVS mean width r = 0.52; PVS mean size r = 0.47). PVS size and width were associated with hypertension (OR 1.22, 95% CI [1.03 to 1.46] and 1.20, 95% CI [1.01 to 1.43], respectively), and stroke (OR 1.34, 95% CI [1.08 to 1.65] and 1.36, 95% CI [1.08 to 1.71], respectively). We found no association between other PVS measures and diabetes, hypercholesterolemia or cardiovascular disease history. Computational PVS volume, length, width and size were more strongly associated with WMH (PVS mean size versus WMH Fazekas score β = 0.66, 95% CI [0.59 to 0.74] and versus WMH volume β = 0.43, 95% CI [0.38 to 0.48]) than computational PVS count (WMH Fazekas score β = 0.21, 95% CI [0.11 to 0.3]; WMH volume β = 0.14, 95% CI [0.09 to 0.19]) or visual score. Individual PVS size showed the strongest association with WMH.

CONCLUSIONS

Computational measures reflecting individual PVS size, length and width were more strongly associated with WMH, stroke and hypertension than computational count or visual PVS score. Multidimensional computational PVS metrics may increase sensitivity to detect associations of PVS with risk exposures, brain lesions and neurological disease, provide greater anatomic detail and accelerate understanding of disorders of brain fluid and waste clearance.

Image processing approaches to enhance perivascular space visibility and quantification using MRI

Imaging the perivascular spaces (PVS), also known as Virchow-Robin space, has significant clinical value, but there remains a need for neuroimaging techniques to improve mapping and quantification of the PVS. Current technique for PVS evaluation is a scoring system based on visual reading of visible PVS in regions of interest, and often limited to large caliber PVS. Enhancing the visibility of the PVS could support medical diagnosis and enable novel neuroscientific investigations. Increasing the MRI resolution is one approach to enhance the visibility of PVS but is limited by acquisition time and physical constraints. Alternatively, image processing approaches can be utilized to improve the contrast ratio between PVS and surrounding tissue. Here we combine T1- and T2-weighted images to enhance PVS contrast, intensifying the visibility of PVS. The Enhanced PVS Contrast (EPC) was achieved by combining T1- and T2-weighted images that were adaptively filtered to remove non-structured high-frequency spatial noise. EPC was evaluated on healthy young adults by presenting them to two expert readers and also through automated quantification. We found that EPC improves the conspicuity of the PVS and aid resolving a larger number of PVS. We also present a highly reliable automated PVS quantification approach, which was optimized using expert readings.

Perivascular Spaces Volume in Sporadic and Hereditary (Dutch-Type) Cerebral Amyloid Angiopathy

Background and Purpose- Magnetic resonance imaging visible perivascular spaces in the centrum semiovale (CSO-PVS) have been associated with cerebral amyloid angiopathy (CAA).We aimed to further confirm this link by evaluating CSO-PVS volume in pathologically-demonstrated sporadic and genetically-demonstrated hereditary forms of the disease. Methods- We studied a retrospective hospital-based cohort consisting of 63 individuals aged >55 having brain magnetic resonance imaging and pathological assessment of CAA (mean age, 73.6±8.5; 46% female), and a separate cohort consisting of 26 carriers, and 28 noncarriers of the hereditary cerebral hemorrhage with amyloidosis-Dutch type (mean age, 46.7±12.8; 61.1% female). CSO-PVS volume was quantified on a single magnetic resonance imaging slice using a computer-assisted segmentation method and expressed as the relative volume of the intracranial volume in that particular slice (CSO-PVS relative volume). We compared CSO-PVS relative volume (1) between subjects with and without the disease in both cohorts; (2) between non-CAA, CAA without hemorrhage, and CAA with hemorrhage cases in the sporadic CAA cohort. All variables reaching P<0.1 in bivariate analyses were entered in logistic regression models. Results- In both sporadic and Dutch cohorts, cases with CAA had significantly higher CSO-PVS relative volume than cases without (median [IQR]: 3.7% [2.5-5.3] versus 1.8% [1.2-2.4], P<0.0001; 3.8% [0.6-6.2] versus 0.7% [0.4-1.6], P=0.007; respectively). In linear regression models, sporadic CAA was associated with higher CSO-PVS relative volume ( P=0.008). In the sporadic CAA cohort, compared with non-CAA cases, CSO-PVS relative volume was higher in both CAA with hemorrhage and without hemorrhage (4.4% [2.6-6.1] and 3% [2.4-3.6] versus 1.8% [1.2-2.4], P<0.001 and P=0.005, respectively). Higher CSO-PVS relative volume was associated with CAA in regression models, both when hemorrhage was present (odds ratio, 2.63; [95% confidence interval, 1.33-5.18]; P=0.005) and absent (odds ratio, 4.55; [95% confidence interval, 0.98-21.04]; P=0.05). Conclusions- Increased CSO-PVS volume is a consistent magnetic resonance imaging marker of cerebrovascular amyloid deposition and a promising diagnostic tool for sporadic CAA without hemorrhagic manifestations.

Association Between Cerebral Small Vessel Disease and Central Motor Conduction Time in Patients with Vascular Risk

BACKGROUND AND PURPOSE

Cerebral small vessel disease (CSVD) is related to motor function disturbance. It includes several types: lacunar infarction, white matter hyperintensity, cerebral microbleeds (CMBs), and enlarged perivascular spaces (EPVS). Transcranial magnetic stimulation (TMS) has been successfully used to evaluate the function of the pyramidal tract. Central motor conduction time (CMCT) is one of the indicators of pyramidal tract dysfunction in motor evoked potential (MEP). The aim of this study was to investigate the association between each type of CSVD and CMCT.

METHODS

We enrolled 350 patients with vascular risk factors or a history of cerebrovascular events, who showed signs of CSVD in magnetic resonance imaging in the prospective registry. Among them, 138 patients agreed to the evaluation of MEP. CMCT, resting motor threshold (RMT), and silent period are indicators of the function of motor pathways in MEP. A total of 276 hemispheres were divided into 45 symptomatic hemispheres with a history of pyramidal tract dysfunction and 231 without it. Correlation between each type of CSVD and CMCT were examined in total, symptomatic, and asymptomatic hemispheres.

RESULTS

The mean age was 70.5 ± 10.3 (mean ± SD) years, and 89 (65%) were men. In the symptomatic hemisphere, CMCT and RMT were significantly higher than in the asymptomatic hemisphere. In the symptomatic hemisphere, significant association was observed between the number of EPVS in the white matter and CMCT (R² = 0.201, p < .01).

CONCLUSIONS

In the symptomatic hemispheres, CMCT was associated with the number of EPVS in the white matter. The EPVS in the white matter may be involved in the motor disturbance due to CSVD.

MRI-based evaluation of structural degeneration in the ageing brain: Pathophysiology and assessment

Advances in MRI technology have significantly contributed to our ability to understand the process of brain ageing, allowing us to track and assess changes that occur during normal ageing and neurological conditions. This paper focuses on reviewing structural changes of the ageing brain that are commonly seen using MRI, summarizing the pathophysiology, prevalence, and neuroanatomical distribution of changes including atrophy, lacunes, white matter lesions, and dilated perivascular spaces. We also review the clinically accessible methodology for assessing these MRI-based changes, covering visual rating scales, as well computer-aided and fully automated methods. Subsequently, we consider novel assessment methods designed to evaluate changes across the whole brain, and finally discuss new directions in this field of research.

Understanding the role of the perivascular space in cerebral small vessel disease

Small vessel diseases (SVDs) are a group of disorders that result from pathological alteration of the small blood vessels in the brain, including the small arteries, capillaries and veins. Of the 35-36 million people that are estimated to suffer from dementia worldwide, up to 65% have an SVD component. Furthermore, SVD causes 20-25% of strokes, worsens outcome after stroke and is a leading cause of disability, cognitive impairment and poor mobility. Yet the underlying cause(s) of SVD are not fully understood. Magnetic resonance imaging has confirmed enlarged perivascular spaces (PVS) as a hallmark feature of SVD. In healthy tissue, these spaces are proposed to form part of a complex brain fluid drainage system which supports interstitial fluid exchange and may also facilitate clearance of waste products from the brain. The pathophysiological signature of PVS and what this infers about their function and interaction with cerebral microcirculation, plus subsequent downstream effects on lesion development in the brain has not been established. Here we discuss the potential of enlarged PVS to be a unique biomarker for SVD and related brain disorders with a vascular component. We propose that widening of PVS suggests presence of peri-vascular cell debris and other waste products that form part of a vicious cycle involving impaired cerebrovascular reactivity, blood-brain barrier dysfunction, perivascular inflammation and ultimately impaired clearance of waste proteins from the interstitial fluid space, leading to accumulation of toxins, hypoxia, and tissue damage. Here, we outline current knowledge, questions and hypotheses regarding understanding the brain fluid dynamics underpinning dementia and stroke through the common denominator of SVD.

Quantitative MRI of Perivascular Spaces at 3T for Early Diagnosis of Mild Cognitive Impairment

BACKGROUND AND PURPOSE

The limitations inherent in the current methods of diagnosing mild cognitive impairment have constrained the use of early therapeutic interventions to delay the progression of mild cognitive impairment to dementia. This study evaluated whether quantifying enlarged perivascular spaces observed on MR imaging can help differentiate those with mild cognitive impairment from cognitively healthy controls and, thus, have an application in the diagnosis of mild cognitive impairment.

MATERIALS AND METHODS

We automated the identification of enlarged perivascular spaces in brain MR Images using a custom quantitative program designed with Matlab. We then quantified the densities of enlarged perivascular spaces for patients with mild cognitive impairment (n = 14) and age-matched cognitively healthy controls (n = 15) and compared them to determine whether the density of enlarged perivascular spaces can serve as an imaging surrogate for mild cognitive impairment diagnosis.

RESULTS

Quantified as a percentage of volume fraction (v/v%), densities of enlarged perivascular spaces were calculated to be 2.82 ± 0.40 v/v% for controls and 4.17 ± 0.57 v/v% for the mild cognitive impairment group in the subcortical brain (P < .001), and 2.74 ± 0.57 v/v% for the controls and 3.90 ± 0.62 v/v% for the mild cognitive impairment cohort in the basal ganglia (P < .001). Maximum intensity projections exhibited a visually conspicuous difference in the distributions of enlarged perivascular spaces for a patient with mild cognitive impairment and a control patient. By means of receiver operating characteristic curve analysis, we determined the sensitivity and specificity of using enlarged perivascular spaces as a differentiating biomarker between mild cognitive impairment and controls to be 92.86% and 93.33%, respectively.

CONCLUSIONS

The density of enlarged perivascular spaces was found to be significantly higher in those with mild cognitive impairment compared with age-matched healthy control subjects. The density of enlarged perivascular spaces, therefore, may be a useful imaging biomarker for the diagnosis of mild cognitive impairment.

[Small vessel cerebrovascular disease]

Small vessel vascular disease is a spectrum of different conditions that includes lacunar infarction, alteration of deep white matter, or microbleeds. Hypertension is the main risk factor, although the atherothrombotic lesion may be present, particularly in large-sized lacunar infarctions along with other vascular risk factors. MRI findings are characteristic and the lesions authentic biomarkers that allow differentiating the value of risk factors and defining their prognostic value.

Multi-channel multi-scale fully convolutional network for 3D perivascular spaces segmentation in 7T MR images

Accurate segmentation of perivascular spaces (PVSs) is an important step for quantitative study of PVS morphology. However, since PVSs are the thin tubular structures with relatively low contrast and also the number of PVSs is often large, it is challenging and time-consuming for manual delineation of PVSs. Although several automatic/semi-automatic methods, especially the traditional learning-based approaches, have been proposed for segmentation of 3D PVSs, their performance often depends on the hand-crafted image features, as well as sophisticated preprocessing operations prior to segmentation (e.g., specially defined regions-of-interest (ROIs)). In this paper, a novel fully convolutional neural network (FCN) with no requirement of any specified hand-crafted features and ROIs is proposed for efficient segmentation of PVSs. Particularly, the original T2-weighted 7T magnetic resonance (MR) images are first filtered via a non-local Haar-transform-based line singularity representation method to enhance the thin tubular structures. Both the original and enhanced MR images are used as multi-channel inputs to complementarily provide detailed image information and enhanced tubular structural information for the localization of PVSs. Multi-scale features are then automatically learned to characterize the spatial associations between PVSs and adjacent brain tissues. Finally, the produced PVS probability maps are recursively loaded into the network as an additional channel of inputs to provide the auxiliary contextual information for further refining the segmentation results. The proposed multi-channel multi-scale FCN has been evaluated on the 7T brain MR images scanned from 20 subjects. The experimental results show its superior performance compared with several state-of-the-art methods.

Perivascular Spaces Segmentation in Brain MRI Using Optimal 3D Filtering

Perivascular Spaces (PVS) are a feature of Small Vessel Disease (SVD), and are an important part of the brain’s circulation and glymphatic drainage system. Quantitative analysis of PVS on Magnetic Resonance Images (MRI) is important for understanding their relationship with neurological diseases. In this work, we propose a segmentation technique based on the 3D Frangi filtering for extraction of PVS from MRI. We used ordered logit models and visual rating scales as alternative ground truth for Frangi filter parameter optimization and evaluation. We optimized and validated our proposed models on two independent cohorts, a dementia sample (N = 20) and patients who previously had mild to moderate stroke (N = 48). Results demonstrate the robustness and generalisability of our segmentation method. Segmentation-based PVS burden estimates correlated well with neuroradiological assessments (Spearman’s ρ = 0.74, p < 0.001), supporting the potential of our proposed method.

Perivascular Spaces in Old Age: Assessment, Distribution, and Correlation with White Matter Hyperintensities

BACKGROUND AND PURPOSE

The visual rating scales for perivascular spaces vary considerably. We sought to develop a new scale for visual assessment of perivascular spaces and to further describe their distribution and association with white matter hyperintensities in old age.

MATERIALS AND METHODS

This population-based study included 530 individuals who did not have dementia and were not institutionalized (age, ≥60 years or older; mean age, 70.7 years; 58.9% women) who were living in central Stockholm, Sweden. A semiquantitative visual rating scale was developed to score the number and size of visible perivascular spaces in 7 brain regions in each hemisphere. A modified Scheltens visual rating scale was used to assess white matter hyperintensities.

RESULTS

The global scores for perivascular spaces ranged from 4-32 for number, 3-22 for size, and 7-54 for the combination of number and size. The weighted κ statistics for the intra- and interrater reliability both were 0.77. The global score for the number of perivascular spaces increased with advancing age (P < .001). The scores for the number of perivascular spaces in the basal ganglia and subinsular regions were significantly correlated with the load of white matter hyperintensities, especially in lobar and deep white matter regions (partial correlation coefficients, >0.223; P < .01).

CONCLUSIONS

The new visual rating scale for perivascular spaces shows excellent intra- and interrater reliability. The number of perivascular spaces globally and, especially, in the basal ganglia, is correlated with the load of lobar and deep white matter hyperintensities, supporting the view that perivascular spaces are a marker for cerebral small-vessel disease.

Structured Learning for 3-D Perivascular Space Segmentation Using Vascular Features

OBJECTIVE

The goal of this paper is to automatically segment perivascular spaces (PVSs) in brain from high-resolution 7T magnetic resonance (MR) images.

METHODS

We propose a structured-learning-based segmentation framework to extract the PVSs from high-resolution 7T MR images. Specifically, we integrate three types of vascular filter responses into a structured random forest for classifying voxels into two categories, i.e., PVS and background. In addition, we propose a novel entropy-based sampling strategy to extract informative samples in the background for training an explicit classification model. Since the vascular filters can extract various vascular features, even thin and low-contrast structures can be effectively extracted from noisy backgrounds. Moreover, continuous and smooth segmentation results can be obtained by utilizing patch-based structured labels.

RESULTS

The performance of our proposed method is evaluated on 19 subjects with 7T MR images, with the Dice similarity coefficient reaching 66%.

CONCLUSION

The joint use of entropy-based sampling strategy, vascular features, and structured learning can improve the segmentation accuracy.

SIGNIFICANCE

Instead of manual annotation, our method provides an automatic way for PVS segmentation. Moreover, our method can be potentially used for other vascular structure segmentation because of its data-driven property.

Quantification of perivascular spaces at 7T: A potential MRI biomarker for epilepsy

PURPOSE

7T (7T) magnetic resonance imaging (MRI) facilitates the visualization of the brain with resolution and contrast beyond what is available at conventional clinical field strengths, enabling improved detection and quantification of small structural features such as perivascular spaces (PVSs). The distribution of PVSs, detected in vivo at 7T, may act as a biomarker for the effects of epilepsy. In this work, we systematically quantify the PVSs in the brains of epilepsy patients and compare them to healthy controls.

METHODS

T₂-weighted turbo spin echo images were obtained at 7T on 21 epilepsy patients and 17 healthy controls. For all subjects, PVSs were manually marked on Osirix image analysis software. Marked PVSs with diameter≥0.5mm were then mapped by hemisphere and lobe. The asymmetry index (AI) was calculated for each region and the maximum asymmetry index (|AI_max|) was reported for each subject. The asymmetry in epilepsy subjects was compared to that of controls, and the region with highest asymmetry was compared to the suspected seizure onset zone.

RESULTS

There was a significant difference between the |AI_max| in epilepsy subjects and in controls (p=0.016). In 72% of patients, the region or lobe of the brain showing maximum PVS asymmetry was the same as the region containing the suspected seizure onset zone.

CONCLUSION

These findings suggest that epilepsy may be associated with significantly asymmetric distribution of PVSs in the brain. Furthermore, the region of maximal asymmetry of the PVSs may help provide localization or confirmation of the seizure onset zone.

Brain lesion segmentation through image synthesis and outlier detection

Cerebral small vessel disease (SVD) can manifest in a number of ways. Many of these result in hyperintense regions visible on T2-weighted magnetic resonance (MR) images. The automatic segmentation of these lesions has been the focus of many studies. However, previous methods tended to be limited to certain types of pathology, as a consequence of either restricting the search to the white matter, or by training on an individual pathology. Here we present an unsupervised abnormality detection method which is able to detect abnormally hyperintense regions on FLAIR regardless of the underlying pathology or location. The method uses a combination of image synthesis, Gaussian mixture models and one class support vector machines, and needs only be trained on healthy tissue. We evaluate our method by comparing segmentation results from 127 subjects with SVD with three established methods and report significantly superior performance across a number of metrics.

MR Imaging-based Multimodal Autoidentification of Perivascular Spaces (mMAPS): Automated Morphologic Segmentation of Enlarged Perivascular Spaces at Clinical Field Strength

Purpose To describe a fully automated segmentation method that yields object-based morphologic estimates of enlarged perivascular spaces (ePVSs) in clinical-field-strength (3.0-T) magnetic resonance (MR) imaging data. Materials and Methods In this HIPAA-compliant study, MR imaging data were obtained with a 3.0-T MR imager in research participants without dementia (mean age, 85.3 years; range, 70.4-101.2 years) who had given written informed consent. This method is built on (a) relative normalized white matter, ventricular and cortical signal intensities within T1-weighted, fluid-attenuated inversion recovery, T2-weighted, and proton density data and (b) morphologic (width, volume, linearity) characterization of each resultant cluster. Visual rating was performed by three raters, including one neuroradiologist, after established single-section guidelines. Correlations between visual counts and automated counts, as well session-to-session correlation of counts within each participant, were assessed with the Pearson correlation coefficient r. Results There was a significant correlation between counts by visual raters and automated detection of ePVSs in the same section (r = 0.65, P < .001; r = 0.69, P < .001; and r = 0.54, P < .01 for raters 1, 2, and 3, respectively). With regard to visual ratings and whole-brain count consistency, average visual rating scores were highly correlated with automated detection of total burden volume (r = 0.58, P < .01) and total ePVS number (r = 0.76, P < .01). Morphology of clusters across 28 data sets was consistent with published radiographic estimates of ePVS; mean width of clusters segmented was 3.12 mm (range, 1.7-13.5 mm). Conclusion This MR imaging-based method for multimodal autoidentification of perivascular spaces yields individual whole-brain morphologic characterization of ePVS in clinical MR imaging data and is an important tool in the detailed assessment of these features. ^© RSNA, 2017 Online supplemental material is available for this article.

Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

In the brain, enlarged perivascular spaces (PVS) relate to cerebral small vessel disease (SVD), poor cognition, inflammation and hypertension. We propose a fully automatic scheme that uses a support vector machine (SVM) to classify the burden of PVS in the basal ganglia (BG) region as low or high. We assess the performance of three different types of descriptors extracted from the BG region in T2-weighted MRI images: (i) statistics obtained from Wavelet transform's coefficients, (ii) local binary patterns and (iii) bag of visual words (BoW) based descriptors characterizing local keypoints obtained from a dense grid with the scale-invariant feature transform (SIFT) characteristics. When the latter were used, the SVM classifier achieved the best accuracy (81.16%). The output from the classifier using the BoW descriptors was compared with visual ratings done by an experienced neuroradiologist (Observer 1) and by a trained image analyst (Observer 2). The agreement and cross-correlation between the classifier and Observer 2 (κ = 0.67 (0.58-0.76)) were slightly higher than between the classifier and Observer 1 (κ = 0.62 (0.53-0.72)) and comparable between both the observers (κ = 0.68 (0.61-0.75)). Finally, three logistic regression models using clinical variables as independent variable and each of the PVS ratings as dependent variable were built to assess how clinically meaningful were the predictions of the classifier. The goodness-of-fit of the model for the classifier was good (area under the curve (AUC) values: 0.93 (model 1), 0.90 (model 2) and 0.92 (model 3)) and slightly better (i.e. AUC values: 0.02 units higher) than that of the model for Observer 2. These results suggest that, although it can be improved, an automatic classifier to assess PVS burden from brain MRI can provide clinically meaningful results close to those from a trained observer.

Segmentation of Perivascular Spaces Using Vascular Features and Structured Random Forest from 7T MR Image

Quantitative analysis of perivascular spaces (PVSs) is important to reveal the correlations between cerebrovascular lesions and neurodegenerative diseases. In this study, we propose a learning-based segmentation framework to extract the PVSs from high-resolution 7T MR images. Specifically, we integrate three types of vascular filter responses into a structured random forest for classifying voxels into PVS and background. In addition, we also propose a novel entropy-based sampling strategy to extract informative samples in the background for training the classification model. Since various vascular features can be extracted by the three vascular filters, even thin and low-contrast structures can be effectively extracted from the noisy background. Moreover, continuous and smooth segmentation results can be obtained by utilizing the patch-based structured labels. The segmentation performance is evaluated on 19 subjects with 7T MR images, and the experimental results demonstrate that the joint use of entropy-based sampling strategy, vascular features and structured learning improves the segmentation accuracy, with the Dice similarity coefficient reaching 66 %.

Update on cerebral small vessel disease: a dynamic whole-brain disease

Cerebral small vessel disease (CSVD) is a very common neurological disease in older people. It causes stroke and dementia, mood disturbance and gait problems. Since it is difficult to visualise CSVD pathologies in vivo, the diagnosis of CSVD has relied on imaging findings including white matter hyperintensities, lacunar ischaemic stroke, lacunes, microbleeds, visible perivascular spaces and many haemorrhagic strokes. However, variations in the use of definition and terms of these features have probably caused confusion and difficulties in interpreting results of previous studies. A standardised use of terms should be encouraged in CSVD research. These CSVD features have long been regarded as different lesions, but emerging evidence has indicated that they might share some common intrinsic microvascular pathologies and therefore, owing to its diffuse nature, CSVD should be regarded as a 'whole-brain disease'. Single antiplatelet (for acute lacunar ischaemic stroke) and management of traditional risk factors still remain the most important therapeutic and preventive approach, due to limited understanding of pathophysiology in CSVD. Increasing evidence suggests that new studies should consider drugs that target endothelium and blood-brain barrier to prevent and treat CSVD. Epidemiology of CSVD might differ in Asian compared with Western populations (where most results and guidelines about CSVD and stroke originate), but more community-based data and clear stratification of stroke types are required to address this.

Reproducibility and variability of quantitative magnetic resonance imaging markers in cerebral small vessel disease

Brain imaging is essential for the diagnosis and characterization of cerebral small vessel disease. Several magnetic resonance imaging markers have therefore emerged, providing new information on the diagnosis, progression, and mechanisms of small vessel disease. Yet, the reproducibility of these small vessel disease markers has received little attention despite being widely used in cross-sectional and longitudinal studies. This review focuses on the main small vessel disease-related markers on magnetic resonance imaging including: white matter hyperintensities, lacunes, dilated perivascular spaces, microbleeds, and brain volume. The aim is to summarize, for each marker, what is currently known about: (1) its reproducibility in studies with a scan-rescan procedure either in single or multicenter settings; (2) the acquisition-related sources of variability; and, (3) the techniques used to minimize this variability. Based on the results, we discuss technical and other challenges that need to be overcome in order for these markers to be reliably used as outcome measures in future clinical trials. We also highlight the key points that need to be considered when designing multicenter magnetic resonance imaging studies of small vessel disease.

11.7 T MR Imaging Revealed Dilatation of Virchow-Robin Spaces within Hippocampus in Maternally Lipopolysaccharide-exposed Rats

PURPOSE

11.7 Tesla MRI was examined to detect Virchow-Robin spaces (VRSs) smaller than 100 μm in the rat brain. The effects of maternal exposure to lipopolysaccharide (LPS) were evaluated on basis of the number of dilated VRSs in the offspring rat brain.

METHODS

T₂-weighted MRI with an in-plane resolution up to 78 μm (repetition time = 5000 ms, echo time = 35 ms, slice thickness = 250 μm, imaging plane, coronal) was applied to identify VRSs. The dilated VRSs were counted in the rat brain at 5 and 10 weeks of age. The dams of half the number in each group were treated with LPS during pregnancy, and the remaining half was employed as control. LPS injection in gestation period was used to simulate maternal infections, the method of which was widely accepted as a rat model inducing neuropsychiatric disorders in the offspring. Effects of LPS exposure on the offspring rat brain were statistically investigated.

RESULTS

VRSs as small as 78 μm were successfully detected by the ultra high-field MRI. All dilated VRSs were observed within lacunosum molecular layer of hippocampus, and molecular and granular layers of dentate gyrus around hippocampal fissure. In juvenile rats (5 weeks of age), the number of dilated VRSs was significantly increased in the prenatal LPS exposed rat brain (12.9 ± 2.4, n = 7) than in the control (5.3 ± 1.5, n = 7, P < 0.05), while in young adult rats (10 weeks of age), there was no significant difference in the number between the prenatal LPS exposed rat brain (3.6 ± 0.9, n = 5) and the control (2.6 ± 0.4, n = 5).

CONCLUSION

The results of the present study suggested that maternal infection might cause dilatation of VRSs through neural damages especially in the dentate gyrus of the offspring rats. Thus, ultra high-field MRI can offer a promising diagnostic tool capable of determining the location of neonatal brain damage caused by maternal infections.

Segmentation of perivascular spaces in 7T MR image using auto-context model with orientation-normalized features

Quantitative study of perivascular spaces (PVSs) in brain magnetic resonance (MR) images is important for understanding the brain lymphatic system and its relationship with neurological diseases. One of the major challenges is the accurate extraction of PVSs that have very thin tubular structures with various directions in three-dimensional (3D) MR images. In this paper, we propose a learning-based PVS segmentation method to address this challenge. Specifically, we first determine a region of interest (ROI) by using the anatomical brain structure and the vesselness information derived from eigenvalues of image derivatives. Then, in the ROI, we extract a number of randomized Haar features which are normalized with respect to the principal directions of the underlying image derivatives. The classifier is trained by the random forest model that can effectively learn both discriminative features and classifier parameters to maximize the information gain. Finally, a sequential learning strategy is used to further enforce various contextual patterns around the thin tubular structures into the classifier. For evaluation, we apply our proposed method to the 7T brain MR images scanned from 17 healthy subjects aged from 25 to 37. The performance is measured by voxel-wise segmentation accuracy, cluster-wise classification accuracy, and similarity of geometric properties, such as volume, length, and diameter distributions between the predicted and the true PVSs. Moreover, the accuracies are also evaluated on the simulation images with motion artifacts and lacunes to demonstrate the potential of our method in segmenting PVSs from elderly and patient populations. The experimental results show that our proposed method outperforms all existing PVS segmentation methods.

Imaging the Perivascular Space as a Potential Biomarker of Neurovascular and Neurodegenerative Diseases

Although the brain lacks conventional lymphatic vessels found in peripheral tissue, evidence suggests that the space surrounding the vasculature serves a similar role in the clearance of fluid and metabolic waste from the brain. With aging, neurodegeneration, and cerebrovascular disease, these microscopic perivascular spaces can become enlarged, allowing for visualization and quantification on structural MRI. The purpose of this review is to: (i) describe some of the recent pre-clinical findings from basic science that shed light on the potential neurophysiological mechanisms driving glymphatic and perivascular waste clearance, (ii) review some of the pathobiological etiologies that may lead to MRI-visible enlarged perivascular spaces (ePVS), (iii) describe the possible clinical implications of ePVS, (iv) evaluate existing qualitative and quantitative techniques used for measuring ePVS burden, and (v) propose future avenues of research that may improve our understanding of this potential clinical neuroimaging biomarker for fluid and metabolic waste clearance dysfunction in neurodegenerative and neurovascular diseases.

Automatic Detection of White Matter Hyperintensities in Healthy Aging and Pathology Using Magnetic Resonance Imaging: A Review

White matter hyperintensities (WMH) are commonly seen in the brain of healthy elderly subjects and patients with several neurological and vascular disorders. A truly reliable and fully automated method for quantitative assessment of WMH on magnetic resonance imaging (MRI) has not yet been identified. In this paper, we review and compare the large number of automated approaches proposed for segmentation of WMH in the elderly and in patients with vascular risk factors. We conclude that, in order to avoid artifacts and exclude the several sources of bias that may influence the analysis, an optimal method should comprise a careful preprocessing of the images, be based on multimodal, complementary data, take into account spatial information about the lesions and correct for false positives. All these features should not exclude computational leanness and adaptability to available data.

Development and initial evaluation of a semi-automatic approach to assess perivascular spaces on conventional magnetic resonance images

PURPOSE

Perivascular spaces (PVS) are associated with ageing, cerebral small vessel disease, inflammation and increased blood brain barrier permeability. Most studies to date use visual rating scales to assess PVS, but these are prone to observer variation.

METHODS

We developed a semi-automatic computational method that extracts PVS on bilateral ovoid basal ganglia (BG) regions on intensity-normalised T2-weighted magnetic resonance images. It uses Analyze™10.0 and was applied to 100 mild stroke patients' datasets. We used linear regression to test association between BGPVS count, volume and visual rating scores; and between BGPVS count & volume, white matter hyperintensity (WMH) rating scores (periventricular: PVH; deep: DWMH) & volume, atrophy rating scores and brain volume.

RESULTS

In the 100 patients WMH ranged from 0.4 to 119ml, and total brain tissue volume from 0.65 to 1.45l. BGPVS volume increased with BGPVS count (67.27, 95%CI [57.93 to 76.60], p<0.001). BGPVS count was positively associated with WMH visual rating (PVH: 2.20, 95%CI [1.22 to 3.18], p<0.001; DWMH: 1.92, 95%CI [0.99 to 2.85], p<0.001), WMH volume (0.065, 95%CI [0.034 to 0.096], p<0.001), and whole brain atrophy visual rating (1.01, 95%CI [0.49 to 1.53], p<0.001). BGPVS count increased as brain volume (as % of ICV) decreased (-0.33, 95%CI [-0.53 to -0.13], p=0.002).

COMPARISON WITH EXISTING METHOD

BGPVS count and volume increased with the overall increase of BGPVS visual scores (2.11, 95%CI [1.36 to 2.86] for count and 0.022, 95%CI [0.012 to 0.031] for volume, p<0.001). Distributions for PVS count and visual scores were also similar.

CONCLUSIONS

This semi-automatic method is applicable to clinical protocols and offers quantitative surrogates for PVS load. It shows good agreement with a visual rating scale and confirmed that BGPVS are associated with WMH and atrophy measurements.

Cerebrospinal fluid diversion in patients with enlarged Virchow-Robin spaces without ventriculomegaly

OBJECTIVE

Enlarged Virchow-Robin spaces (eVRS) are an MRI biomarker in several neurological diseases of inflammatory, neurodegenerative, vascular, metabolic, or genetic origin. We report on a further condition in which eVRS were observed in patients with an ongoing chronic hydrocephalus-like clinical picture without ventriculomegaly who improved after CSF diversion, and we discuss the possible mechanisms underlying this finding.

MATERIALS AND METHODS

A retrospective study of seven patients presenting progressive gait, cognitive, and urinary disturbances in association with eVRS was undertaken.

RESULTS

All patients presented an Evans ratio <0.30 and >20 eVRS at the level of basal ganglia and periventricular parenchyma as assessed by T2-weighted MRI. All patients underwent prolonged external lumbar drainage (PELD) with good response. Six patients received ventriculoperitoneal shunt with improvement of their clinical status compared to that before PELD (follow-up: 8-58 months, mean 24.6). The seventh patient did not undergo ventriculoperitoneal shunt and received a second PELD with persistent improvement (follow-up: 14 months).

CONCLUSIONS

Our results indicate that a mechanism involving CSF accumulation and stasis in the subarachnoid space was at least a concurrent factor of this clinical picture. This study should stimulate new perspectives on the role of CSF disturbances in the pathogenesis of diseases associated with VRS enlargement.

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.