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

Associations of glymphatic function with structural network and cognition in self-limited epilepsy with centrotemporal spikes

PURPOSE

To investigate glymphatic function by Virchow-Robin space (VRS) counts and volume in patients with newly diagnosed self-limited epilepsy with centrotemporal spikes (SeLECTS) and evaluate its relationship with structural connectivity and cognitive impairment.

METHODS

Thirty-two children with SeLECTS and thirty-two age- and sex-matched typically developing (TD) children were enrolled in this study. VRS counts and volume were quantified. Structural networks were constructed and the topological metrics were analyzed. Wechsler Intelligence Scale (WISC) was used to assess cognitive function in all participants. Correlation analysis assessed the association between VRS counts and volume, network connectivity, and cognitive impairment. Mediation effects of topological metrics of the structural networks on the relationship between glymphatic function and cognitive impairment were explored.

RESULTS

Patients with SeLECTS showed a higher VRS counts, VRS volume, and global shortest path length (Lp); they also showed a lower global efficiency (Eg). VRS counts and volume were significantly correlated with full-scale intelligence quotient (FIQ) (r_VRS counts = -0.520, r_VRS volume = -0.639), performance intelligence quotient (PIQ) (r_VRS counts = -0.693, r_VRS volume = -0.597), verbal intelligence quotient (VIQ) (r_VRS counts = -0.713, r_VRS volume = -0.699), Eg (r_VRS counts = -0.499, r_VRS volume = -0.490), and Lp (r_VRS volume = 0.671) in patients with SeLECTS. Eg mediated 24.59% of the effects for the relationship between VRS volume and FIQ.

CONCLUSION

Glymphatic function may be impaired in SeLECTS reflected by VRS counts and volume. Glymphatic dysfunction may result in cognitive impairment by disrupting structural connectivity in SeLECTS.

Systematic review and meta-analysis of automated methods for quantifying enlarged perivascular spaces in the brain

Research into magnetic resonance imaging (MRI)-visible perivascular spaces (PVS) has recently increased, as results from studies in different diseases and populations are cementing their association with sleep, disease phenotypes, and overall health indicators. With the establishment of worldwide consortia and the availability of large databases, computational methods that allow to automatically process all this wealth of information are becoming increasingly relevant. Several computational approaches have been proposed to assess PVS from MRI, and efforts have been made to summarise and appraise the most widely applied ones. We systematically reviewed and meta-analysed all publications available up to September 2023 describing the development, improvement, or application of computational PVS quantification methods from MRI. We analysed 67 approaches and 60 applications of their implementation, from 112 publications. The two most widely applied were the use of a morphological filter to enhance PVS-like structures, with Frangi being the choice preferred by most, and the use of a U-Net configuration with or without residual connections. Older adults or population studies comprising adults from 18 years old onwards were, overall, more frequent than studies using clinical samples. PVS were mainly assessed from T2-weighted MRI acquired in 1.5T and/or 3T scanners, although combinations using it with T1-weighted and FLAIR images were also abundant. Common associations researched included age, sex, hypertension, diabetes, white matter hyperintensities, sleep and cognition, with occupation-related, ethnicity, and genetic/hereditable traits being also explored. Despite promising improvements to overcome barriers such as noise and differentiation from other confounds, a need for joined efforts for a wider testing and increasing availability of the most promising methods is now paramount.

Glymphatic Imaging in Pediatrics

The glymphatic system, which facilitates cerebrospinal fluid (CSF) flow through the brain parenchyma, is important for brain development and waste clearance. Advances in imaging techniques, particularly magnetic resonance imaging, have make it possible to evaluate glymphatic structures and functions in vivo. Recently, several studies have focused on the development and alterations of the glymphatic system in pediatric disorders. This review discusses the development of the glymphatic system, advances of imaging techniques and their applications in pediatric disorders. First, the results of the reviewed studies indicate that the development of the glymphatic system is a long-lasting process that continues into adulthood. Second, there is a need for improved glymphatic imaging techniques that are non-invasive and fast to improve suitability for pediatric applications, as some of existing methods use contrast injection and are susceptible to motion artifacts from long scanning times. Several novel techniques are potentially feasible for pediatric patients and may be used in the future. Third, the glymphatic dysfunction is associated with a large number of pediatric disorders, although only a few have recently been investigated. In conclusion, research on the pediatric glymphatic system remains an emerging field. The preliminary applications of glymphatic imaging techniques have provided unique insight into the pathological mechanism of pediatric diseases, but mainly limited in visualization of enlarged perivascular spaces and morphological measurements on CSF volumes. More in-depth studies on glymphatic functions are required to improve our understanding of the mechanisms underlying brain development and pediatric diseases. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 3.

Assessment of the glymphatic function in children with attention-deficit/hyperactivity disorder

OBJECTIVES

Whether the alternation of the glymphatic system exists in neurodevelopmental disease still remains unclear. In this study, we investigated structural and functional changes in the glymphatic system in the treatment-naïve attention-deficit/hyperactivity disorder (ADHD) children by quantitatively measuring the Virchow-Robin spaces (VRS) volume and diffusion tensor image-analysis along the perivascular space (DTI-ALPS).

METHODS

Forty-seven pediatric ADHD patients and 52 age- and gender-matched typically developing (TD) children were recruited in this prospective study. The VRS volume was calculated using a semi-automated approach in axial T2-weighted images. Diffusivities along the x-, y-, and z-axes in the projection, association, and subcortical neural fiber areas were measured. The ALPS index, a ratio that accentuated water diffusion along the perivascular space, was calculated. The Mann-Whitney U test was used to compare the quantitative parameters; Pearson's correlation was used to analyze the correlation with clinical symptoms.

RESULTS

The cerebral VRS volume (mean, 15.514 mL vs. 11.702 mL) and the VRS volume ratio in the ADHD group were larger than those in the TD group (all p < 0.001). The diffusivity along the x-axis in association fiber area and ALPS index were significantly smaller in the ADHD group vs. TD group (mean, 1.40 vs.1.59, p < 0.05 after false discovery rate adjustment). Besides, the ALPS index was related to inattention symptoms of ADHD (r =  - 0.323, p < 0.05).

CONCLUSIONS

Our study suggests that the glymphatic system alternation may participate in the pathogenesis of ADHD, which may be a new research direction for exploring the mechanisms of psycho-behavioral developmental disorders. Moreover, the VRS volume and ALPS index could be used as the metrics for diagnosing ADHD.

CLINICAL RELEVANCE STATEMENT

Considering the potential relevance of the glymphatic system for exploring the mechanisms of attention deficit/hyperactivity, the Virchow-Robin spaces volume and the analysis along the perivascular space index could be used as additional metrics for diagnosing the disorder.

KEY POINTS

• Increased Virchow-Robin space volume and decreased analysis along the perivascular space index were found in the treatment-naïve attention-deficit/hyperactivity disorder children. • The results of this study indicate that the glymphatic system alternation may have a valuable role in the pathogenesis of attention-deficit/hyperactivity disorder. • The analysis along the perivascular space index is correlated with inattention symptoms of attention-deficit/hyperactivity disorder children.

Perivascular spaces and where to find them - MR imaging and evaluation methods

BACKGROUND

 Perivascular spaces (synonym: Virchow-Robin spaces) were first described over 150 years ago. They are defined as the fluid-filled spaces surrounding the small penetrating cerebral vessels. They gained growing scientific interest especially with the postulation of the so-called glymphatic system and their possible role in neurodegenerative and neuroinflammatory diseases.

METHODS

 PubMed was used for a systematic search with a focus on literature regarding MRI imaging and evaluation methods of perivascular spaces. Studies on human in-vivo imaging were included with a focus on studies involving healthy populations. No time frame was set. The nomenclature in the literature is very heterogeneous with terms like "large", "dilated", "enlarged" perivascular spaces whereas borders and definitions often remain unclear. This work generally talks about perivascular spaces.

RESULTS

 This review article discusses the morphologic MRI characteristics in different sequences. With the continual improvement of image quality, more and tinier structures can be depicted in detail. Visual analysis and semi or fully automated segmentation methods are briefly discussed.

CONCLUSION

 If they are looked for, perivascular spaces are apparent in basically every cranial MRI examination. Their physiologic or pathologic value is still under debate.

KEY POINTS

  · Perivascular spaces can be seen in basically every cranial MRI examination.. · Primarily T2-weighend sequences are used for visual analysis. Additional sequences are helpful for distinction from their differential diagnoses.. · There are promising approaches for the semi or fully automated segmentation of perivascular spaces with the possibility to collect more quantitative parameters..

CITATION FORMAT

· Seehafer S, Larsen N, Aludin S et al. Perivascular spaces and where to find them - MRI imaging and evaluation methods. Fortschr Röntgenstr 2024; DOI: 10.1055/a-2254-5651.

Imaging the neurovascular unit in health and neurodegeneration: a scoping review of interdependencies between MRI measures

The neurovascular unit (NVU) is a complex structure that facilitates nutrient delivery and metabolic waste clearance, forms the blood-brain barrier (BBB), and supports fluid homeostasis in the brain. The integrity of NVU subcomponents can be measured in vivo using magnetic resonance imaging (MRI), including quantification of enlarged perivascular spaces (ePVS), BBB permeability, cerebral perfusion and extracellular free water. The breakdown of NVU subparts is individually associated with aging, pathology, and cognition. However, how these subcomponents interact as a system, and how interdependencies are impacted by pathology remains unclear. This systematic scoping review identified 26 studies that investigated the inter-relationships between multiple subcomponents of the NVU in nonclinical and neurodegenerative populations using MRI. A further 112 studies investigated associations between the NVU and white matter hyperintensities (WMH). We identify two putative clusters of NVU interdependencies: a 'vascular' cluster comprising BBB permeability, perfusion and basal ganglia ePVS; and a 'fluid' cluster comprising ePVS, free water and WMH. Emerging evidence suggests that subcomponent coupling within these clusters may be differentially related to aging, neurovascular injury or neurodegenerative pathology.

Correlation between enlarged perivascular space and brain white matter hyperintensities in patients with recent small subcortical infarct

BACKGROUND

This study aimed to investigate the correlation between enlarged perivascular space (EPVS) and white matter hyperintensities (WMH) at different locations in patients with recent small subcortical infarct (RSSI).

METHODS

Data were collected from patients with RSSI who were hospitalized at Changzhou Second People's Hospital between October 2020 and December 2021. All patients underwent cranial magnetic resonance imaging, and the grades of EPVS and WMH were assessed, including basal ganglia EPVS (BG-EPVS), centrum semiovale EPVS (CSO-EPVS), deep WMH (DWMH), and periventricular WMH (PWMH). The volumes of EPVS and WMH at different locations were quantified using 3D Slicer software. Patients were grouped according to the severity of BG-EPVS and CSO-EPVS. Univariate and multivariate analyses were used to analyze the relationship between EPVS and WMH.

RESULTS

A total of 215 patients with RSSI were included in the analysis. Patients with moderate-to-severe BG-EPVS had higher DWMH and PWMH severity than those with mild BG-EPVS, both in terms of volume and grade. There was no significant difference in WMH severity between patients with mild CSO-EPVS and those with moderate-to-severe CSO-EPVS. Multivariate analysis indicated that after adjustments were made for confounding factors, DWMH volume (β = 0.311; 95% CI, 0.089-0.400; p = .002) and PWMH volume (β = 0.296; 95% CI, 0.083-0.424; p = .004) were independently associated with BG-EPVS. Pearson correlation showed that PWMH volume (r = .589; p < .001) and DWMH volume (r = .596; p < .001) were positively related to BG-EPVS volume.

CONCLUSION

DWMH and PWMH are closely related to BG-EPVS in patients with RSSI.

The etiology and evolution of magnetic resonance imaging-visible perivascular spaces: Systematic review and meta-analysis

Objectives

Perivascular spaces have been involved in neuroinflammatory and neurodegenerative diseases. Upon a certain size, these spaces can become visible on magnetic resonance imaging (MRI), referred to as enlarged perivascular spaces (EPVS) or MRI-visible perivascular spaces (MVPVS). However, the lack of systematic evidence on etiology and temporal dynamics of MVPVS hampers their diagnostic utility as MRI biomarker. Thus, the goal of this systematic review was to summarize potential etiologies and evolution of MVPVS.

Methods

In a comprehensive literature search, out of 1,488 unique publications, 140 records assessing etiopathogenesis and dynamics of MVPVS were eligible for a qualitative summary. 6 records were included in a meta-analysis to assess the association between MVPVS and brain atrophy.

Results

Four overarching and partly overlapping etiologies of MVPVS have been proposed: (1) Impairment of interstitial fluid circulation, (2) Spiral elongation of arteries, (3) Brain atrophy and/or perivascular myelin loss, and (4) Immune cell accumulation in the perivascular space. The meta-analysis in patients with neuroinflammatory diseases did not support an association between MVPVS and brain volume measures [R: −0.15 (95%-CI −0.40–0.11)]. Based on few and mostly small studies in tumefactive MVPVS and in vascular and neuroinflammatory diseases, temporal evolution of MVPVS is slow.

Conclusion

Collectively, this study provides high-grade evidence for MVPVS etiopathogenesis and temporal dynamics. Although several potential etiologies for MVPVS emergence have been proposed, they are only partially supported by data. Advanced MRI methods should be employed to further dissect etiopathogenesis and evolution of MVPVS. This can benefit their implementation as an imaging biomarker.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=346564, identifier CRD42022346564.

Association between visualization of the perivascular space and morphological changes in the brain among the community-dwelling elderly

PURPOSE

We aimed to investigate the association between perivascular space (PVS) visible on MRI and brain atrophy or morphological change using quantitative indexes.

METHOD

This population-based cohort study included 216 older participants. The PVS in basal ganglia (BG-PVS) and cerebral white matter (WM-PVS) was evaluated using a four-point visual rating scale. We segmented brain parenchyma and CSF, and calculated the CSF/intracranial volume ratio, which represents atrophic change. WM lesions were classified using the Fazekas scale. We introduced a new category "idiopathic normal pressure hydrocephalus (iNPH)-like conformation", which was based on two quantitative indexes: Evans index and callosal angle. The association between PVS grade and demographic or morphological factors was evaluated.

RESULTS

A stepwise increase in the CSF/intracranial volume ratio with BG-PVS grade progression and a stepwise decrease with WM-PVS grade progression were observed. A higher CSF/intracranial volume ratio was significantly related to a higher BG-PVS grade in a univariate analysis, but this significance disappeared in a multivariate analysis. The iNPH-like group was significantly related to a lower WM-PVS grade in a univariate analysis, and this significance remained in a multivariate analysis.

CONCLUSIONS

The association between BG-PVS enlargement and atrophic changes was verified. On the contrary, WM-PVS showed a different trend, and a lower WM-PVS grade was associated with an iNPH-like conformation. This result implies that the less-visible WM-PVS on imaging as well as BG-PVS enlargement would reflect abnormal brain change.

Deep Learning Based Detection of Enlarged Perivascular Spaces on Brain MRI

Deep learning has been demonstrated effective in many neuroimaging applications. However, in many scenarios, the number of imaging sequences capturing information related to small vessel disease lesions is insufficient to support data-driven techniques. Additionally, cohort-based studies may not always have the optimal or essential imaging sequences for accurate lesion detection. Therefore, it is necessary to determine which imaging sequences are crucial for precise detection. This study introduces a deep learning framework to detect enlarged perivascular spaces (ePVS) and aims to find the optimal combination of MRI sequences for deep learning-based quantification. We implemented an effective lightweight U-Net adapted for ePVS detection and comprehensively investigated different combinations of information from SWI, FLAIR, T1-weighted (T1w), and T2-weighted (T2w) MRI sequences. The experimental results showed that T2w MRI is the most important for accurate ePVS detection, and the incorporation of SWI, FLAIR and T1w MRI in the deep neural network had minor improvements in accuracy and resulted in the highest sensitivity and precision (sensitivity =0.82, precision =0.83). The proposed method achieved comparable accuracy at a minimal time cost compared to manual reading. The proposed automated pipeline enables robust and time-efficient readings of ePVS from MR scans and demonstrates the importance of T2w MRI for ePVS detection and the potential benefits of using multimodal images. Furthermore, the model provides whole-brain maps of ePVS, enabling a better understanding of their clinical correlates compared to the clinical rating methods within only a couple of brain regions.

Automated Methods for Detecting and Quantitation of Enlarged Perivascular spaces on MRI

The brain's glymphatic system is a network of intracerebral vessels that function to remove "waste products" such as degraded proteins from the brain. It comprises of the vasculature, perivascular spaces (PVS), and astrocytes. Poor glymphatic function has been implicated in numerous diseases; however, its contribution is still unknown. Efforts have been made to image the glymphatic system to further assess its role in the pathogenesis of different diseases. Numerous imaging modalities have been utilized including two-photon microscopy and contrast-enhanced magnetic resonance imaging (MRI). However, these are associated with limitations for clinical use. PVS form a part of the glymphatic system and can be visualized on standard MRI sequences when enlarged. It is thought that PVS become enlarged secondary to poor glymphatic drainage of metabolites. Thus, quantitating PVS could be a good surrogate marker for glymphatic function. Numerous manual rating scales have been developed to measure the PVS number and size on MRI scans; however, these are associated with many limitations. Instead, automated methods have been created to measure PVS more accurately in different diseases. In this review, we discuss the imaging techniques currently available to visualize the glymphatic system as well as the automated methods currently available to measure PVS, and the strengths and limitations associated with each technique. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.

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.

Perivascular spaces as a marker of disease severity and neurodegeneration in patients with behavioral variant frontotemporal dementia

Background

Behavioural Variant Frontotemporal Dementia (bvFTD) is a rapidly progressing neurodegenerative proteinopathy. Perivascular spaces (PVS) form a part of the brain’s glymphatic clearance system. When enlarged due to poor glymphatic clearance of toxic proteins, PVS become larger and more conspicuous on MRI. Therefore, enlarged PVS may be a useful biomarker of disease severity and progression in neurodegenerative proteinopathies such as bvFTD. This study aimed to determine the utility of PVS as a biomarker of disease progression in patients with bvFTD.

Materials and methods

Serial baseline and week 52 MRIs acquired from ten patients with bvFTD prospectively recruited and followed in a Phase 1b open label trial of sodium selenate for bvFTD were used in this study. An automated algorithm quantified PVS on MRI, which was visually inspected and validated by a member of the study team. The number and volume of PVS were extracted and mixed models used to assess the relationship between PVS burden and other measures of disease (cognition, carer burden scale, protein biomarkers). Additional exploratory analysis investigated PVS burden in patients who appeared to not progress over the 12 months of selenate treatment (i.e., “non-progressors”).

Results

Overall, PVS cluster number (ß = −3.27, CI [−7.80 – 1.27], p = 0.267) and PVS volume (ß = −36.8, CI [−84.9 – 11.3], p = 0.171) did not change over the paired MRI scans 12 months apart. There was association between cognition total composite scores and the PVS burden (PVS cluster ß = −0.802e^–3, CI [9.45e^–3 – −6.60e^–3, p ≤ 0.001; PVS volume ß = −1.30e^–3, CI [−1.55e^–3 – −1.05e^–3], p ≤ 0.001), as well as between the change in the cognition total composite score and the change in PVS volume (ß = 4.36e^–3, CI [1.33e^–3 – 7.40e^–3], p = 0.046) over the trial period. There was a significant association between CSF t-tau and the number of PVS clusters (ß = 2.845, CI [0.630 – 5.06], p = 0.036). Additionally, there was a significant relationship between the change in CSF t-tau and the change in the number of PVS (ß = 1.54, CI [0.918 – 2.16], p < 0.001) and PVS volume (ß = 13.8, CI [6.37 – 21.1], p = 0.003) over the trial period. An association was found between the change in NfL and the change in PVS volume (ß = 1.40, CI [0.272 – 2.52], p = 0.045) over time. Within the “non-progressor” group (n = 7), there was a significant relationship between the change in the CSF total-tau (t-tau) levels and the change in the PVS burden (PVS cluster (ß = 1.46, CI [0.577 – 2.34], p = 0.014; PVS volume ß = 14.6, CI [3.86 – 25.4], p = 0.032) over the trial period. Additionally, there was evidence of a significant relationship between the change in NfL levels and the change in the PVS burden over time (PVS cluster ß = 0.296, CI [0.229 – 0.361], p ≤ 0.001; PVS volume ß = 3.67, CI [2.42 – 4.92], p = 0.002).

Conclusion

Analysis of serial MRI scans 12 months apart in patients with bvFTD demonstrated a relationship between PVS burden and disease severity as measured by the total cognitive composite score and CSF t-tau. Further studies are needed to confirm PVS as a robust marker of neurodegeneration in proteinopathies.

Effect of cerebral small vessel disease on cognitive impairment in Parkinson's disease

OBJECTIVES

To explore the association between cerebral small vessel disease (cSVD) and cognitive impairment (CI) in Parkinson's disease (PD).

METHODS

81 PD patients were recruited into the study from September 2018 to December 2020. The demographic characteristics and radiologic and laboratory data were collected. Cognitive assessments were carried out using the Montreal Cognitive Assessment. The association between cSVD and cognitive impairment was analyzed using univariate and binary logistic regression analysis.

RESULTS

The binary logistic regression analysis showed that, after correcting for age, educational years, hyperhomocysteinemia, hypertension, and diabetes mellitus, total cSVD scores (OR 1.55, 95% CI 1.07-2.27, P = 0.02), the presence of paraventricular white matter hyperintensity (PVH) (OR 11.78, 95% CI 3.08-45.01, P < 0.001), white matter hyperintensity (WMH) (OR 7.95, 95% CI 2.28-27.79, P = 0.001), and perivascular space (PVS) (OR 6.66, 95% CI 2.08-21.40, P = 0.001) were independent risk factors for PD-CI.

CONCLUSION

The presence of cSVD was associated with cognitive dysfunction in patients with PD. It may be beneficial to manage cSVD to prevent the progression of cognitive impairment in patients with PD.

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.

Enlarged Perivascular Spaces Are Negatively Associated With Montreal Cognitive Assessment Scores in Older Adults

Emerging evidence suggests that enlarged perivascular spaces (ePVS) may be a clinically significant neuroimaging marker of global cognitive function related to cerebral small vessel disease (cSVD). We tested this possibility by assessing the relationship between ePVS and both a standardized measure of global cognitive function, the Montreal Cognitive Assessment (MoCA), and an established marker of cSVD, white matter hyperintensity volume (WMH) volume. One hundred and eleven community-dwelling older adults (56-86) underwent neuroimaging and MoCA testing. Quantification of region-specific ePVS burden was performed using a previously validated visual rating method and WMH volumes were computed using the standard ADNI pipeline. Separate linear regression models were run with ePVS as a predictor of MoCA scores and whole brain WMH volume. Results indicated a negative association between MoCA scores and both total ePVS counts (P ≤ 0.001) and centrum semiovale ePVS counts (P ≤ 0.001), after controlling for other relevant cSVD variables. Further, WMH volumes were positively associated with total ePVS (P = 0.010), basal ganglia ePVS (P ≤ 0.001), and centrum semiovale ePVS (P = 0.027). Our results suggest that ePVS burden, particularly in the centrum semiovale, may be a clinically significant neuroimaging marker of global cognitive dysfunction related to cSVD.

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.

Associations Between Vascular Risk Factors and Perivascular Spaces in Adults with Intact Cognition, Mild Cognitive Impairment, and Dementia

BACKGROUND

Perivascular spaces (PVS) are fluid-filled compartments surrounding small intracerebral vessels that transport fluid and clear waste.

OBJECTIVE

We examined associations between PVS count, vascular and neurodegenerative risk factors, and cognitive status among the predominantly Hispanic participants of the FL-VIP Study of Alzheimer's Disease Risk.

METHODS

Using brain MRI (n = 228), we counted PVS in single axial image through the basal ganglia (BG) and centrum semiovale (CSO). PVS per region were scored as 0 (none), 1 (<10), 2 (11-20), 3 (21-40), and 4 (>40). Generalized linear models examined PVS associations with vascular risk factors and a composite vascular comorbidity risk (VASCom) score.

RESULTS

Our sample (mean age 72±8 years, 61% women, 60% Hispanic, mean education 15±4 years, 33% APOE4 carriers) was 59% hypertensive, 21% diabetic, 66% hypercholesteremic, and 30% obese. Mean VASCom score was 2.3±1.6. PVS scores ranged from 0-4 in the BG (mean 1.3±0.7) and CSO (mean 1.2±0.9), and 0-7 combined (mean 2.5±1.4). In multivariable regression models, BG PVS was associated with age (β= 0.03/year, p < 0.0001), Hispanic ethnicity (β= 0.29, p = 0.01), education (β= 0.04/year, p = 0.04), and coronary bypass surgery (β= 0.93, p = 0.02). CSO PVS only associated with age (β= 0.03/year, p < 0.01). APOE4 and amyloid-β were not associated with PVS.

CONCLUSION

BG PVS may be a marker of subclinical cerebrovascular disease. Further research is needed to validate associations and identify mechanisms linking BG PVS and cerebrovascular disease markers. PVS may be a marker of neurodegeneration despite our negative preliminary findings and more research is warranted. The association between BG PVS and Hispanic ethnicity also requires further investigation.

Automatic quantification of perivascular spaces in T2-weighted images at 7 T MRI

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Fully automated detection of perivascular spaces on 7T MRI.

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Good correlation with manual assessments of perivascular spaces.

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Quantitative measurements of PVS characteristics: density, length, and tortuosity.

Perivascular spaces (PVS) are believed to be involved in brain waste disposal. PVS are associated with cerebral small vessel disease. At higher field strengths more PVS can be observed, challenging manual assessment. We developed a method to automatically detect and quantify PVS.

A machine learning approach identified PVS in an automatically positioned ROI in the centrum semiovale (CSO), based on -resolution T2-weighted TSE scans. Next, 3D PVS tracking was performed in 50 subjects (mean age 62.9 years (range 27–78), 19 male), and quantitative measures were extracted. Maps of PVS density, length, and tortuosity were created. Manual PVS annotations were available to train and validate the automatic method.

Good correlation was found between the automatic and manual PVS count: ICC (absolute/consistency) is 0.64/0.75, and Dice similarity coefficient (DSC) is 0.61. The automatic method counts fewer PVS than the manual count, because it ignores the smallest PVS (length <2 mm). For 20 subjects manual PVS annotations of a second observer were available. Compared with the correlation between the automatic and manual PVS, higher inter-observer ICC was observed (0.85/0.88), but DSC was lower (0.49 in 4 persons). Longer PVS are observed posterior in the CSO compared with anterior in the CSO. Higher PVS tortuosity are observed in the center of the CSO compared with the periphery of the CSO.

Our fully automatic method can detect PVS in a 2D slab in the CSO, and extract quantitative PVS parameters by performing 3D tracking. This method enables automated quantitative analysis of PVS.

3D Segmentation of Perivascular Spaces on T1-Weighted 3 Tesla MR Images With a Convolutional Autoencoder and a U-Shaped Neural Network

We implemented a deep learning (DL) algorithm for the 3-dimensional segmentation of perivascular spaces (PVSs) in deep white matter (DWM) and basal ganglia (BG). This algorithm is based on an autoencoder and a U-shaped network (U-net), and was trained and tested using T1-weighted magnetic resonance imaging (MRI) data from a large database of 1,832 healthy young adults. An important feature of this approach is the ability to learn from relatively sparse data, which gives the present algorithm a major advantage over other DL algorithms. Here, we trained the algorithm with 40 T1-weighted MRI datasets in which all "visible" PVSs were manually annotated by an experienced operator. After learning, performance was assessed using another set of 10 MRI scans from the same database in which PVSs were also traced by the same operator and were checked by consensus with another experienced operator. The Sorensen-Dice coefficients for PVS voxel detection in DWM (resp. BG) were 0.51 (resp. 0.66), and 0.64 (resp. 0.71) for PVS cluster detection (volume threshold of 0.5 within a range of 0 to 1). Dice values above 0.90 could be reached for detecting PVSs larger than 10 mm3 and 0.95 for PVSs larger than 15 mm3. We then applied the trained algorithm to the rest of the database (1,782 individuals). The individual PVS load provided by the algorithm showed a high agreement with a semi-quantitative visual rating done by an independent expert rater, both for DWM and for BG. Finally, we applied the trained algorithm to an age-matched sample from another MRI database acquired using a different scanner. We obtained a very similar distribution of PVS load, demonstrating the interoperability of this algorithm.

Perivascular spaces and brain waste clearance systems: relevance for neurodegenerative and cerebrovascular pathology

Perivascular spaces (PVS) of the brain, often called Virchow-Robin spaces, comprise fluid, cells and connective tissue, and are externally limited by astrocytic endfeet. PVS are involved in clearing brain waste and belong to the "glymphatic" system and/or the "intramural periarterial drainage" pathway through the basement membranes of the arteries. Related brain waste clearance systems include the blood-brain barrier, scavenger cells, cerebrospinal fluid, perineural lymphatic drainage pathways and the newly characterised meningeal lymphatic vessels. Any functional abnormality of PVS or related clearance systems might lead to accumulation of brain waste. It has been postulated that PVS enlargement can be secondary to accumulation of β-amyloid. Lack of integrity of the vascular wall, microbleeds, cerebral amyloid angiopathy (CAA) and enlarged PVS often occur in the preclinical stages of Alzheimer's disease, preceding substantial brain atrophy. PVS enlargement in the form of état criblé at the basal ganglia has also been considered to reflect focal atrophy, most probably secondary to ischaemic injury, based upon both pathological and imaging arguments. In addition, distinct topographic patterns of enlarged PVS are related to different types of microangiopathy: CAA is linked to enlarged juxtacortical PVS, whereas subjects with vascular risk factors tend to have enlarged PVS in the basal ganglia. Therefore, enlarged PVS are progressively being regarded as a marker of neurodegenerative and cerebrovascular pathology. The present review addresses the evolving concept of PVS and brain waste clearance systems, the potential relevance of their dysfunction to neurodegenerative and cerebrovascular pathology, and potential therapeutic approaches of interest.

Quantification of visible Virchow-Robin spaces for detecting the functional status of the glymphatic system in children with newly diagnosed idiopathic generalized epilepsy

PURPOSE

The cerebral glymphatic system, particularly the Virchow-Robin Spaces (VRS), plays an important role in waste clearance from the brain. Idiopathic generalized epilepsy (IGE) is a common epilepsy type associated with blood-brain-barrier dysfunction, abnormal exchange of cerebrospinal fluid and interstitial fluid. These disorders may be reflected in the glymphatic system. Therefore, this study investigated the relationships between visible VRS on MRI and seizures, to detect changes in glymphatic function.

METHODS

We retrospectively included 32 children with newly diagnosed IGE and 30 controls aged 3-13 years. Visible VRS were identified using a custom-designed automated method. VRS counts and volume were quantified and compared between children with IGE and controls. Meanwhile, Correlations of VRS counts and volume with seizure duration and course after seizure onset were respectively explored via Spearman's coefficient (r).

RESULTS

In this study, visible VRS counts were higher in IGE than control group (VRS_{_epilepsy}, 234.34 ± 113.88 vs. VRS_{_control}, 111.83 ± 52.46; P < 0.001), as similar results were found in VRS volume (VRS_{_epilepsy}, 1377.47 ± 778.79 mm³ vs. VRS_{_control}, 795.153 ± 452.49 mm³; P = 0.001). Visible VRS counts and volume positively correlated with seizure duration (r_{_counts} = 0.638, r_{_volume} = 0.639; P < 0.001) and gradually decreased with time after seizure onset (r_{_counts} = -0.559, r__volume = -0.558; P < 0.001).

CONCLUSION

Epileptic seizures can induce changes in VRS counts and volume, which were associated with seizure duration and post-onset course. Quantitative metrics of VRS visible on MRI might be potential biomarkers for monitoring glymphatic function.

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.

Enhancement of Perivascular Spaces Using Densely Connected Deep Convolutional Neural Network

Perivascular spaces (PVS) in the human brain are related to various brain diseases. However, it is difficult to quantify them due to their thin and blurry appearance. In this paper, we introduce a deep-learning-based method, which can enhance a magnetic resonance (MR) image to better visualize the PVS. To accurately predict the enhanced image, we propose a very deep 3D convolutional neural network that contains densely connected networks with skip connections. The proposed networks can utilize rich contextual information derived from low-level to high-level features and effectively alleviate the gradient vanishing problem caused by the deep layers. The proposed method is evaluated on 17 7T MR images by a twofold cross-validation. The experiments show that our proposed network is much more effective to enhance the PVS than the previous PVS enhancement 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.

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.

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.

Enhancement of Perivascular Spaces in 7 T MR Image using Haar Transform of Non-local Cubes and Block-matching Filtering

Perivascular spaces (PVSs) in brain have a close relationship with typical neurological diseases. The quantitative studies of PVSs are meaningful but usually difficult, due to their thin and weak signals and also background noise in the 7 T brain magnetic resonance images (MRI). To clearly distinguish the PVSs in the 7 T MRI, we propose a novel PVS enhancement method based on the Haar transform of non-local cubes. Specifically, we extract a certain number of cubes from a small neighbor to form a cube group, and then perform Haar transform on each cube group. The Haar transform coefficients are processed using a nonlinear function to amplify the weak signals relevant to the PVSs and to suppress the noise. The enhanced image is reconstructed using the inverse Haar transform of the processed coefficients. Finally, we perform a block-matching 4D filtering on the enhanced image to further remove any remaining noise, and thus obtain an enhanced and denoised 7 T MRI for PVS segmentation. We apply two existing methods to complete PVS segmentation, i.e., (1) vesselness-thresholding and (2) random forest classification. The experimental results show that the PVS segmentation performances can be significantly improved by using the enhanced and denoised 7 T MRI.

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.

Endothelial Function, Inflammation, Thrombosis, and Basal Ganglia Perivascular Spaces in Patients with Stroke

Background and Objective

Recent studies suggest perivascular spaces are a marker of small vessel disease, blood–brain barrier permeability, and inflammation, but little is known about their risk factors and associations with peripheral blood markers.

Materials and Methods

In prospectively recruited patients with recent minor ischemic stroke, we investigated the influence of age, sex, hypertension, diabetes, and smoking on the severity of perivascular spaces in the basal ganglia seen on T2-weighted magnetic resonance imaging. We assessed plasma markers of endothelial function (von Willebrand factor, intracellular adhesion molecule-1), inflammation (interleukin-6, tumor necrosis factor-alpha, C-reactive protein), and thrombosis (fibrinogen, prothrombin fragments 1 + 2, thrombin–antithrombin complex, tissue plasminogen activator, D-dimer). We used a validated semi-automated method to measure basal ganglia perivascular spaces count and volume. We tested uni- and multivariable associations between blood markers and basal ganglia perivascular spaces count and volume.

Findings

In 100 patients (median age: 67 years, range: 37-92), on adjusted analysis, basal ganglia perivascular spaces count was associated with age (r = .117, P = .003) and hypertension (r = 2.225, P = .013). On multivariable linear regression, adjusted for age, sex, hypertension, smoking and diabetes, reduced von Willebrand factor was associated with increased basal ganglia perivascular spaces count (r = −.025, P = .032).

Conclusion

The association of increased basal ganglia perivascular spaces count with reduced von Willebrand factor is novel. As von Willebrand factor may promote cerebral endothelial integrity, insufficient von Willebrand factor is consistent with dysfunctional cerebral endothelium and increased basal ganglia perivascular spaces in cerebral small vessel disease. Quantitative perivascular spaces measurement may increase sensitivity to detect cerebral endothelial dysfunction.

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.

Topographic distribution of white matter changes and lacunar infarcts in neurodegenerative and vascular dementia syndromes: A post-mortem 7.0-tesla magnetic resonance imaging study

Background

White matter changes and lacunar infarcts are regarded as linked to the same underlying small-vessel pathology. On magnetic resonance imaging, white matter changes are frequently observed, while the number of lacunar infarcts is probably underestimated. The present study post-mortem 7.0-tesla magnetic resonance imaging study compares the severity and the distribution of white matter changes and lacunar infarcts in different neurodegenerative and vascular dementia syndromes in order to determine their impact on the disease evolution.

Patients and methods

Eighty-four post-mortem brains consisting of 15 patients with pure Alzheimer's disease and 12 with associated cerebral amyloid angiopathy, 14 patients with frontotemporal lobar degeneration, 7 with Lewy body dementia, 10 with progressive supranuclear palsy, 14 with vascular dementia and 12 control brains were examined. Six hemispheric coronal sections of each brain underwent 7.0-tesla magnetic resonance imaging. Location and severity of white matter changes and lacunar infarcts were evaluated semi-quantitatively in each section separately.

Results

White matter changes predominated in the prefrontal and frontal sections of frontotemporal lobar degeneration and in the post-central section of associated cerebral amyloid angiopathy brains, while overall increased in vascular dementia cases. Lacunar infarcts were more frequent in the vascular dementia brains and mainly increased in the centrum semiovale.

Conclusions

White matter changes have a different topographic distribution in neurodegenerative diseases and are most severe and extended in vascular dementia. Lacunar infarcts predominate in the deep white matter of vascular dementia compared to the neurodegenerative diseases. Vascular cognitive impairment is mainly linked to white matter changes due to chronic ischaemia as well as to lacunar infarcts due to small-vessel occlusion.

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.