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

Deep Cascade of Convolutional Neural Networks for Quantification of Enlarged Perivascular Spaces in the Basal Ganglia in Magnetic Resonance Imaging

In this paper, we present a cascaded deep convolution neural network (CNN) for assessing enlarged perivascular space (ePVS) within the basal ganglia region using T2-weighted MRI. Enlarged perivascular spaces (ePVSs) are potential biomarkers for various neurodegenerative disorders, including dementia and Parkinson's disease. Accurate assessment of ePVS is crucial for early diagnosis and monitoring disease progression. Our approach first utilizes an ePVS enhancement CNN to improve ePVS visibility and then employs a quantification CNN to predict the number of ePVSs. The ePVS enhancement CNN selectively enhances the ePVS areas without the need for additional heuristic parameters, achieving a higher contrast-to-noise ratio (CNR) of 113.77 compared to Tophat, Clahe, and Laplacian-based enhancement algorithms. The subsequent ePVS quantification CNN was trained and validated using fourfold cross-validation on a dataset of 76 participants. The quantification CNN attained 88% accuracy at the image level and 94% accuracy at the subject level. These results demonstrate significant improvements over traditional algorithm-based methods, highlighting the robustness and reliability of our deep learning approach. The proposed cascaded deep CNN model not only enhances the visibility of ePVS but also provides accurate quantification, making it a promising tool for evaluating neurodegenerative disorders. This method offers a novel and significant advancement in the non-invasive assessment of ePVS, potentially aiding in early diagnosis and targeted treatment strategies.

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.

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.

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.

Automatic Quantification of Enlarged Perivascular Space in Patients With Traumatic Brain Injury Using Super-Resolution of T2-Weighted Images

The perivascular space (PVS) is important to brain waste clearance and brain metabolic homeostasis. Enlarged PVS (ePVS) becomes visible on magnetic resonance imaging (MRI) and is best appreciated on T2-weighted (T2w) images. However, quantification of ePVS is challenging because standard-of-care T1-weighted (T1w) and T2w images are often obtained via two-dimensional (2D) acquisition, whereas accurate quantification of ePVS normally requires high-resolution volumetric three-dimensional (3D) T1w and T2w images. The purpose of this study was to investigate the use of a deep-learning-based super-resolution (SR) technique to improve ePVS quantification from 2D T2w images for application in patients with traumatic brain injury (TBI). We prospectively recruited 26 volunteers (age: 31 ± 12 years, 12 male/14 female) where both 2D T2w and 3D T2w images were acquired along with 3D T1w images to validate the ePVS quantification using SR T2w images. We then applied the SR method to retrospectively acquired 2D T2w images in 41 patients with chronic TBI (age: 41 ± 16 years, 32 male/9 female). ePVS volumes were automatically quantified within the whole-brain white matter and major brain lobes (temporal, parietal, frontal, occipital) in all subjects. Pittsburgh Sleep Quality Index (PSQI) scores were obtained on all patients with TBI. Compared with the silver standard (3D T2w), in the validation study, the SR T2w provided similar whole-brain white matter ePVS volume (r = 0.98, p < 0.0001), and similar age-related ePVS burden increase (r = 0.80, p < 0.0001). In the patient study, patients with TBI with poor sleep showed a higher age-related ePVS burden increase than those with good sleep. Sleep status is a significant interaction factor in the whole brain (p = 0.047) and the frontal lobe (p = 0.027). We demonstrate that images produced by SR of 2D T2w images can be automatically analyzed to produce results comparable to those obtained by 3D T2 volumes. Reliable age-related ePVS burden across the whole-brain white matter was observed in all subjects. Poor sleep, affecting the glymphatic function, may contribute to the accelerated increase of ePVS burden following TBI.

Assessment of Perivascular Space Morphometry Across the White Matter in Huntington's Disease Using MRI

Background

Perivascular spaces (PVS) are fluid-filled cavities surrounding small cerebral blood vessels. There are limited reports of enlarged PVS across the grey matter in manifest Huntington's disease (HD). Little is known about how PVS morphometry in the white matter may contribute to HD. Enlarged PVS have the potential to both contribute to HD pathology and affect the distribution and success of intraparenchymal and intrathecally administered huntingtin-lowering therapies.

Objective

To investigate PVS morphometry in the global white matter across the spectrum of HD. Relationships between PVS morphometry and disease burden and severity measures were examined.

Methods

White matter PVS were segmented on 3T T2 W MRI brain scans of 33 healthy controls, 30 premanifest HD (pre-HD), and 32 early manifest HD (early-HD) participants from the Vancouver site of the TRACK-HD study. PVS count and total PVS volume were measured.

Results

PVS total count slightly increased in pre-HD (p = 0.004), and early-HD groups (p = 0.005), compared to healthy controls. PVS volume, as a percentage of white matter volume, increased subtly in pre-HD compared to healthy controls (p = 0.044), but not in early-HD. No associations between PVS measures and HD disease burden or severity were found.

Conclusions

This study reveals relatively preserved PVS morphometry across the global white matter of pre-HD and early-HD. Subtle morphometric abnormalities are implied but require confirmation in a larger cohort. However, in conjunction with previous publications, further investigation of PVS in HD and its potential impact on future treatments, with a focus on subcortical grey matter, is warranted.

Enlarged Perivascular Space and Index for Diffusivity Along the Perivascular Space as Emerging Neuroimaging Biomarkers of Neurological Diseases

The existence of lymphatic vessels or similar clearance systems in the central nervous system (CNS) that transport nutrients and remove cellular waste is a neuroscientific question of great significance. As the brain is the most metabolically active organ in the body, there is likely to be a potential correlation between its clearance system and the pathological state of the CNS. Until recently the successive discoveries of the glymphatic system and the meningeal lymphatics solved this puzzle. This article reviews the basic anatomy and physiology of the glymphatic system. Imaging techniques to visualize the function of the glymphatic system mainly including post-contrast imaging techniques, indirect lymphatic assessment by detecting increased perivascular space, and diffusion tensor image analysis along the perivascular space (DTI-ALPS) are discussed. The pathological link between glymphatic system dysfunction and neurological disorders is the key point, focusing on the enlarged perivascular space (EPVS) and the index of diffusivity along the perivascular space (ALPS index), which may represent the activity of the glymphatic system as possible clinical neuroimaging biomarkers of neurological disorders. The pathological link between glymphatic system dysfunction and neurological disorders is the key point, focusing on the enlarged perivascular space (EPVS) and the index for of diffusivity along the perivascular space (ALPS index), which may represent the activity of the glymphatic system as possible clinical neuroimaging biomarkers of neurological disorders.

Breast Fibroglandular Tissue Segmentation for Automated BPE Quantification With Iterative Cycle-Consistent Semi-Supervised Learning

Background Parenchymal Enhancement (BPE) quantification in Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) plays a pivotal role in clinical breast cancer diagnosis and prognosis. However, the emerging deep learning-based breast fibroglandular tissue segmentation, a crucial step in automated BPE quantification, often suffers from limited training samples with accurate annotations. To address this challenge, we propose a novel iterative cycle-consistent semi-supervised framework to leverage segmentation performance by using a large amount of paired pre-/post-contrast images without annotations. Specifically, we design the reconstruction network, cascaded with the segmentation network, to learn a mapping from the pre-contrast images and segmentation predictions to the post-contrast images. Thus, we can implicitly use the reconstruction task to explore the inter-relationship between these two-phase images, which in return guides the segmentation task. Moreover, the reconstructed post-contrast images across multiple auto-context modeling-based iterations can be viewed as new augmentations, facilitating cycle-consistent constraints across each segmentation output. Extensive experiments on two datasets with various data distributions show great segmentation and BPE quantification accuracy compared with other state-of-the-art semi-supervised methods. Importantly, our method achieves 11.80 times of quantification accuracy improvement along with 10 times faster, compared with clinical physicians, demonstrating its potential for automated BPE quantification. The code is available at https://github.com/ZhangJD-ong/Iterative-Cycle-consistent-Semi-supervised-Learning-for-fibroglandular-tissue-segmentation.

Deep Medullary Vein and MRI Markers Were Related to Cerebral Hemorrhage Subtypes

BACKGROUND

To explore the performance of deep medullary vein (DMV) and magnetic resonance imaging (MRI) markers in different intracerebral hemorrhage (ICH) subtypes in patients with cerebral small vessel disease (CSVD).

METHODS

In total, 232 cases of CSVD with ICH were included in this study. The clinical and image data were retrospectively analyzed. Patients were divided into hypertensive arteriopathy (HTNA)-related ICH, cerebral amyloid angiopathy (CAA)-related ICH, and mixed ICH groups. The DMV score was determined in the cerebral hemisphere contralateral to the ICH.

RESULTS

The DMV score was different between the HTNA-related and mixed ICH groups (p < 0.01). The MRI markers and CSVD burden score were significant among the ICH groups (p < 0.05). Compared to mixed ICH, HTNA-related ICH diagnosis was associated with higher deep white matter hyperintensity (DWMH) (OR: 0.452, 95% CI: 0.253-0.809, p < 0.05) and high-degree perivascular space (PVS) (OR: 0.633, 95% CI: 0.416-0.963, p < 0.05), and CAA-related ICH diagnosis was associated with increased age (OR: 1.074; 95% CI: 1.028-1.122, p = 0.001). The DMV score correlated with cerebral microbleed (CMB), PVS, DWMH, periventricular white matter hyperintensity (PWMH), and CSVD burden score (p < 0.05) but not with lacuna (p > 0.05). Age was an independent risk factor for the severity of DMV score (OR: 1.052; 95% CI: 0.026-0.076, p < 0.001).

CONCLUSION

DMV scores, CSVD markers, and CSVD burden scores were associated with different subtypes of ICH. In addition, DMV scores were associated with the severity of CSVD and CSVD markers.

Perivascular network segmentations derived from high-field MRI and their implications for perivascular and parenchymal mass transport in the rat brain

A custom segmentation workflow was applied to ex vivo high-field MR images of rat brains acquired following in vivo intraventricular contrast agent infusion to generate maps of the perivascular spaces (PVS). The resulting perivascular network segmentations enabled analysis of perivascular connections to the ventricles, parenchymal solute clearance, and dispersive solute transport within PVS. Numerous perivascular connections between the brain surface and the ventricles suggest the ventricles integrate into a PVS-mediated clearance system and raise the possibility of cerebrospinal fluid (CSF) return from the subarachnoid space to the ventricles via PVS. Assuming rapid solute exchange between the PVS and CSF spaces primarily by advection, the extensive perivascular network decreased the mean clearance distance from parenchyma to the nearest CSF compartment resulting in an over 21-fold reduction in the estimated diffusive clearance time scale, irrespective of solute diffusivity. This corresponds to an estimated diffusive clearance time scale under 10 min for amyloid-beta which suggests that the widespread distribution of PVS may render diffusion an effective parenchymal clearance mechanism. Additional analysis of oscillatory solute dispersion within PVS indicates that advection rather than dispersion is likely the primary transport mechanism for dissolved compounds greater than 66 kDa in the long (> 2 mm) perivascular segments identified here, although dispersion may be significant for smaller compounds in shorter perivascular segments.

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.

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.

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.

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.

Semi-automated Segmentation and Quantification of Perivascular Spaces at 7 Tesla in COVID-19

While COVID-19 is primarily considered a respiratory disease, it has been shown to affect the central nervous system. Mounting evidence shows that COVID-19 is associated with neurological complications as well as effects thought to be related to neuroinflammatory processes. Due to the novelty of COVID-19, there is a need to better understand the possible long-term effects it may have on patients, particularly linkage to neuroinflammatory processes. Perivascular spaces (PVS) are small fluid-filled spaces in the brain that appear on MRI scans near blood vessels and are believed to play a role in modulation of the immune response, leukocyte trafficking, and glymphatic drainage. Some studies have suggested that increased number or presence of PVS could be considered a marker of increased blood-brain barrier permeability or dysfunction and may be involved in or precede cascades leading to neuroinflammatory processes. Due to their size, PVS are better detected on MRI at ultrahigh magnetic field strengths such as 7 Tesla, with improved sensitivity and resolution to quantify both concentration and size. As such, the objective of this prospective study was to leverage a semi-automated detection tool to identify and quantify differences in perivascular spaces between a group of 10 COVID-19 patients and a similar subset of controls to determine whether PVS might be biomarkers of COVID-19-mediated neuroinflammation. Results demonstrate a detectable difference in neuroinflammatory measures in the patient group compared to controls. PVS count and white matter volume were significantly different in the patient group compared to controls, yet there was no significant association between PVS count and symptom measures. Our findings suggest that the PVS count may be a viable marker for neuroinflammation in COVID-19, and other diseases which may be linked to neuroinflammatory processes.

Automated grading of enlarged perivascular spaces in clinical imaging data of an acute stroke cohort using an interpretable, 3D deep learning framework

Enlarged perivascular spaces (EPVS), specifically in stroke patients, has been shown to strongly correlate with other measures of small vessel disease and cognitive impairment at 1 year follow-up. Typical grading of EPVS is often challenging and time consuming and is usually based on a subjective visual rating scale. The purpose of the current study was to develop an interpretable, 3D neural network for grading enlarged perivascular spaces (EPVS) severity at the level of the basal ganglia using clinical-grade imaging in a heterogenous acute stroke cohort, in the context of total cerebral small vessel disease (CSVD) burden. T2-weighted images from a retrospective cohort of 262 acute stroke patients, collected in 2015 from 5 regional medical centers, were used for analyses. Patients were given a label of 0 for none-to-mild EPVS (< 10) and 1 for moderate-to-severe EPVS (≥ 10). A three-dimensional residual network of 152 layers (3D-ResNet-152) was created to predict EPVS severity and 3D gradient class activation mapping (3DGradCAM) was used for visual interpretation of results. Our model achieved an accuracy 0.897 and area-under-the-curve of 0.879 on a hold-out test set of 15% of the total cohort (n = 39). 3DGradCAM showed areas of focus that were in physiologically valid locations, including other prevalent areas for EPVS. These maps also suggested that distribution of class activation values is indicative of the confidence in the model's decision. Potential clinical implications of our results include: (1) support for feasibility of automated of EPVS scoring using clinical-grade neuroimaging data, potentially alleviating rater subjectivity and improving confidence of visual rating scales, and (2) demonstration that explainable models are critical for clinical translation.

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.

Direct Rating Estimation of Enlarged Perivascular Spaces (EPVS) in Brain MRI Using Deep Neural Network

In this article, we propose a deep-learning-based estimation model for rating enlarged perivascular spaces (EPVS) in the brain’s basal ganglia region using T2-weighted magnetic resonance imaging (MRI) images. The proposed method estimates the EPVS rating directly from the T2-weighted MRI without using either the detection or the segmentation of EVPS. The model uses the cropped basal ganglia region on the T2-weighted MRI. We formulated the rating of EPVS as a multi-class classification problem. Model performance was evaluated using 96 subjects’ T2-weighted MRI data that were collected from two hospitals. The results show that the proposed method can automatically rate EPVS—demonstrating great potential to be used as a risk indicator of dementia to aid early diagnosis.

Functional connectivity changes in cerebral small vessel disease - a systematic review of the resting-state MRI literature

BACKGROUND

Cerebral small vessel disease (CSVD) is a common neurological disease present in the ageing population that is associated with an increased risk of dementia and stroke. Damage to white matter tracts compromises the substrate for interneuronal connectivity. Analysing resting-state functional magnetic resonance imaging (fMRI) can reveal dysfunctional patterns of brain connectivity and contribute to explaining the pathophysiology of clinical phenotypes in CSVD.

MATERIALS AND METHODS

This systematic review provides an overview of methods and results of recent resting-state functional MRI studies in patients with CSVD. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol, a systematic search of the literature was performed.

RESULTS

Of 493 studies that were screened, 44 reports were identified that investigated resting-state fMRI connectivity in the context of cerebral small vessel disease. The risk of bias and heterogeneity of results were moderate to high. Patterns associated with CSVD included disturbed connectivity within and between intrinsic brain networks, in particular the default mode, dorsal attention, frontoparietal control, and salience networks; decoupling of neuronal activity along an anterior-posterior axis; and increases in functional connectivity in the early stage of the disease.

CONCLUSION

The recent literature provides further evidence for a functional disconnection model of cognitive impairment in CSVD. We suggest that the salience network might play a hitherto underappreciated role in this model. Low quality of evidence and the lack of preregistered multi-centre studies remain challenges to be overcome in the future.

Magnetic resonance imaging manifestations of cerebral small vessel disease: automated quantification and clinical application

The common cerebral small vessel disease (CSVD) neuroimaging features visible on conventional structural magnetic resonance imaging include recent small subcortical infarcts, lacunes, white matter hyperintensities, perivascular spaces, microbleeds, and brain atrophy. The CSVD neuroimaging features have shared and distinct clinical consequences, and the automatic quantification methods for these features are increasingly used in research and clinical settings. This review article explores the recent progress in CSVD neuroimaging feature quantification and provides an overview of the clinical consequences of these CSVD features as well as the possibilities of using these features as endpoints in clinical trials. The added value of CSVD neuroimaging quantification is also discussed for researches focused on the mechanism of CSVD and the prognosis in subjects with CSVD.

Autoidentification of perivascular spaces in white matter using clinical field strength T1 and FLAIR MR imaging

Recent interest in enlarged perivascular spaces (ePVS) in the brain, which can be visualized on MRI and appear isointense to cerebrospinal fluid on all sequence weightings, has resulted in the necessity of reliable algorithms for automated segmentation to allow for whole brain assessment of ePVS burden. However, several publicly available datasets do not contain sequences required for recently published algorithms. This prospective study presents a method for identification of enlarged perivascular spaces (ePVS) in white matter using 3T T1 and FLAIR MR imaging (MAPS-T1), making the algorithm accessible to groups with valuable sets of limited data. The approach was applied identically to two datasets: 1) a repeated measurement in a dementia-free aged human population (N = 14), and 2) an aged sample of multisite ADNI datasets (N = 30). ePVS segmentation was accomplished by a stepwise local homogeneity search of white matter-masked T1-weighted data, constrained by FLAIR hyperintensity, and further constrained by width, volume, and linearity measurements. Pearson's r was employed for statistical testing between visual (gold standard) assessment and repeated measures in cohort one. Visual ePVS counts were significantly correlated with MAPS-T1 (r = .72, P < .0001). Correlations between repeated measurements in cohort one were significant for both visual and automated methods in the single visually-rated slice (MAPS-T1: r = .87, P < .0001, visual: (r = .86, P < .0001) and for whole brain assessment (MAPS-T1: r = .77, P = .001). Results from each cohort were manually inspected and found to have positive predictive values of 77.5% and 87.5%, respectively. The approach described in this report is an important tool for detailed assessment of ePVS burden in white matter on routinely acquired MRI sequences.

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.

Clinical vascular imaging in the brain at 7T

Stroke and related cerebrovascular diseases are a major cause of mortality and disability. Even at standard-field-strengths (1.5T), MRI is by far the most sensitive imaging technique to detect acute brain infarctions and to characterize incidental cerebrovascular lesions, such as white matter hyperintensities, lacunes and microbleeds. Arterial time-of-flight (TOF) MR angiography (MRA) can depict luminal narrowing or occlusion of the major brain feeding arteries, and this without the need for contrast administration. Compared to 1.5T MRA, the use of high-field strength (3T) and even more so ultra-high-field strengths (7T), enables the visualization of the lumen of much smaller intracranial vessels, while adding a contrast agent to TOF MRA at 7T may enable the visualization of even more distal arteries in addition to veins and venules. Moreover, with 3T and 7T, the arterial vessel walls beyond the circle of Willis become visible with high-resolution vessel wall imaging. In addition, with 7T MRI, the brain parenchyma can now be visualized on a submillimeter scale. As a result, high-resolution imaging studies of the brain and its blood supply at 7T have generated new concepts of different cerebrovascular diseases. In the current article, we will discuss emerging clinical applications and future directions of vascular imaging in the brain at 7T MRI.

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.

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.