Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration: A united approach

Item does not contain fulltextCerebral small vessel disease (SVD) is a common accompaniment of ageing. Features seen on neuroimaging include recent small subcortical infarcts, lacunes, white matter hyperintensities, perivascular spaces, microbleeds, and brain atrophy. SVD can present as a stroke or cognitive decline, or can have few or no symptoms. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive deficits, physical disabilities, and other symptoms of neurodegeneration. Terminology and definitions for imaging the features of SVD vary widely, which is also true for protocols for image acquisition and image analysis. This lack of consistency hampers progress in identifying the contribution of SVD to the pathophysiology and clinical features of common neurodegenerative diseases. We are an international working group from the Centres of Excellence in Neurodegeneration. We completed a structured process to develop definitions and imaging standards for markers and consequences of SVD. We aimed to achieve the following: first, to provide a common advisory about terms and definitions for features visible on MRI; second, to suggest minimum standards for image acquisition and analysis; third, to agree on standards for scientific reporting of changes related to SVD on neuroimaging; and fourth, to review emerging imaging methods for detection and quantification of preclinical manifestations of SVD. Our findings and recommendations apply to research studies, and can be used in the clinical setting to standardise image interpretation, acquisition, and reporting. This Position Paper summarises the main outcomes of this international effort to provide the STandards for ReportIng Vascular changes on nEuroimaging (STRIVE)

Segmentation of perivascular spaces in 7 T 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 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

An anomaly detection approach to identify chronic brain infarcts on MRI

The performance of current machine learning methods to detect heterogeneous pathology is limited by the quantity and quality of pathology in medical images. A possible solution is anomaly detection; an approach that can detect all abnormalities by learning how 'normal' tissue looks like. In this work, we propose an anomaly detection method using a neural network architecture for the detection of chronic brain infarcts on brain MR images. The neural network was trained to learn the visual appearance of normal appearing brains of 697 patients. We evaluated its performance on the detection of chronic brain infarcts in 225 patients, which were previously labeled. Our proposed method detected 374 chronic brain infarcts (68% of the total amount of brain infarcts) which represented 97.5% of the total infarct volume. Additionally, 26 new brain infarcts were identified that were originally missed by the radiologist during radiological reading. Our proposed method also detected white matter hyperintensities, anomalous calcifications, and imaging artefacts. This work shows that anomaly detection is a powerful approach for the detection of multiple brain abnormalities, and can potentially be used to improve the radiological workflow efficiency by guiding radiologists to brain anomalies which otherwise remain unnoticed

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

Cerebrovascular dysfunction in cerebral small vessel disease

INTRODUCTION: Cerebral small vessel disease (SVD) is the cause of a quarter of all ischaemic strokes and is postulated to have a role in up to half of all dementias. SVD pathophysiology remains unclear but cerebrovascular dysfunction may be important. If confirmed many licensed medications have mechanisms of action targeting vascular function, potentially enabling new treatments via drug repurposing. Knowledge is limited however, as most studies assessing cerebrovascular dysfunction are small, single centre, single imaging modality studies due to the complexities in measuring cerebrovascular dysfunctions in humans. This thesis describes the development and application of imaging techniques measuring several cerebrovascular dysfunctions to investigate SVD pathophysiology and trial medications that may improve small blood vessel function in SVD. METHODS: Participants with minor ischaemic strokes were recruited to a series of studies utilising advanced MRI techniques to measure cerebrovascular dysfunction. Specifically MRI scans measured the ability of different tissues in the brain to change blood flow in response to breathing carbon dioxide (cerebrovascular reactivity; CVR) and the flow and pulsatility through the cerebral arteries, venous sinuses and CSF spaces. A single centre observational study optimised and established feasibility of the techniques and tested associations of cerebrovascular dysfunctions with clinical and imaging phenotypes. Then a randomised pilot clinical trial tested two medications’ (cilostazol and isosorbide mononitrate) ability to improve CVR and pulsatility over a period of eight weeks. The techniques were then expanded to include imaging of blood brain barrier permeability and utilised in multi-centre studies investigating cerebrovascular dysfunction in both sporadic and monogenetic SVDs. RESULTS: Imaging protocols were feasible, consistently being completed with usable data in over 85% of participants. After correcting for the effects of age, sex and systolic blood pressure, lower CVR was associated with higher white matter hyperintensity volume, Fazekas score and perivascular space counts. Lower CVR was associated with higher pulsatility of blood flow in the superior sagittal sinus and lower CSF flow stroke volume at the foramen magnum. Cilostazol and isosorbide mononitrate increased CVR in white matter. The CVR, intra-cranial flow and pulsatility techniques, alongside blood brain barrier permeability and microstructural integrity imaging were successfully employed in a multi-centre observational study. A clinical trial assessing the effects of drugs targeting blood pressure variability is nearing completion. DISCUSSION: Cerebrovascular dysfunction in SVD has been confirmed and may play a more direct role in disease pathogenesis than previously established risk factors. Advanced imaging measures assessing cerebrovascular dysfunction are feasible in multi-centre studies and trials. Identifying drugs that improve cerebrovascular dysfunction using these techniques may be useful in selecting candidates for definitive clinical trials which require large sample sizes and long follow up periods to show improvement against outcomes of stroke and dementia incidence and cognitive function

Neuroimaging of cerebral small vessel disease

Lacunar stroke accounts for one quarter of all ischaemic stroke and in the long term carries a greater risk of death and disability than was previously realised. Much of our current knowledge originated from neuropathological studies in the 1950s and 1960s. In the last thirty years, brain computed tomography (CT) and magnetic resonance imaging (MRI) have revolutionised our understanding of lacunar stroke and associated features of cerebral small vessel disease (SVD), namely white matter lesions (WML), enlarged perivascular spaces (EPVS) and brain microbleeds (BMB). The purpose of the projects which led to the writing of this thesis was to improve understanding of imaging characteristics of cerebral SVD. We aimed to assess (i) clinical and imaging features which might explain misclassification of lacunar infarcts as cortical infarcts and vice versa, (ii) the proportion of symptomatic lacunar infarcts progressing to lacunar cavities and associations of cavitation, (iii) completeness of reporting of lacunar lesions in the lacunar stroke literature, (iv) definitions and detection of lacunar lesions amongst SVD researchers, (v) the relationship between WML and carotid stenosis, (vi) clinical and imaging associations of EPVS and, (vii) observer variability in the assessment of EPVS and BMB, in order to develop visual rating scales. Section one describes neuroimaging of lacunar stroke. To investigate features which might explain clinical stroke subtype misclassification (‘clinical-imaging dissociation’), I used data from a stroke study. The main factor associated with clinical-imaging dissociation was diabetes, and in patients with acute lacunar infarction, proximity of the lacunar infarct to the cortex, age, diabetes and left hemisphere location. To investigate the proportion of symptomatic lacunar infarcts progressing to cavities, I used data from two stroke studies. A fifth of patients with acute lacunar ischaemic stroke showed definite cavitation on follow-up imaging at a median of 227 days; cavitation was associated with increasing time to follow-up. To assess completeness of reporting of lacunar lesions in the lacunar stroke literature, I reviewed 50 articles from three journals with a stroke focus. There was marked variation in terminology and descriptions of imaging definitions of lacunar lesions. To assess lacunar lesion definitions and detection amongst SVD researchers, I used an online survey consisting of case-based and non-case-based questions. There was marked variation in definitions and descriptions. Cavitated lesions were detected with the highest degree of confidence. Section two describes neuroimaging of associated features of cerebral SVD. Using data from two stroke studies, I examined the relationship between WML and ipsilateral carotid artery stenosis. There was no association between carotid stenosis and WML. I tested the association of EPVS with WML and lacunar stroke subtype using data from a stroke study. Total EPVS were associated with age and deep WML; basal ganglia (BG) EPVS were associated with age, centrum semiovale (CS) EPVS, cerebral atrophy and lacunar stroke subtype. Quantification of observer variability in EPVS rating was assessed on 60 MRI scans selected from a stroke study and an ageing cohort. Intrarater agreement was good and interrater agreement was moderate. Main reasons for interrater disagreement included the visualisation of very small EPVS and the presence of concomitant WML and lacunar lesions. Observer variability in BMB rating was quantified using MRI scans from a stroke study. Interrater agreement was moderate but improved following modification of the pilot rating scale (BOMBS; Brain Observer MicroBleed Scale), which had its main effect by differentiating ‘certain’ BMB from ‘uncertain’ BMB and BMB ‘mimics’. In conclusion, neuroimaging, particularly MRI, is a valuable tool for the investigation of lacunar stroke and associated features of cerebral SVD. With recent technological advances in both CT and MRI, neuroimaging will remain central to future SVD studies, hopefully leading to a much improved understanding of this important disease

Brain atrophy in cerebral small vessel diseases: Extent, consequences, technical limitations and perspectives: The HARNESS initiative

Brain atrophy is increasingly evaluated in cerebral small vessel diseases. We aim at systematically reviewing the available data regarding its extent, correlates and cognitive consequences. Given that in this context, brain atrophy measures might be biased, the first part of the review focuses on technical aspects. Thereafter, data from the literature are analyzed in light of these potential limitations, to better understand the relationships between brain atrophy and other MRI markers of cerebral small vessel diseases. In the last part, we review the links between brain atrophy and cognitive alterations in patients with cerebral small vessel diseases

Magnetic resonance imaging in CADASIL and small vessel disease

Introduction: CADASIL is the most common monogenic cause of small vessel disease affecting the brain. CADASIL results in subcortical infarcts, dementia, and disability. Magnetic resonance imaging reveals often extensive white matter hyperintensities, multiple lacunes, cerebral microbleeds and enlarged perivascular spaces. These generally occur at a younger age and in the absence of conventional vascular risk factors when compared to sporadic small vessel disease. The location of the cysteine altering NOTCH3 variant on the EGFr domain is recognised as significant in determining disease progression and extent of imaging abnormalities. Other factors, that are not completely delineated, influence the rate of disease progression, and must account for the significant variation and unpredictable nature of this within CADASIL. Methods: We used conventional MRI and ultra-high field intensity MRI to further investigate the relationships between clinical and imaging features in CADASIL and make comparisons with sporadic cerebral small vessel disease. We updated the population data for CADASIL in Scotland to estimate the current disease prevalence. We used 7-Tesla MRI in people who have recently had a lacunar infarct to detect the susceptibility vessel sign on susceptibility-weighted imaging and correlated this with time-of-flight magnetic resonance imaging to confirm small vessel thrombotic occlusion. We quantified differences between 1.5, 3 and 7-Tesla MRI in this setting for detection of lacunes, cerebral microbleeds and enlarged perivascular spaces. We used 7-Tesla MRI in a human volunteer study to establish measurement of flow velocity in the lenticulostriate branches of the middle cerebral arteries as a surrogate measure of blood flow to the basal ganglia region. Main results: We estimated a disease prevalence of 2.76 per 100,000 based on clinical attendance at a national specialist clinic. The majority of people had a proximal genetic mutation with 59.7% mutations found at exon 4. The distribution of white matter hyperintensities, lacunes and cerebral microbleeds differed between CADASIL and sporadic small vessel disease. For white matter hyperintensities the anterior temporal pole and parietal lobe were significantly more commonly affected in CADASIL. Lacunes were more likely to be present in the temporal and frontal lobes, and cerebral microbleeds were more likely to be found in the thalamus and brainstem in CADASIL and there was a suggestion of more microbleeds in the internal capsule. MRI in CADASIL displays significantly larger volumes of white matter hyperintensities, and larger numbers of lacunes and cerebral microbleeds than in sporadic small vessel disease even when compared to an older cohort with more extensive co-morbidities. Our cohort continues the previous genetic pattern of proximal genetic mutations (EGFR 1 to 6) displaying more extensive white matter hyperintensities, lacunes and cerebral microbleeds and suffering from earlier onset of stroke, as compared to those with distal mutations (EGFR 7 to 34) who are more likely hypertensive. 7-Tesla MRI provides detailed images of cerebral microbleeds and may identify susceptibility vessel sign on relevant sequences. We have not been able to corroborate this by visualising an associated occluded vessel on time-of flight magnetic resonance angiography in any of the cases. We used these images to discover that 7-Tesla allows visualisation of higher numbers of enlarged perivascular spaces compared to 1.5 and 3-Tesla. We were able to use phase-contrast imaging at 7-Tesla to assess the flow velocity of lenticulostriate vessels which may serve as a correlate to cerebral blood flow measured using alternative methods. Conclusions: CADASIL is a rare small vessel disease with distinct imaging features. The extensive imaging abnormalities do not consistently correlate with extent of disability and symptom burden. Although there is increasing recognition of the genetic and clinical factors that influence outcome in CADASIL, there is a need to better understand the reasons behind and potential protective factors that cause such variable outcome. Further investigation of small vessel dysfunction and blood flow using ultra-high field strength will hopefully allow us to better understand these mechanisms, appreciate differences between genetic and sporadic small vessel disease and provide utility in investigating therapeutic interventions