ANTONIA perfusion and stroke. A software tool for the multi-purpose analysis of MR perfusion-weighted datasets and quantitative ischemic stroke assessment

Annotation-free prediction of treatment-specific tissue outcome from 4D CT perfusion imaging in acute ischemic stroke

Acute ischemic stroke is a critical health condition that requires timely intervention. Following admission, clinicians typically use perfusion imaging to facilitate treatment decision-making. While deep learning models leveraging perfusion data have demonstrated the ability to predict post-treatment tissue infarction for individual patients, predictions are often represented as binary or probabilistic masks that are not straightforward to interpret or easy to obtain. Moreover, these models typically rely on large amounts of subjectively segmented data and non-standard perfusion analysis techniques. To address these challenges, we propose a novel deep learning approach that directly predicts follow-up computed tomography images from full spatio-temporal 4D perfusion scans through a temporal compression. The results show that this method leads to realistic follow-up image predictions containing the infarcted tissue outcomes. The proposed compression method achieves comparable prediction results to using perfusion maps as inputs but without the need for perfusion analysis or arterial input function selection. Additionally, separate models trained on 45 patients treated with thrombolysis and 102 treated with thrombectomy showed that each model correctly captured the different patient-specific treatment effects as shown by image difference maps. The findings of this work clearly highlight the potential of our method to provide interpretable stroke treatment decision support without requiring manual annotations.

Children with Congenital Heart Diseases Exhibit Altered Deep Gray Matter Structures

BACKGROUND AND OBJECTIVES

Children with congenital heart diseases (CHDs) have an increased risk of developing neurologic deficits, even in the absence of apparent brain pathology. The aim of this work was to compare quantitative macro- and microstructural properties of subcortical gray matter structures of pediatric CHD patients with normal appearing brain magnetic resonance imaging to healthy controls.

METHODS

We retrospectively reviewed children with coarctation of the aorta (COA) and hypoplastic left heart syndrome (HLHS) admitted to our hospital. We identified 24 pediatric CHD patients (17 COA, 7 HLHS) with normal-appearing brain MRI. Using an atlas-based approach, the volume and apparent diffusion coefficient (ADC) were determined for the thalamus, caudate, putamen, pallidum, hippocampus, amygdala, nucleus accumbens, cerebral white matter, cerebral cortex, and brainstem. Multivariate statistics were used to compare the extracted values to reference values from 100 typically developing children without any known cardiac or neurological diseases.

RESULTS

Multivariate analysis of covariance using the regional ADC and volume values as dependent variables and age and sex as co-variates revealed a significant difference between pediatric CHD patients and healthy controls (p < 0.001). Post-hoc comparisons demonstrated significantly reduced brain volumes in most subcortical brain regions investigated and elevated ADC values in the thalamus for children with CHD. No significant differences were found comparing children with COA and HLHS.

CONCLUSIONS

Despite normal appearing brain MRI, children with CHD exhibit wide-spread macro-structural and regional micro-structural differences of subcortical brain structures compared to healthy controls, which could negatively impact neurodevelopment, leading to neurological deficits in childhood and beyond.

Providing clinical context to the spatio-temporal analysis of 4D CT perfusion to predict acute ischemic stroke lesion outcomes

Acute ischemic stroke is a leading cause of mortality and morbidity worldwide. Timely identification of the extent of a stroke is crucial for effective treatment, whereas spatio-temporal (4D) Computed Tomography Perfusion (CTP) imaging is playing a critical role in this process. Recently, the first deep learning-based methods that leverage the full spatio-temporal nature of perfusion imaging for predicting stroke lesion outcomes have been proposed. However, clinical information is typically not integrated into the learning process, which may be helpful to improve the tissue outcome prediction given the known influence of various factors (i.e., physiological, demographic, and treatment factors) on lesion growth. Cross-attention, a multimodal fusion strategy, has been successfully used to combine information from multiple sources, but it has yet to be applied to stroke lesion outcome prediction. Therefore, this work aimed to develop and evaluate a novel multimodal and spatio-temporal deep learning model that utilizes cross-attention to combine information from 4D CTP and clinical metadata simultaneously to predict stroke lesion outcomes. The proposed model was evaluated using a dataset of 70 acute ischemic stroke patients, demonstrating significantly improved volume estimates (mean error = 19 ml) compared to a baseline unimodal approach (mean error = 35 ml, p< 0.05). The proposed model allows generating attention maps and counterfactual outcome scenarios to investigate the relevance of clinical variables in predicting stroke lesion outcomes at a patient level, helping to provide a better understanding of the model's decision-making process.

Functional Lesion Network Mapping of Sensory Deficits After Ischemic Stroke

BACKGROUND

Sensory deficits are common after stroke, leading to disability and poor quality of life. Although lesion locations and patterns of structural brain network disruption have been associated with sensory disturbances, the relation with functional lesion connectivity has not yet been established.

METHODS

Retrospective analysis of a prospective cohort study of patients with acute ischemic stroke. Indirect functional lesion network mapping to identify brain regions remote from the primary lesion associated with deficits on the Rivermead Assessment of Somatosensory Performance test. Associations between Rivermead Assessment of Somatosensory Performance scores and functional connectivity of the lesion site with prespecified components of the somatosensory system.

RESULTS

One hundred one patients (mean age, 62 years; 32% women) from the TOPOS study (Topological and Clinical Prospective Study About Somatosensation in Stroke). Lesion network mapping identified a bilateral fronto-parietal network associated with sensory deficits in the acute phase after stroke. There were graded associations between deficits and functional lesion connectivity to sensory cortices, but not the thalamus.

CONCLUSIONS

Infarcts in brain regions remote from, but functionally connected, to the somatosensory network are associated with somatosensory deficits measured by the Rivermead Assessment of Somatosensory Performance test, reflecting the hierarchical functional anatomy of sensory processing. Further research is needed to translate these findings into improved prognosis and personalized rehabilitation strategies.

Predicting the tissue outcome of acute ischemic stroke from acute 4D computed tomography perfusion imaging using temporal features and deep learning

Predicting follow-up lesions from baseline CT perfusion (CTP) datasets in acute ischemic stroke patients is important for clinical decision making. Deep convolutional networks (DCNs) are assumed to be the current state-of-the-art for this task. However, many DCN classifiers have not been validated against the methods currently used in research (random decision forests, RDF) and clinical routine (Tmax thresholding). Specialized DCNs have even been designed to extract complex temporal features directly from spatiotemporal CTP data instead of using standard perfusion parameter maps. However, the benefits of applying deep learning to source or deconvolved CTP data compared to perfusion parameter maps have not been formally investigated so far. In this work, a modular UNet-based DCN is proposed that separates temporal feature extraction from tissue outcome prediction, allowing for both model validation using perfusion parameter maps as well as end-to-end learning from spatiotemporal CTP data. 145 retrospective datasets comprising baseline CTP imaging, perfusion parameter maps, and follow-up non-contrast CT with manual lesion segmentations were assembled from acute ischemic stroke patients treated with intravenous thrombolysis alone (IV; n = 43) or intra-arterial mechanical thrombectomy (IA; n = 102) with or without combined IV. Using the perfusion parameter maps as input, the proposed DCN (mean Dice: 0.287) outperformed the RDF (0.262) and simple Tmax-thresholding (0.249). The performance of the proposed DCN was approximately equal using features optimized from the deconvolved residual curves (0.286) compared to perfusion parameter maps (0.287), while using features optimized from the source concentration-time curves (0.296) provided the best tissue outcome predictions.

Predicting treatment-specific lesion outcomes in acute ischemic stroke from 4D CT perfusion imaging using spatio-temporal convolutional neural networks

For the diagnosis and precise treatment of acute ischemic stroke, predicting the final location and volume of lesions is of great clinical interest. Current deep learning-based prediction methods mainly use perfusion parameter maps, which can be calculated from spatio-temporal (4D) CT perfusion (CTP) imaging data, to estimate the tissue outcome of an acute ischemic stroke. However, this calculation relies on a deconvolution operation, an ill-posed problem requiring strong regularization and definition of an arterial input function. Thus, improved predictions might be achievable if the deep learning models were applied directly to acute 4D CTP data rather than perfusion maps. In this work, a novel deep spatio-temporal convolutional neural network is proposed for predicting treatment-dependent stroke lesion outcomes by making full use of raw 4D CTP data. By merging a U-Net-like architecture with temporal convolutional networks, we efficiently process the spatio-temporal information available in CTP datasets to make a tissue outcome prediction. The proposed method was evaluated on 147 patients using a 10-fold cross validation, which demonstrated that the proposed 3D+time model (mean Dice=0.45) significantly outperforms both a 2D+time variant of our approach (mean Dice=0.43) and a state-of-the-art method that uses perfusion maps (mean Dice=0.38). These results show that 4D CTP datasets include more predictive information than perfusion parameter maps, and that the proposed method is an efficient approach to make use of this complex data.

Early effect of thrombolysis on structural brain network organisation after anterior‐circulation stroke in the randomized WAKE‐UP trial

The symptoms of acute ischemic stroke can be attributed to disruption of the brain network architecture. Systemic thrombolysis is an effective treatment that preserves structural connectivity in the first days after the event. Its effect on the evolution of global network organisation is, however, not well understood. We present a secondary analysis of 269 patients from the randomized WAKE‐UP trial, comparing 127 imaging‐selected patients treated with alteplase with 142 controls who received placebo. We used indirect network mapping to quantify the impact of ischemic lesions on structural brain network organisation in terms of both global parameters of segregation and integration, and local disruption of individual connections. Network damage was estimated before randomization and again 22 to 36 h after administration of either alteplase or placebo. Evolution of structural network organisation was characterised by a loss in integration and gain in segregation, and this trajectory was attenuated by the administration of alteplase. Preserved brain network organization was associated with excellent functional outcome. Furthermore, the protective effect of alteplase was spatio‐topologically nonuniform, concentrating on a subnetwork of high centrality supported in the salvageable white matter surrounding the ischemic cores. This interplay between the location of the lesion, the pathophysiology of the ischemic penumbra, and the spatial embedding of the brain network explains the observed potential of thrombolysis to attenuate topological network damage early after stroke. Our findings might, in the future, lead to new brain network‐informed imaging biomarkers and improved prognostication in ischemic stroke.

Association of stroke lesion shape with newly detected atrial fibrillation – Results from the MonDAFIS study

Paroxysmal Atrial fibrillation (AF) is often clinically silent and may be missed by the usual diagnostic workup after ischemic stroke. We aimed to determine whether shape characteristics of ischemic stroke lesions can be used to predict AF in stroke patients without known AF at baseline. Lesion shape quantification on brain MRI was performed in selected patients from the intervention arm of the Impact of standardized MONitoring for Detection of Atrial Fibrillation in Ischemic Stroke (MonDAFIS) study, which included patients with ischemic stroke or TIA without prior AF. Multiple morphologic parameters were calculated based on lesion segmentation in acute brain MRI data. Multivariate logistic models were used to test the association of lesion morphology, clinical parameters, and AF. A stepwise elimination regression was conducted to identify the most important variables. A total of 755 patients were included. Patients with AF detected within 2 years after stroke (n = 86) had a larger overall oriented bounding box (OBB) volume (p = 0.003) and a higher number of brain lesion components (p = 0.008) than patients without AF. In the multivariate model, OBB volume (OR 1.72, 95%CI 1.29–2.35, p < 0.001), age (OR 2.13, 95%CI 1.52–3.06, p < 0.001), and female sex (OR 2.45, 95%CI 1.41–4.31, p = 0.002) were independently associated with detected AF. Ischemic lesions in patients with detected AF after stroke presented with a more dispersed infarct pattern and a higher number of lesion components. Together with clinical characteristics, these lesion shape characteristics may help in guiding prolonged cardiac monitoring after stroke.

Magnetic particle imaging for assessment of cerebral perfusion and ischemia

Stroke is one of the leading worldwide causes of death and sustained disability. Rapid and accurate assessment of cerebral perfusion is essential to diagnose and successfully treat stroke patients. Magnetic particle imaging (MPI) is a new technology with the potential to overcome some limitations of established imaging modalities. It is an innovative and radiation-free imaging technique with high sensitivity, specificity, and superior temporal resolution. MPI enables imaging and diagnosis of stroke and other neurological pathologies such as hemorrhage, tumors, and inflammatory processes. MPI scanners also offer the potential for targeted therapies of these diseases. Due to lower field requirements, MPI scanners can be designed as resistive magnets and employed as mobile devices for bedside imaging. With these advantages, MPI could accelerate and improve the diagnosis and treatment of neurological disorders. This review provides a basic introduction to MPI, discusses its current use for stroke imaging, and addresses future applications, including the potential for clinical implementation. This article is categorized under: Diagnostic Tools > In Vivo Nanodiagnostics and Imaging Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease.

Treatment Efficacy Analysis in Acute Ischemic Stroke Patients Using In Silico Modeling Based on Machine Learning: A Proof-of-Principle

Interventional neuroradiology is characterized by engineering- and experience-driven device development with design improvements every few months. However, clinical validation of these new devices requires lengthy and expensive randomized controlled trials. This contribution proposes a machine learning-based in silico study design to evaluate new devices more quickly with a small sample size. Acute diffusion- and perfusion-weighted MRI, segmented one-week follow-up imaging, and clinical variables were available for 90 acute ischemic stroke patients. Three treatment option-specific random forest models were trained to predict the one-week follow-up lesion segmentation for (1) patients successfully recanalized using intra-arterial mechanical thrombectomy, (2) patients successfully recanalized using intravenous thrombolysis, and (3) non-recanalizing patients as an analogue for conservative treatment for each patient in the sample, independent of the true group membership. A repeated-measures analysis of the three predicted follow-up lesions for each patient revealed significantly larger lesions for the non-recanalizing group compared to the successful intravenous thrombolysis treatment group, which in turn showed significantly larger lesions compared to the successful mechanical thrombectomy treatment group (p < 0.001). A groupwise comparison of the true follow-up lesions for the three treatment options showed the same trend but did not reach statistical significance (p = 0.19). We conclude that the proposed machine learning-based in silico trial design leads to clinically feasible results and can support new efficacy studies by providing additional power and potential early intermediate results.

Perfusion Changes in Acute Stroke Treated with Theophylline as an Add-on to Thrombolysis : A Randomized Clinical Trial Subgroup Analysis

PURPOSE

Theophylline has been suggested to have a neuroprotective effect in ischemic stroke; however, results from animal stroke models and clinical trials in humans are controversial. The aim of this study was to assess the effect of theophylline on the cerebral perfusion with multiparametric magnetic resonance imaging (MRI).

METHODS

The relative cerebral blood flow (rCBF), relative cerebral blood volume (rCBV), and relative mean transit time (rMTT) in the infarct core, penumbra, and unaffected tissue were measured using multi-parametric MRI at baseline and 3‑h follow-up in patients treated with theophylline or placebo as an add-on to thrombolytic therapy.

RESULTS

No significant differences in mean rCBF, rCBV, and rMTT was found in the penumbra and unaffected tissue between the theophylline group and the control group between baseline and 3‑h follow-up. In the infarct core, mean rCBV increased on average by 0.05 in the theophylline group and decreased by 0.14 in the control group (p < 0.04). Mean rCBF and mean rMTT in the infarct core were similar between the two treatment groups.

CONCLUSION

The results indicate that theophylline does not change the perfusion in potentially salvageable penumbral tissue but only affects the rCBV in the infarct core. In contrast to the penumbra, the infarct core is unlikely to be salvageable, which might explain why theophylline failed in clinical trials.

Machine Learning-Based Prediction of Brain Tissue Infarction in Patients With Acute Ischemic Stroke Treated With Theophylline as an Add-On to Thrombolytic Therapy: A Randomized Clinical Trial Subgroup Analysis

Background and Purpose: The theophylline in acute ischemic stroke trial investigated the neuroprotective effect of theophylline as an add-on to thrombolytic therapy in patients with acute ischemic stroke. The aim of this pre-planned subgroup analysis was to use predictive modeling to virtually test for differences in the follow-up lesion volumes.

Materials and Methods: A subgroup of 52 patients from the theophylline in acute ischemic stroke trial with multi-parametric MRI data acquired at baseline and at 24-h follow-up were analyzed. A machine learning model using voxel-by-voxel information from diffusion- and perfusion-weighted MRI and clinical parameters was used to predict the infarct volume for each individual patient and both treatment arms. After training of the two predictive models, two virtual lesion outcomes were available for each patient, one lesion predicted for theophylline treatment and one lesion predicted for placebo treatment.

Results: The mean predicted volume of follow-up lesions was 11.4 ml (standard deviation 18.7) for patients virtually treated with theophylline and 11.2 ml (standard deviation 17.3) for patients virtually treated with placebo (p = 0.86).

Conclusions: The predicted follow-up brain lesions for each patient were not significantly different for patients virtually treated with theophylline or placebo, as an add-on to thrombolytic therapy. Thus, this study confirmed the lack of neuroprotective effect of theophylline shown in the main clinical trial and is contrary to the results from preclinical stroke models.

Grey and white matter network disruption is associated with sensory deficits after stroke

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Somatosensory deficits occur in about 60% of patients after ischaemic stroke.

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Clinical and imaging data of 101 ischaemic stroke patients were analysed.

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Stroke lesions may disrupt grey (GM) and/or white matter (WM) network.

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Lesion volume explains 23% of sensory deficit variance; GM / WM disruption adds 14%

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Subnetwork of postcentral, supramarginal, transverse temporal gyri involved.

Somatosensory deficits after ischaemic stroke are common and can occur in patients with lesions in the anterior parietal cortex and subcortical nuclei. It is less clear to what extent damage to white matter tracts within the somatosensory system may contribute to somatosensory deficits after stroke. We compared the roles of cortical damage and disruption of subcortical white matter tracts as correlates of somatosensory deficit after ischaemic stroke.

Clinical and imaging data were assessed in incident stroke patients. Somatosensory deficits were measured using a standardized somatosensory test. Remote effects were quantified by projecting the MRI-based segmented stroke lesions onto a predefined atlas of white matter connectivity. Direct ischaemic damage to grey matter was computed by lesion overlap with grey matter areas. The association between lesion impact scores and sensory deficit was assessed statistically.

In 101 patients, median sensory score was 188/193 (97.4%). Lesion volume was associated with somatosensory deficit, explaining 23.3% of variance. Beyond this, the stroke-induced grey and white matter disruption within a subnetwork of the postcentral, supramarginal, and transverse temporal gyri explained an additional 14% of the somatosensory outcome variability. On mutual comparison, white matter network disruption was a stronger predictor than grey matter damage.

Ischaemic damage to both grey and white matter are structural correlates of acute somatosensory disturbance after ischaemic stroke. Our data suggest that white matter integrity of a somatosensory network of primary and secondary cortex is a prerequisite for normal processing of somatosensory inputs and might be considered as an additional parameter for stroke outcome prediction in the future.

Preserved structural connectivity mediates the clinical effect of thrombolysis in patients with anterior-circulation stroke

Thrombolysis with recombinant tissue plasminogen activator in acute ischemic stroke aims to restore compromised blood flow and prevent further neuronal damage. Despite the proven clinical efficacy of this treatment, little is known about the short-term effects of systemic thrombolysis on structural brain connectivity. In this secondary analysis of the WAKE-UP trial, we used MRI-derived measures of infarct size and estimated structural network disruption to establish that thrombolysis is associated not only with less infarct growth, but also with reduced loss of large-scale connectivity between grey-matter areas after stroke. In a causal mediation analysis, infarct growth mediated a non-significant 8.3% (CI95% [-8.0, 32.6]%) of the clinical effect of thrombolysis on functional outcome. The proportion mediated jointly through infarct growth and change of structural connectivity, especially in the border zone around the infarct core, however, was as high as 33.4% (CI95% [8.8, 77.4]%). Preservation of structural connectivity is thus an important determinant of treatment success and favourable functional outcome in addition to lesion volume. It might, in the future, serve as an imaging endpoint in clinical trials or as a target for therapeutic interventions.

Utility of Multi-Modal MRI for Differentiating of Parkinson's Disease and Progressive Supranuclear Palsy Using Machine Learning

Background: Patients with Parkinson's disease (PD) and progressive supranuclear palsy Richardson's syndrome (PSP-RS) often show overlapping clinical features, leading to misdiagnoses. The objective of this study was to investigate the feasibility and utility of using multi-modal MRI datasets for an automatic differentiation of PD patients, PSP-RS patients, and healthy control (HC) subjects. Material and Methods: T1-weighted, T2-weighted, and diffusion-tensor (DTI) MRI datasets from 45 PD patients, 20 PSP-RS patients, and 38 HC subjects were available for this study. Using an atlas-based approach, regional values of brain morphology (T1-weighted), brain iron metabolism (T2-weighted), and microstructural integrity (DTI) were measured and employed for feature selection and subsequent classification using combinations of various established machine learning methods. Results: The optimal machine learning model using regional morphology features only achieved a classification accuracy of 65% (67/103 correct classifications) differentiating PD patients, PSP-RS patients, and HC subjects. The optimal machine learning model using only quantitative T2 values performed slightly better and achieved an accuracy of 75.7% (78/103). The optimal classifier using DTI features alone performed considerably better with 95.1% accuracy (98/103). The optimal multi-modal classifier using all features also achieved an accuracy of 95.1% but required more features and achieved a slightly lower F1-score compared to the optimal model using DTI features alone. Conclusion: Machine learning models using multi-modal MRI perform significantly better than uni-modal machine learning models using morphological parameters based on T1-weighted MRI datasets alone or brain iron metabolism markers based on T2-weighted MRI datasets alone. However, machine learnig models using regional brain microstructural integrity metrics computed from DTI datasets perform similar to the optimal multi-modal machine learning model. Thus, given the results from this study cohort, it appears that morphology and brain iron metabolism markers may not provide additional value for classification compared to using DTI metrics alone.

Design of a head coil for high resolution mouse brain perfusion imaging using magnetic particle imaging

Magnetic particle imaging (MPI) is a novel and versatile imaging modality developing toward human application. When up-scaling to human size, the sensitivity of the systems naturally drops as the coil sensitivity depends on the bore diameter. Thus, new methods to push the sensitivity limit further have to be investigated to cope for this loss. In this paper a dedicated surface coil for mice is developed, improving the sensitivity in cerebral imaging applications. Similar to magnetic resonance imaging the developed surface coil improves the sensitivity due to the closer vicinity to the region of interest. With the developed surface coil presented in this work, it is possible to image tracer samples containing only 896 pg[Formula: see text]  and detect even small vessels and anatomical structures within a wild type mouse model. As current sensitivity measures require a tracer system a new method for determining a sensitivity measure without this requirement is presented and verified to enable comparison between MPI receiver systems.

Symptoms and probabilistic anatomical mapping of lacunar infarcts

Background

The anatomical distribution of acute lacunar infarcts has mainly been studied for supratentorial lesions. In addition, little is known about the association with distinct stroke symptoms, not summarized as classical lacunar syndromes. We aimed to describe the spatial lesion distribution of acute supra- and infratentorial lacunar infarcts and their association with stroke symptoms in patients eligible for thrombolysis.

Methods

All patients enrolled in the WAKE-UP trial (efficacy and safety of magnetic resonance imaging [MRI]-based thrombolysis in wake-up stroke) were screened for lacunar infarcts on diffusion-weighted imaging (DWI). The relationship between the anatomical distribution of supra- and infratentorial lacunar infarcts, their demographic characteristics and acute stroke symptoms, defined by the National Institutes of Health Stroke Scale (NIHSS) score, were correlated and compared.

Results

Maps of lesion distribution from 224 lacunar infarct patients (76 [33.9%] females, mean age [standard deviation] of 63.4 [11.5] years) were generated using computational image mapping methods. Median infarct volume was 0.73 ml (interquartile range [IQR] 0.37–1.15 ml). Median NIHSS sum score on hospital arrival was 4 (IQR 3–6). 165 (73.7%) patients had lacunar infarcts in the supratentorial deep white or grey matter, while 59 (26.3%) patients had infratentorial lacunar infarcts. Patients with supratentorial lacunar infarcts presented with a significantly lower occurrence of deficits in the NIHSS items gaze (p < 0.001) and dysarthria (p = 0.008), but had more often a paresis of the left arm (p = 0.009) and left leg (p = 0.068) compared to patients with infratentorial infarcts.

Conclusions

The anatomical lesion distribution of lacunar infarcts reveals a distinct pattern and supports an association of localization with different stroke symptoms.

Trial registration

NCT01525290.

Localized prediction of tissue outcome in acute ischemic stroke patients using diffusion- and perfusion-weighted MRI datasets

BACKGROUND

An accurate prediction of tissue outcome in acute ischemic stroke patients is of high interest for treatment decision making. To date, various machine learning models have been proposed that combine multi-parametric imaging data for this purpose. However, most of these machine learning models were trained using voxel information extracted from the whole brain, without taking differences in susceptibility to ischemia into account that exist between brain regions. The aim of this study was to develop and evaluate a local tissue outcome prediction approach, which makes predictions using locally trained machine learning models and thus accounts for regional differences.

MATERIAL AND METHODS

Multi-parametric MRI data from 99 acute ischemic stroke patients were used for the development and evaluation of the local tissue outcome prediction approach. Diffusion (ADC) and perfusion parameter maps (CBF, CBV, MTT, Tmax) and corresponding follow-up lesion masks for each patient were registered to the MNI brain atlas. Logistic regression (LR) and random forest (RF) models were trained employing a local approach, which makes predictions using models individually trained for each specific voxel position using the corresponding local data. A global approach, which uses a single model trained using all voxels of the brain, was used for comparison. Tissue outcome predictions resulting from the global and local RF and LR models, as well as a combined (hybrid) approach were quantitatively evaluated and compared using the area under the receiver operating characteristic curve (ROC AUC), the Dice coefficient, and the sensitivity and specificity metrics.

RESULTS

Statistical analysis revealed the highest ROC AUC and Dice values for the hybrid approach. With 0.872 (ROC AUC; LR) and 0.353 (Dice; RF), these values were significantly higher (p < 0.01) than the values of the two other approaches. In addition, the local approach achieved the highest sensitivity of 0.448 (LR). Overall, the hybrid approach was only outperformed in sensitivity (LR) by the local approach and in specificity by both other approaches. However, in these cases the effect sizes were comparatively small.

CONCLUSION

The results of this study suggest that using locally trained machine learning models can lead to better lesion outcome prediction results compared to a single global machine learning model trained using all voxel information independent of the location in the brain.

Normalization of reduced functional connectivity after revascularization of asymptomatic carotid stenosis

Internal carotid artery stenosis is a risk factor for ischemic stroke. Even in the absence of visible structural brain changes, patients with asymptomatic stenosis are prone to cognitive impairment. On a neuronal level, it was suggested that stenosis may lead to disturbed functional brain connectivity. If so, carotid revascularization should have an effect on hypothesized brain network disturbances. We studied functional connectivity in a motor network by resting-state electroencephalography in 12 patients with high grade asymptomatic carotid stenosis before and after interventional or surgical revascularization as compared to 23 controls. In patients with stenosis, functional connectivity of neural oscillations was significantly decreased prior and improved returning to normal connectivity after revascularization. In a subgroup of patients, also studied by contrast perfusion magnetic resonance imaging, reduced connectivity was associated with decreased regional brain perfusion reflected by increased mean transit time in the middle cerebral artery borderzone. Cognitive testing revealed only minor differences between patients and controls. In summary, we identified oscillatory connectivity changes in patients with asymptomatic carotid stenosis correlating with regional hypoperfusion, which both normalized after revascularization. Hence, electrophysiological changes might be a reversible precursor preceding macroscopic structural brain damage and behavioral impairment in patients with asymptomatic carotid stenosis.

Ischemic lesion growth in acute stroke: Water uptake quantification distinguishes between edema and tissue infarct

Infarct growth from the early ischemic core to the total infarct lesion volume (LV) is often used as an outcome variable of treatment effects, but can be overestimated due to vasogenic edema. The purpose of this study was (1) to assess two components of early lesion growth by distinguishing between water uptake and true net infarct growth and (2) to investigate potential treatment effects on edema-corrected net lesion growth. Sixty-two M1-MCA-stroke patients with acute multimodal and follow-up CT (FCT) were included. Ischemic lesion growth was calculated by subtracting the initial CTP-derived ischemic core volume from the LV in the FCT. To determine edema-corrected net lesion growth, net water uptake of the ischemic lesion on FCT was quantified and subtracted from the volume of uncorrected lesion growth. The mean lesion growth without edema correction was 20.4 mL (95% CI: 8.2-32.5 mL). The mean net lesion growth after edema correction was 7.3 mL (95% CI: -2.1-16.7 mL; p < 0.0001). Lesion growth was significantly overestimated due to ischemic edema when determined in early-FCT imaging. In 18 patients, LV was lower than the initial ischemic core volume by CTP. These apparently "reversible" core lesions were more likely in patients with shorter times from symptom onset to imaging and higher recanalization rates.

Structural integrity of white matter tracts as a predictor of acute ischemic stroke outcome

BACKGROUND AND PURPOSE

Clinical assessment scores in acute ischemic stroke are only moderately correlated with lesion volume since lesion location is an important confounding factor. Many studies have investigated gray matter indicators of stroke severity, but the understanding of white matter tract involvement is limited in the early phase after stroke. This study aimed to measure and model the involvement of white matter tracts with respect to 24-h post-stroke National Institutes of Health Stroke Scale (NIHSS).

MATERIAL AND METHODS

A total of 96 patients (50 females, mean age 66.4 ± 14.0 years, median NIHSS 5, interquartile range: 2-9.5) with follow-up fluid-attenuated inversion recovery magnetic resonance imaging data sets acquired one to seven days after acute ischemic stroke onset due to proximal anterior circulation occlusion were included. Lesions were semi-automatically segmented and non-linearly registered to a common reference atlas. The lesion overlap and tract integrity were determined for each white matter tract in the AALCAT atlas and used to model NIHSS outcomes using a supervised linear-kernel support vector regression method, which was evaluated using leave-one-patient-out cross validation.

RESULTS

The support vector regression model using the tract integrity and tract lesion overlap measurements predicted the 24-h NIHSS score with a high correlation value of r = 0.7. Using the tract overlap and tract integrity feature improved the modeling accuracy of NIHSS significantly by 6% (p < 0.05) compared to using overlap measures only.

CONCLUSION

White matter tract integrity and lesion load are important predictors for clinical outcome after an acute ischemic stroke as measured by the NIHSS and should be integrated for predictive modeling.

Elevated brain oxygen extraction fraction measured by MRI susceptibility relates to perfusion status in acute ischemic stroke

Recent clinical trials of new revascularization therapies in acute ischemic stroke have highlighted the importance of physiological imaging to identify optimal treatments for patients. Oxygen extraction fraction (OEF) is a hallmark of at-risk tissue in stroke, and can be quantified from the susceptibility effect of deoxyhemoglobin molecules in venous blood on MRI phase scans. We measured OEF within cerebral veins using advanced quantitative susceptibility mapping (QSM) MRI reconstructions in 20 acute stroke patients. Absolute OEF was elevated in the affected (29.3 ± 3.4%) versus the contralateral hemisphere (25.5 ± 3.1%) of patients with large diffusion-perfusion lesion mismatch (P = 0.032). In these patients, OEF negatively correlated with relative CBF measured by dynamic susceptibility contrast MRI (P = 0.004), suggesting compensation for reduced flow. Patients with perfusion-diffusion match or no hypo-perfusion showed less OEF difference between hemispheres. Nine patients received longitudinal assessment and showed OEF ratio (affected to contralateral) of 1.2 ± 0.1 at baseline that normalized (decreased) to 1.0 ± 0.1 at follow-up three days later (P = 0.03). Our feasibility study demonstrates that QSM MRI can non-invasively quantify OEF in stroke patients, relates to perfusion status, and is sensitive to OEF changes over time. Clinical trial registration: Longitudinal MRI examinations of patients with brain ischemia and blood brain barrier permeability; clinicaltrials.org :NCT02077582.

Improved multi-parametric prediction of tissue outcome in acute ischemic stroke patients using spatial features

Introduction

In recent years, numerous methods have been proposed to predict tissue outcome in acute stroke patients using machine learning methods incorporating multiparametric imaging data. Most methods include diffusion and perfusion parameters as image-based parameters but do not include any spatial information although these parameters are spatially dependent, e.g. different perfusion properties in white and gray brain matter. This study aims to investigate if including spatial features improves the accuracy of multi-parametric tissue outcome prediction.

Materials and methods

Acute and follow-up multi-center MRI datasets of 99 patients were available for this study. Logistic regression, random forest, and XGBoost machine learning models were trained and tested using acute MR diffusion and perfusion features and known follow-up lesions. Different combinations of atlas coordinates and lesion probability maps were included as spatial information. The stroke lesion predictions were compared to the true tissue outcomes using the area under the receiver operating characteristic curve (ROC AUC) and the Dice metric.

Results

The statistical analysis revealed that including spatial features significantly improves the tissue outcome prediction. Overall, the XGBoost and random forest models performed best in every setting and achieved state-of-the-art results regarding both metrics with similar improvements achieved including Montreal Neurological Institute (MNI) reference space coordinates or voxel-wise lesion probabilities.

Conclusion

Spatial features should be integrated to improve lesion outcome prediction using machine learning models.

Somatosensory Deficits After Ischemic Stroke

Background and Purpose- About 50% to 80% of stroke survivors present with somatosensory deficits. Somatosensory deficits because of an ischemic stroke are determined by the infarct location. However, a detailed understanding of the long-term effect of lesions on somatosensory performance is lacking. Methods- This prospective observational study enrolled 101 ischemic stroke patients. For voxel-based lesion-symptom mapping, magnetic resonance imaging fluid-attenuated inversion recovery imaging infarct lesions were segmented within 5 days after stroke. Standardized tests such as the National Institutes of Health Stroke Scale and the Rivermead Assessment of Somatosensory Performance were performed during acute stage, after 3 and 12 months. This included bilateral testing for multiple tactile and proprioceptive somatosensory modalities (pressure, light touch, sharp-dull discrimination, temperature discrimination, sensory extinction, 2-point discrimination, and joint position and movement sense). We further study the association of acute somatosensory deficit with functional outcome 12 months after stroke assessed by the modified Rankin Scale using univariate and multiple linear regression analysis also including acute motor deficit assessed by the arm research action test. Results- Sixty patients (59.4%) showed impairment in at least one somatosensory modality. Light touch was most frequently affected (38.7%), whereas temperature was least frequently affected (21.8%). After 3 months, significant recovery was observed in all somatosensory modalities, with only minor additional improvements after 12 months. Voxel-based lesion-symptom mapping revealed significant associations of lesions in the primary and secondary somatosensory and insular cortex with somatosensory deficits. Acute somatosensory deficit was associated with functional outcome at 12 months. However, including the acute motor deficit, somatosensory deficit was no longer an independent predictor of functional outcome. Conclusions- Our study confirms that somatosensory deficits are frequent in acute ischemic stroke but largely recover over time. Infarct lesions in the primary and secondary somatosensory cortex and insula show a robust association with somatosensory impairment. Long-term disability is influenced by somatosensory deficits but driven by motor symptoms.

Quantitative Signal Intensity in Fluid-Attenuated Inversion Recovery and Treatment Effect in the WAKE-UP Trial

Background and Purpose—

Relative signal intensity of acute ischemic stroke lesions in fluid-attenuated inversion recovery (fluid-attenuated inversion recovery relative signal intensity [FLAIR-rSI]) magnetic resonance imaging is associated with time elapsed since stroke onset with higher intensities signifying longer time intervals. In the randomized controlled WAKE-UP trial (Efficacy and Safety of MRI-Based Thrombolysis in Wake-Up Stroke Trial), intravenous alteplase was effective in patients with unknown onset stroke selected by visual assessment of diffusion weighted imaging fluid-attenuated inversion recovery mismatch, that is, in those with no marked fluid-attenuated inversion recovery hyperintensity in the region of the acute diffusion weighted imaging lesion. In this post hoc analysis, we investigated whether quantitatively measured FLAIR-rSI modifies treatment effect of intravenous alteplase.

Methods—

FLAIR-rSI of stroke lesions was measured relative to signal intensity in a mirrored region in the contralesional hemisphere. The relationship between FLAIR-rSI and treatment effect on functional outcome assessed by the modified Rankin Scale (mRS) after 90 days was analyzed by binary logistic regression using different end points, that is, favorable outcome defined as mRS score of 0 to 1, independent outcome defined as mRS score of 0 to 2, ordinal analysis of mRS scores (shift analysis). All models were adjusted for National Institutes of Health Stroke Scale at symptom onset and stroke lesion volume.

Results—

FLAIR-rSI was successfully quantified in stroke lesions in 433 patients (86% of 503 patients included in WAKE-UP). Mean FLAIR-rSI was 1.06 (SD, 0.09). Interaction of FLAIR-rSI and treatment effect was not significant for mRS score of 0 to 1 ( P =0.169) and shift analysis ( P =0.086) but reached significance for mRS score of 0 to 2 ( P =0.004). We observed a smooth continuing trend of decreasing treatment effects in relation to clinical end points with increasing FLAIR-rSI.

Conclusions—

In patients in whom no marked parenchymal fluid-attenuated inversion recovery hyperintensity was detected by visual judgement in the WAKE-UP trial, higher FLAIR-rSI of diffusion weighted imaging lesions was associated with decreased treatment effects of intravenous thrombolysis. This parallels the known association of treatment effect and elapsing time of stroke onset.

Temporal evolution and spatial distribution of quantitative T2 MRI following acute ischemia reperfusion injury

Background

Determining mechanisms of secondary stroke injury related to cerebral blood flow and the severity of microvascular injury contributing to edema and blood-brain barrier breakdown will be critical for the development of adjuvant therapies for revascularization treatment.

Aim

To characterize the heterogeneity of the ischemic lesion using quantitative T2 imaging along with diffusion-weighted magnetic resonance imaging (DWI) within five hours of treatment.

Methods

Quantitative T2 magnetic resonance imaging was acquired within 5 h (baseline) and at 24 h (follow-up) of stroke treatment in 29 patients. Dynamic contrast enhanced permeability imaging was performed at baseline in a subgroup of patients. Absolute volume change and lesion percent change was determined for the quantitative T2, DWI, and absolute volume change sequences. A Gaussian process with RRELIEFF feature selection algorithm was used for prediction of relative quantitative T2 and DWI lesion growth, baseline and follow-up quantitative T2/DWI lesion ratios, and also NIHSS at 24 h and change in NIHSS from admission to 24 h.

Results

In n = 27 patients, median (interquartile range) lesion percent change was 114.8% (48.9%, 259.1%) for quantitative T2, 48.2% (−12.6%, 179.6%) for absolute volume change, and 62.7% (26.3%, 230.9%) for DWI, respectively. Our model, consisting of baseline NIHSS, CT ASPECTS, and systolic blood pressure, showed a strong correlation with quantitative T2 percent change (cross correlation R2 = 0.80). There was a strong predictive ability for quantitative T2/DWI lesion ratio at 24 h using baseline NIHSS and last seen normal to 24 h magnetic resonance imaging time (cross correlation R2 = 0.93). Baseline dynamic contrast enhanced permeability was moderately correlated to the baseline quantitative T2 values (rho = 0.38).

Conclusion

Quantitative T2 imaging provides critical information for development of therapeutic approaches that could ameliorate microvascular damage during ischemia reperfusion.

Technical considerations of multi-parametric tissue outcome prediction methods in acute ischemic stroke patients

Decisions regarding acute stroke treatment rely heavily on imaging, but interpretation can be difficult for physicians. Machine learning methods can assist clinicians by providing tissue outcome predictions for different treatment approaches based on acute multi-parametric imaging. To produce such clinically viable machine learning models, factors such as classifier choice, data normalization, and data balancing must be considered. This study gives comprehensive consideration to these factors by comparing the agreement of voxel-based tissue outcome predictions using acute imaging and clinical parameters with manual lesion segmentations derived from follow-up imaging. This study considers random decision forest, generalized linear model, and k-nearest-neighbor machine learning classifiers in conjunction with three data normalization approaches (non-normalized, relative to contralateral hemisphere, and relative to contralateral VOI), and two data balancing strategies (full dataset and stratified subsampling). These classifier settings were evaluated based on 90 MRI datasets from acute ischemic stroke patients. Distinction was made between patients recanalized using intraarterial and intravenous methods, as well as those without successful recanalization. For primary quantitative comparison, the Dice metric was computed for each voxel-based tissue outcome prediction and its corresponding follow-up lesion segmentation. It was found that the random forest classifier outperformed the generalized linear model and the k-nearest-neighbor classifier, that normalization did not improve the Dice score of the lesion outcome predictions, and that the models generated lesion outcome predictions with higher Dice scores when trained with balanced datasets. No significant difference was found between the treatment groups (intraarterial vs intravenous) regarding the Dice score of the tissue outcome predictions.

Stroke Lesion Segmentation in FLAIR MRI Datasets Using Customized Markov Random Fields

Robust and reliable stroke lesion segmentation is a crucial step toward employing lesion volume as an independent endpoint for randomized trials. The aim of this work was to develop and evaluate a novel method to segment sub-acute ischemic stroke lesions from fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) datasets. After preprocessing of the datasets, a Bayesian technique based on Gabor textures extracted from the FLAIR signal intensities is utilized to generate a first estimate of the lesion segmentation. Using this initial segmentation, a customized voxel-level Markov random field model based on intensity as well as Gabor texture features is employed to refine the stroke lesion segmentation. The proposed method was developed and evaluated based on 151 multi-center datasets from three different databases using a leave-one-patient-out validation approach. The comparison of the automatically segmented stroke lesions with manual ground truth segmentation revealed an average Dice coefficient of 0.582, which is in the upper range of previously presented lesion segmentation methods using multi-modal MRI datasets. Furthermore, the results obtained by the proposed technique are superior compared to the results obtained by two methods based on convolutional neural networks and three phase level-sets, respectively, which performed best in the ISLES 2015 challenge using multi-modal imaging datasets. The results of the quantitative evaluation suggest that the proposed method leads to robust lesion segmentation results using FLAIR MRI datasets only as a follow-up sequence.

Dynamics of brain perfusion and cognitive performance in revascularization of carotid artery stenosis

INTRODUCTION

There is evidence suggesting a detrimental effect of asymptomatic carotid artery stenosis on cognitive function even in the absence of ischemic cerebral lesions. Hypoperfusion has been suggested as pathophysiological mechanism causing cognitive impairment. We aimed to assess cognitive performance and cerebral perfusion changes in patients with carotid artery stenosis without ischemic lesions by arterial spin labeling (ASL) and contrast enhanced (CE) perfusion MRI before and after revascularization therapy.

METHODS

17 asymptomatic patients with unilateral high-grade (≥70%) carotid artery stenosis without evidence of structural brain lesions underwent ASL and CE perfusion MRI and cognitive testing (MMSE, DemTect, Clock-Drawing Test, Trail-Making Test, Stroop Test) before and 6-8 weeks after revascularization therapy by endarterectomy or stenting. Multiparametric perfusion maps (ASL: cerebral blood flow (ASL-CBF), bolus arrival time (ASL-BAT); CE: cerebral blood flow (CE-CBF), mean transit time (CE-MTT), cerebral blood volume (CE-CBV)) were calculated and analyzed by vascular territory. Relative perfusion values were calculated.

RESULTS

Multivariate analysis revealed a significant impact of revascularization therapy on all perfusion measures analyzed. At baseline post-hoc testing showed significant hypoperfusion in MCA borderzones as assessed by ASL-CBF, ASL-BAT, CE-MTT and CE-CBV. All perfusion alterations normalized after revascularization. We did not observe any significant correlation of cognitive test results with perfusion parameters. There was no significant change in cognitive performance after revascularization.

CONCLUSION

We found evidence of traceable perfusion alterations in patients with high grade carotid artery stenosis in the absence of structural brain lesions, which proved fully reversible after revascularization therapy. In this cohort of asymptomatic patients we did not observe an association of hypoperfusion with cognitive performance.

Dynamics of Water Diffusion Changes in Different Tissue Compartments From Acute to Chronic Stroke-A Serial Diffusion Tensor Imaging Study

Background and Purpose: The immediate decrease of the apparent diffusion coefficient (ADC) is the main characteristic change of water diffusion in acute ischemic stroke. There is only limited information on the time course of diffusion parameters in different tissue compartments of cerebral ischemia.

Materials and Methods: In a longitudinal study, we examined 21 patients with acute ischemic stroke by diffusion tensor imaging within 5 h after symptom onset, 3 h later, 2 days, and 1 month after symptom onset. Acute diffusion lesion and the fluid-attenuated inversion recovery (FLAIR) after 2 days were used as volumes of interest to define persistent core, lesion growth, and reversible acute diffusion lesion. For all diffusion parameters ratios between the stroke lesion VOIs and the mirror VOIs were calculated for each time point. ADC ratio, fractional anisotropy ratios, and eigenvalues ratios were measured in these volumes of interest and in contralateral mirror regions at each time points.

Results: In the persistent core, ADC ratio (0.772) and all eigenvalues ratios were reduced on admission up to 1 day after stroke and increased after 1 month (ADC ratio 1.067). Within the region of infarct growth time course of diffusion parameter changes was similar, but delayed. In the brain area with reversible diffusion lesion, a partial normalization of diffusion parameters over the time was observed, while after 1 month diffusion parameters did not show the signature of healthy brain tissue. There were significantly different trends for all parameters over time between the three tissue compartments.

Conclusion: Diffusion tensor imaging displays characteristic changes of water diffusion in different tissue compartments over time in acute ischemic stroke. Even regions with reversible diffusion lesion show diffusion signatures of persisting tissue alterations.

Diffusion-weighted imaging lesion growth occurs despite recanalization in acute ischemic stroke: Implications for future treatment trials

Background

A proportion of patients presenting with acute small ischemic strokes have poor functional outcomes, even following rapid recanalization treatment.

Aims

Infarct growth may occur even after successful recanalization and could represent an appropriate endpoint for future stroke therapy trials.

Methods

Magnetic resonance diffusion-weighted imaging lesion volumes were obtained at 5 h (initial posttreatment) and 24 h (follow-up) after acute stroke treatment for n = 33 in ischemic stroke patients. Sample sizes per arm (90% power, 30% effect size) for diffusion-weighted imaging lesion growth between initial and 24 h, early change in the National Institutes of Health Stroke Scale between pre- and 24 h, National Institutes of Health Stroke Scale at 24 h, and diffusion-weighted imaging lesion volume at 24 h were estimated to power a placebo-controlled stroke therapy trial.

Results

For patients with poor recanalization (modified thrombolysis in cerebral infarction <2 a; modified arterial occlusion lesion = 0–2) (n = 11), the median diffusion-weighted imaging lesion growth was 8.1 (interquartile range: 4.5, 22.4) ml and with good recanalization (modified thrombolysis in cerebral infarction =2 b or 3; modified arterial occlusion lesion = 3) (n = 22), the median diffusion-weighted imaging lesion growth was 10.0 (interquartile range: 6.0, 28.2) ml ( P = 0.749). When considering a 30% effect size, the sample size required per arm to achieve significance in an acute stroke study would be: (1) N = 49 for the diffusion-weighted imaging lesion growth between initial posttreatment and follow-up time points, (2) N = 65 for the change in the National Institutes of Health Stroke Scale between admission and 24 h, (3) N = 259 for the National Institutes of Health Stroke Scale at 24 h, and (4) N = 256 for diffusion-weighted imaging volume at 24 h.

Conclusion

Despite best efforts to recanalize the ischemic brain, early diffusion-weighted imaging lesion growth still occurs. Treatment trials in stroke should consider early diffusion-weighted imaging lesion growth as a surrogate outcome measure to significantly reduce sample sizes.

[Imaging in acute ischemic stroke using automated postprocessing algorithms]

There are several automated analytical methods to detect thromboembolic vascular occlusions, the infarct core and the potential infarct-endangered tissue (tissue at risk) by means of multimodal computed tomography (CT) and magnetic resonance imaging (MRI). The infarct core is more reliably visualized by diffusion-weighted imaging (DWI) MRI or CT perfusion than by native CT. The extent of tissue at risk and endangerment can only be estimated; however, it seems essential whether "tissue at risk" actually exists. To ensure consistent patient care, uniform imaging protocols should be acquired in the referring hospital and thrombectomy center and the collected data should be standardized and automatically evaluated and presented. Whether patients with a large infarct core and with or without tissue at risk or patients with large vessel occlusion (LVO) but low NIHSS benefit from thrombectomy has to be evaluated in controlled clinical trials using standardized imaging protocols. A promising, potentially time-saving approach is also native CT and CT angiography using a flat-panel detector angiography system for assessment of vessel occlusion and leptomeningeal collaterals.

Clinical Outcome of Isolated Cerebellar Stroke-A Prospective Observational Study

Background: The aim of this prospective study was to investigate clinical deficits of patients with isolated cerebellar stroke applying a dedicated clinical score, the modified International Cooperative Ataxia Rating Scale (MICARS) and identifying factors that influence recovery.

Methods: Fifteen patients with acute isolated cerebellar stroke received a standard stroke MRI on the day of admission and were clinically assessed using the mRS, NIHSS and the modified International Cooperative Ataxia Rating Scale (MICARS) on day 1, 3, 7, 30, and 90. A generalized linear model for repeated measures was employed to analyze the effect of stroke lesion location, volume, days after stroke, patient age, and MICARS score at admission on the total MICARS score.

Results: Median patient age was 54 years, lesion location in most cases was right (87%) and in the PICA territory (11/15). Median lesion volume was 3.2 ml. Median NIHSS was 1. The median MICARS decreased from on day 1 with 23–4 at day 90. The generalized linear model identified MICARS score at day 1, lesion location, days after admission and the interaction of the last two on the total MICARS score, whereas there was no significant effect of stroke volume or patient age.

Conclusions: Isolated cerebellar stroke can present with low NIHSS while more specific scales like the MICARS indicate a severe deficit. Patient age at onset of stroke and lesion volume had no significant effect on recovery from cerebellar symptoms as opposed to severity of symptoms at admission and lesion location.

Impact of Lesion Load Thresholds on Alberta Stroke Program Early Computed Tomographic Score in Diffusion-Weighted Imaging

Background and aims

Assessment of ischemic lesions on computed tomography or MRI diffusion-weighted imaging (DWI) using the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is widely used to guide acute stroke treatment. However, it has never been defined how many voxels need to be affected to label a DWI-ASPECTS region ischemic. We aimed to assess the effect of various lesion load thresholds on DWI-ASPECTS and compare this automated analysis with visual rating.

Materials and methods

We analyzed overlap of individual DWI lesions of 315 patients from the previously published predictive value of fluid-attenuated inversion recovery study with a probabilistic ASPECTS template derived from 221 CT images. We applied multiple lesion load thresholds per DWI-ASPECTS region (>0, >1, >10, and >20% in each DWI-ASPECTS region) to compute DWI-ASPECTS for each patient and compared the results to visual reading by an experienced stroke neurologist.

Results

By visual rating, median ASPECTS was 9, 84 patients had a DWI-ASPECTS score ≤7. Mean DWI lesion volume was 22.1 (±35) ml. In contrast, by use of >0, >1-, >10-, and >20%-thresholds, median DWI-ASPECTS was 1, 5, 8, and 10; 97.1% (306), 72.7% (229), 41% (129), and 25.7% (81) had DWI-ASPECTS ≤7, respectively. Overall agreement between automated assessment and visual rating was low for every threshold used (>0%: κ_w = 0.020 1%: κ_w = 0.151; 10%: κ_w = 0.386; 20% κ_w = 0.381). Agreement for dichotomized DWI-ASPECTS ranged from fair to substantial (≤7: >10% κ = 0.48; >20% κ = 0.45; ≤5: >10% κ = 0.528; and >20% κ = 0.695).

Conclusion

Overall agreement between automated and the standard used visual scoring is low regardless of the lesion load threshold used. However, dichotomized scoring achieved more comparable results. Varying lesion load thresholds had a critical impact on patient selection by ASPECTS. Of note, the relatively low lesion volume and lack of patients with large artery occlusion in our cohort may limit generalizability of these findings.

Characteristics associated with outcome in patients with first-ever posterior fossa stroke

BACKGROUND AND PURPOSE

Factors such as infarct volume, infarct location and symptom severity can considerably influence long-term outcome in posterior fossa strokes. The decision about therapy can sometimes be complicated by discrepancies between infarct volume and clinical severity. We aimed to evaluate imaging and clinical parameters possibly influencing long-term outcome in patients with first-ever posterior fossa stroke.

METHODS

Imaging was performed on a 3-T magnetic resonance imaging scanner. Sixty-one of 1795 patients from the observational 1000Plus and LOBI studies (NCT00715533 and NCT02077582, clinicaltrials.org) were enrolled, meeting the inclusion criteria of first-ever posterior fossa stroke and magnetic resonance imaging examination within 24 h after symptom onset. Infarcts were classified as belonging to a proximal, middle or distal territory location in the posterior fossa. Good outcome was defined as a modified Rankin scale score of ≤1 at 3 months.

RESULTS

The largest lesion volumes on diffusion-weighted imaging on day 0 and fluid attenuation inversion recovery (FLAIR) on day 6 were found in the middle territory location with a median volume of 0.4 mL on diffusion-weighted imaging and 1.0 mL on FLAIR on day 6 versus 0.1/0.3 mL in the proximal and 0.1/0.1 mL in the distal territory location of the posterior fossa, respectively. Parameters associated with poor outcome were older age (P = 0.005), higher National Institutes of Health Stroke Scale score on admission/discharge (P = 0.016; P = 0.001), larger lesion volumes on FLAIR on day 6 (P = 0.013) and dysphagia (P = 0.02). There was no significant association between infarct location and modified Rankin scale score on day 90.

CONCLUSION

Infarct volume and clinical severity, but not infarct location, were the main contributors to poor long-term outcome in first-ever posterior fossa strokes.

Stroke subtype classification by geometrical descriptors of lesion shape

Background and purpose

Inference of etiology from lesion pattern in acute magnetic resonance imaging is valuable for management and prognosis of acute stroke patients. This study aims to assess the value of three-dimensional geometrical lesion-shape descriptors for stroke-subtype classification, specifically regarding stroke of cardioembolic origin.

Methods

Stroke Etiology was classified according to ASCOD in retrospectively selected patients with acute stroke. Lesions were segmented on diffusion-weighed datasets, and descriptors of lesion shape quantified: surface area, sphericity, bounding box volume, and ratio between bounding box and lesion volume. Morphological measures were compared between stroke subtypes classified by ASCOD and between patients with embolic stroke of cardiac and non-cardiac source.

Results

150 patients (mean age 77 years; 95% CI, 65–80 years; median NIHSS 6, range 0–22) were included. Group comparison of lesion shape measures demonstrated that lesions caused by small-vessel disease were smaller and more spherical compared to other stroke subtypes. No significant differences of morphological measures were detected between patients with cardioembolic and non-cardioembolic stroke.

Conclusion

Stroke lesions caused by small vessel disease can be distinguished from other stroke lesions based on distinctive morphological properties. However, within the group of embolic strokes, etiology could not be inferred from the morphology measures studied in our analysis.

Comprehensive analysis of early fractional anisotropy changes in acute ischemic stroke

BACKGROUND AND PURPOSE

Cerebral ischemia leads to a rapid decrease of the apparent diffusion coefficient. For fractional anisotropy both increase and decrease have been reported in acute ischemic stroke. Aim of this study was to characterize early water diffusion changes in a homogenous group of acute stroke patients and to clarify the issue of early fractional anisotropy changes and their relation to time from symptom onset.

METHODS

MRI data of patients with acute ischemic stroke examined by diffusion tensor imaging within 8h after symptom were analyzed. We calculated fractional anisotropy, eigenvalues and the isotropic and anisotropic components of the diffusion tensor. The values were calculated as ratios between the ischemic lesion and a mirror region in the unaffected side and correlated with clinical parameters.

RESULTS

We included 63 patients: 49% female, mean age 69 ± 14 years, median NIHSS on admission 9 (IQR 4-14). For the whole sample, mean fractional anisotropy was increased (ratio: 1.083 ± 0.168), while all other diffusion parameters were decreased. Both the isotropic and anisotropic component of the diffusion tensor were decreased with a more pronounced decrease of the isotropic component (ratios: isotropic = 0.730 ± 0.106, anisotropic = 0.788 ± 0.127; p<0.001). There was no correlation of fractional anisotropy with time from symptom onset. Looking at individual patients, fractional anisotropy was increased in 70%. There were no differences in clinical characteristics between patients with increased and decreased fractional anisotropy.

CONCLUSION

Fractional anisotropy increase in acute stroke results from a more pronounced decrease of the isotropic diffusion component and is not related to time from symptom onset. Thus, fractional anisotropy is not helpful as a surrogate marker of lesion age in the very first hours of stroke.

Magnetic Particle Imaging for Real-Time Perfusion Imaging in Acute Stroke

The fast and accurate assessment of cerebral perfusion is fundamental for the diagnosis and successful treatment of stroke patients. Magnetic particle imaging (MPI) is a new radiation-free tomographic imaging method with a superior temporal resolution, compared to other conventional imaging methods. In addition, MPI scanners can be built as prehospital mobile devices, which require less complex infrastructure than computed tomography (CT) and magnetic resonance imaging (MRI). With these advantages, MPI could accelerate the stroke diagnosis and treatment, thereby improving outcomes. Our objective was to investigate the capabilities of MPI to detect perfusion deficits in a murine model of ischemic stroke. Cerebral ischemia was induced by inserting of a microfilament in the internal carotid artery in C57BL/6 mice, thereby blocking the blood flow into the medial cerebral artery. After the injection of a contrast agent (superparamagnetic iron oxide nanoparticles) specifically tailored for MPI, cerebral perfusion and vascular anatomy were assessed by the MPI scanner within seconds. To validate and compare our MPI data, we performed perfusion imaging with a small animal MRI scanner. MPI detected the perfusion deficits in the ischemic brain, which were comparable to those with MRI but in real-time. For the first time, we showed that MPI could be used as a diagnostic tool for relevant diseases in vivo, such as an ischemic stroke. Due to its shorter image acquisition times and increased temporal resolution compared to that of MRI or CT, we expect that MPI offers the potential to improve stroke imaging and treatment.

Diffusion-Weighted MRI Stroke Volume Following Recanalization Treatment is Threshold-Dependent

PURPOSE

Infarct lesion segmentation has been problematic as there are a wide range of relative and absolute diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) thresholds that have been used for this purpose. We examined differences of stroke lesion volume and evolution evaluated by magnetic resonance imaging (MRI) during the immediate post-treatment phase (<5 h) and at 24 h.

METHODS

In this study 33 acute ischemic stroke patients were imaged with MRI <5 h and 24 h post-reperfusion treatment. Lesion volumes were segmented on ADC maps and average DWI using literature cited absolute ADC and relative DWI thresholds. The segmented lesion volumes within both time points were compared and the absolute change in lesion volume (infarct growth) between the two time points was calculated and compared using Bland-Altman analysis.

RESULTS

Lesion volumes differed significantly when different relative DWI or absolute ADC thresholds were used (p < 0.05), which held true for baseline as well as follow-up lesions. The median absolute changes in lesion volume from baseline to follow-up for ADC thresholds of 550 × 10^-6 mm²/s, 600 × 10^-6 mm²/s, 630 × 10^-6 mm²/s and 650 × 10^-6 mm²/s were 3.5 ml, 4.2 ml, 4.5 ml, and 6.5 ml, respectively (p < 0.05). Likewise, the median absolute changes in lesion volume from baseline to follow-up for DWI thresholds, k = 0.85, 1.28, 1.64, 1.96, and 2.7 were 10.1 ml, 7.3 ml, 5.7 ml, 5.4 ml and 4.2 ml, respectively (p < 0.05).

CONCLUSION

Absolute lesion volumes and changes in lesion volumes (infarct growth) measured after recanalization treatment were dependent on absolute ADC and relative DWI thresholds, which may have clinical significance. Standardization of techniques for measuring DWI lesion volumes requires immediate attention.

Automated Infarct Core Volumetry Within the Hypoperfused Tissue: Technical Implementation and Evaluation

OBJECTIVE

The aim of this study was to develop a rapid and fully automatic infarct core and tissue at risk volumetry approach in acute ischemic stroke.

METHODS

We evaluated an algorithm in which segmentation was restricted to 1 hemisphere and the potential lesion characterized on the basis of the perfusion parameter Tmax with a region-wise comparison of local histograms to its mirrored counterpart.

RESULTS

We applied the "Tmax inside" method to 30 cases of a public data set with ground-truth segmentations for diffusion-weighted and perfusion magnetic resonance imaging. Lesions were robustly identified with significantly higher dice coefficients (apparent diffusion coefficient, 0.83 ± 0.22; Tmax, 0.80 ± 0.05, compared with 0.53 ± 0.27 and 0.56 ± 0.18) than for a global thresholding approach.

CONCLUSIONS

The proposed "Tmax inside" method is superior to the commonly used global thresholding approach. Furthermore, the method allows evaluating changes in cerebral blood volume and blood flow by taking the counterpart in the healthy hemisphere as a patient-individual reference.

ERic Acute StrokE Recanalization: A study using predictive analytics to assess a new device for mechanical thrombectomy

Aim and hypothesis Using a new study design, we investigate whether next-generation mechanical thrombectomy devices improve clinical outcomes in ischemic stroke patients. We hypothesize that this new methodology is superior to intravenous tissue plasminogen activator therapy alone. Methods and design ERic Acute StrokE Recanalization is an investigator-initiated prospective single-arm, multicenter, controlled, open label study to compare the safety and effectiveness of a new recanalization device and distal access catheter in acute ischemic stroke patients with symptoms attributable to acute ischemic stroke and vessel occlusion of the internal cerebral artery or middle cerebral artery. Study outcome The primary effectiveness endpoint is the volume of saved tissue. Volume of saved tissue is defined as difference of the actual infarct volume and the brain volume that is predicted to develop infarction by using an optimized high-level machine learning model that is trained on data from a historical cohort treated with IV tissue plasminogen activator. Sample size estimates Based on own preliminary data, 45 patients fulfilling all inclusion criteria need to complete the study to show an efficacy >38% with a power of 80% and a one-sided alpha error risk of 0.05 (based on a one sample t-test). Discussion ERic Acute StrokE Recanalization is the first prospective study in interventional stroke therapy to use predictive analytics as primary and secondary endpoint. Such trial design cannot replace randomized controlled trials with clinical endpoints. However, ERic Acute StrokE Recanalization could serve as an exemplary trial design for evaluating nonpivotal neurovascular interventions.

Anisotropic cerebral vascular architecture causes orientation dependency in cerebral blood flow and volume measured with dynamic susceptibility contrast magnetic resonance imaging

Measurements of cerebral perfusion using dynamic susceptibility contrast magnetic resonance imaging rely on the assumption of isotropic vascular architecture. However, a considerable fraction of vessels runs in parallel with white matter tracts. Here, we investigate the effects of tissue orientation on dynamic susceptibility contrast magnetic resonance imaging. Tissue orientation was measured using diffusion tensor imaging and dynamic susceptibility contrast was performed with gradient echo planar imaging. Perfusion parameters and the raw dynamic susceptibility contrast signals were correlated with tissue orientation. Additionally, numerical simulations were performed for a range of vascular volumes of both the isotropic vascular bed and anisotropic vessel components, as well as for a range of contrast agent concentrations. The effect of the contrast agent was much larger in white matter tissue perpendicular to the main magnetic field compared to white matter parallel to the main magnetic field. In addition, cerebral blood flow and cerebral blood volume were affected in the same way with angle-dependent variations of up to 130%. Mean transit time and time to maximum of the residual curve exhibited weak orientation dependency of 10%. Numerical simulations agreed with the measured data, showing that one-third of the white matter vascular volume is comprised of vessels running in parallel with the fibre tracts.

DCE-MRI blood–brain barrier assessment in acute ischemic stroke

Objective:

To quantitatively evaluate blood–brain barrier changes in ischemic stroke patients using dynamic contrast-enhanced (DCE) MRI.

Methods:

We examined 54 stroke patients (clinicaltrials.govNCT00715533, NCT02077582) in a 3T MRI scanner within 48 hours after symptom onset. Twenty-eight patients had a follow-up examination on day 5–7. DCE T1 mapping and Patlak analysis were employed to assess BBB permeability changes.

Results:

Median stroke Ktrans values (0.7 × 10−3 min−1 [interquartile range (IQR) 0.4–1.8] × 10−3 min−1) were more than 3-fold higher compared to median mirror Ktrans values (0.2 × 10−3 min−1, IQR 0.1–0.7 × 10−3 min−1, p < 0.001) and further increased at follow-up (n = 28, 2.3 × 10−3 min−1, IQR 0.8–4.6 × 10−3 min−1, p < 0.001). By contrast, mirror Ktrans values decreased over time with a clear interaction of timepoint and stroke/mirror side (p < 0.001). Median stroke Ktrans values were 2.5 times lower than in hemorrhagic transformed regions (0.7 vs 1.8 × 10−3 min−1; p = 0.055). There was no association between stroke Ktrans values and the delay from symptom onset to baseline examination, age, and presence of hyperintense acute reperfusion marker.

Conclusion:

BBB in acute stroke patients can be successfully assessed quantitatively. The decrease of BBB permeability in unaffected regions at follow-up may be an indicator of global BBB leakage even in vessel territories remote from the index infarct.

Hypointense Vessels Detected by Susceptibility-Weighted Imaging Identifies Tissue at Risk of Infarction in Anterior Circulation Stroke

BACKGROUND AND PURPOSE

The diagnostic value of susceptibility-weighted magnetic resonance imaging of acute stroke patients has shown potential as a surrogate marker of impaired hemodynamics. We investigate the value of asymmetrical hypointense cerebral vessels (HV) for the identification of vessel status and tissue at risk of infarction (TaR).

METHODS

Symmetry of HV was visually rated on SWI data from a well-defined population of acute anterior circulation stroke with onset <24 hours. MRI perfusion data was analyzed and volumes of tissue at risk segmented using a delay threshold of Tmax> 6 seconds. Status of the extra- and intracranial arteries was assessed by ultrasound and MR angiography.

RESULTS

35 patients were included (12 women; median age 69 years, IQR 61-77; median NIHSS at admission 10, IQR 6-20). Asymmetrically distributed HV were detected at the stroke hemisphere in 25 patients (71%). Of those, 12 patients displayed occlusion of the middle cerebral artery, whereas occlusion of the extracranial ICA was detected in 6 patients. TaR was larger, yet not significantly different in patients with asymmetrically HV (mean volume 38.9 ml, SD 52.9 ml) compared to patients showing symmetrical HV (4.2 ml; SD 10.7 ml, p-value 0.081). Significant differences where, however, found after excluding patients with extracranial ICA occlusions (42.9 ml; SD 50.4 ml vs. 4.2 ml, SD 10.8 ml, p-value 0.025).

CONCLUSION

Visual analysis of HV in SWI identifies tissue at risk in patients with anterior circulation stroke. Potentially pre-existing extracranial ICA occlusions leading to prominent HV have to be considered as a confounding factor.

Gray Matter Growth Is Accompanied by Increasing Blood Flow and Decreasing Apparent Diffusion Coefficient during Childhood

BACKGROUND AND PURPOSE

Normal values of gray matter volume, cerebral blood flow, and water diffusion have not been established for healthy children. We sought to determine reference values for age-dependent changes of these parameters in healthy children.

MATERIALS AND METHODS

We retrospectively reviewed MR imaging data from 100 healthy children. Using an atlas-based approach, age-related normal values for regional CBF, apparent diffusion coefficient, and volume were determined for the cerebral cortex, hippocampus, thalamus, caudate, putamen, globus pallidus, amygdala, and nucleus accumbens.

RESULTS

All gray matter structures grew rapidly before the age of 10 years and then plateaued or slightly declined thereafter. The ADC of all structures decreased with age, with the most rapid changes occurring prior to the age of 5 years. With the exception of the globus pallidus, CBF increased rather linearly with age.

CONCLUSIONS

Normal brain gray matter is characterized by rapid early volume growth and increasing CBF with concomitantly decreasing ADC. The extracted reference data that combine CBF and ADC parameters during brain growth may provide a useful resource when assessing pathologic changes in children.

Voxel-based lesion-symptom mapping of stroke lesions underlying somatosensory deficits

The aim of this study was to investigate the relationship between stroke lesion location and the resulting somatosensory deficit. We studied exteroceptive and proprioceptive somatosensory symptoms and stroke lesions in 38 patients with first-ever acute stroke. The Erasmus modified Nottingham Sensory Assessment was used to clinically evaluate somatosensory functioning in the arm and hand within the first week after stroke onset. Additionally, more objective measures such as the perceptual threshold of touch and somatosensory evoked potentials were recorded. Non-parametric voxel-based lesion-symptom mapping was performed to investigate lesion contribution to different somatosensory deficits in the upper limb. Additionally, structural connectivity of brain areas that demonstrated the strongest association with somatosensory symptoms was determined, using probabilistic fiber tracking based on diffusion tensor imaging data from a healthy age-matched sample. Voxels with a significant association to somatosensory deficits were clustered in two core brain regions: the central parietal white matter, also referred to as the sensory component of the superior thalamic radiation, and the parietal operculum close to the insular cortex, representing the secondary somatosensory cortex. Our objective recordings confirmed findings from clinical assessments. Probabilistic tracking connected the first region to thalamus, internal capsule, brain stem, postcentral gyrus, cerebellum, and frontal pathways, while the second region demonstrated structural connections to thalamus, insular and primary somatosensory cortex. This study reveals that stroke lesions in the sensory fibers of the superior thalamocortical radiation and the parietal operculum are significantly associated with multiple exteroceptive and proprioceptive deficits in the arm and hand.

Structural plasticity of remote cortical brain regions is determined by connectivity to the primary lesion in subcortical stroke

Cortical atrophy as demonstrated by measurement of cortical thickness (CT) is a hallmark of various neurodegenerative diseases. In the wake of an acute ischemic stroke, brain architecture undergoes dynamic changes that can be tracked by structural and functional magnetic resonance imaging studies as soon as 3 months after stroke. In this study, we measured changes of CT in cortical areas connected to subcortical stroke lesions in 12 patients with upper extremity paresis combining white-matter tractography and semi-automatic measurement of CT using the Freesurfer software. Three months after stroke, a significant decrease in CT of -2.6% (median, upper/lower boundary of 95% confidence interval -4.1%/-1.1%) was detected in areas connected to ischemic lesions, whereas CT in unconnected cortical areas remained largely unchanged. A cluster of significant cortical thinning was detected in the superior frontal gyrus of the stroke hemisphere using a surface-based general linear model correcting for multiple comparisons. There was no significant correlation of changes in CT with clinical outcome parameters. Our results show a specific impact of subcortical lesions on distant, yet connected cortical areas explainable by secondary neuro-axonal degeneration of distant areas.