BACKGROUND:Pediatric stroke is a distinct clinical entity as compared with that in adults due to its unique and diverse set of etiologies. Furthermore, the role and application of diagnostic imaging has specific constraints and considerations. The intention of this article is to review these concepts in a thorough manner to offer a pediatric stroke imaging framework that providers can employ when taking care of these patients. METHODS:A comprehensive primary and secondary literature review was performed with specific attention to the common causes of pediatric stroke, appropriate use of neuroimaging, specific imaging findings, and developing techniques which may improve our ability to accurately diagnose these patients. RESULTS:Findings from this literature review were synthesized and summarized in order to thoroughly review the aforementioned concepts and outline the current consensus-based approach to diagnostic imaging in pediatric stroke. Furthermore, imaging findings drawn from patients seen in our institution are demonstrated to familiarize readers with pediatric stroke neuroimaging. CONCLUSIONS:The challenges posed by pediatric stroke can be mitigated, in part, by the thoughtful application of diagnostic imaging, with the ultimate hope of improving outcomes for these vulnerable patients.

Aneurysmal subarachnoid hemorrhage (aSAH) results in significant mortality and disability. Delayed Cerebral Ischemia (DCI) is the leading cause of poor outcome after aSAH among those who survive the initial hemorrhage event. Significant effort is expended to identify patients at risk of DCI, but existing tests, such as trans-cranial Doppler, suffer from relatively poor sensitivity. Tests to identify patients with DCI prospectively are of high interest.

Background

Rapid and accurate triage of acute ischemic stroke (AIS) is essential for early revascularization and improved patient outcomes. Response to acute reperfusion therapies varies significantly based on patient-specific cerebrovascular anatomy that governs cerebral blood flow. We present an end-to-end machine learning approach for automatic stroke triage.

Methods

Employing a validated convolutional neural network (CNN) segmentation model for image processing, we extract each patient's cerebrovasculature and its morphological features from baseline non-invasive angiography scans. These features are used to detect occlusion's presence and the site automatically, and for the first time, to estimate collateral circulation without manual intervention. We then use the extracted cerebrovascular features along with commonly used clinical and imaging parameters to predict the 90 days functional outcome for each patient.

Results

The CNN model achieved a segmentation accuracy of 94% based on the Dice similarity coefficient (DSC). The automatic stroke detection algorithm had a sensitivity and specificity of 92% and 94%, respectively. The models for occlusion site detection and automatic collateral grading reached 96% and 87.2% accuracy, respectively. Incorporating the automatically extracted cerebrovascular features significantly improved the 90 days outcome prediction accuracy from 0.63 to 0.83.

Conclusion

The fast, automatic, and comprehensive model presented here can improve stroke diagnosis, aid collateral assessment, and enhance prognostication for treatment decisions, using cerebrovascular morphology.

Atherosclerosis at the carotid bifurcation is a major risk factor for stroke. As mechanical forces may impact lesion stability, finite element studies have been conducted on models of diseased vessels to elucidate the effects of lesion characteristics on the stresses within plaque materials. It is hoped that patient-specific biomechanical analyses may serve clinically to assess the rupture potential for any particular lesion, allowing better stratification of patients into the most appropriate treatments. Due to a sparsity of in vivo plaque rupture data, the relationship between various mechanical descriptors such as stresses or strains and rupture vulnerability is incompletely known, and the patient-specific utility of biomechanical analyses is unclear. In this article, we present a comparison between carotid atheroma rupture observed in vivo and the plaque stress distribution from fluid–structure interaction analysis based on pre-rupture medical imaging. The effects of image resolution are explored and the calculated stress fields are shown to vary by as much as 50% with sub-pixel geometric uncertainty. Within these bounds, we find a region of pronounced elevation in stress within the fibrous plaque layer of the lesion with a location and extent corresponding to that of the observed site of plaque rupture.

Introduction

Left atrial (LA) tissue characteristics may play an important role in atrial fibrillation (AF) induction and perpetuation. Although frequently used in clinical practice, computed tomography (CT) has not been employed to describe differences in LA wall properties between AF patients and controls. We sought to noninvasively characterize AF-associated differences in LA tissue using CT.

Methods

CT images of the LA were obtained in 98 consecutive patients undergoing AF ablation and in 89 controls. A custom software algorithm was used to measure wall thickness and density in four prespecified regions of the LA.

Results

On average, LA walls were thinner (-15.5%, 95% confidence interval [CI] -23.2 to -7.8%, P < 0.001) and demonstrated significantly lower density (-19.7 Hounsfield Units [HU], 95% CI -27.0 to -12.5 HU, P < 0.001) in AF patients compared to controls. In linear mixed models adjusting for demographics, clinical variables, and other CT measurements, the average LA, interatrial septum, LA appendage, and anterior walls remained significantly thinner in AF patients. After adjusting for the same potential confounders, history of AF was associated with reduced density in the LA anterior wall and increased density below the right inferior pulmonary vein and in the LA appendage.

Conclusion

Application of an automated measurement algorithm to CT imaging of the atrium identified significant thinning of the LA wall and regional alterations in tissue density in patients with a history of AF. These findings suggest differences in LA tissue composition can be noninvasively identified and quantified using CT.

The purpose of this study is to compare the variability of PCT results obtained by automatic selection of the arterial input function (AIF), venous output function (VOF) and symmetry axis versus manual selection. Imaging data from 30 PCT studies obtained as part of standard clinical stroke care at our institution in patients with suspected acute hemispheric ischemic stroke were retrospectively reviewed. Two observers performed the post-processing of 30 CTP datasets. Each observer processed the data twice, the first time employing manual selection of AIF, VOF and symmetry axis, and a second time using automated selection of these same parameters, with the user being allowed to adjust them whenever deemed appropriate. The volumes of infarct core and of total perfusion defect were recorded. The cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT) and blood–brain barrier permeability (BBBP) values in standardized regions of interest were recorded. Interobserver variability was quantified using the Bland and Altman's approach. Automated post-processing yielded lower coefficients of variation for the volume of the infarct core and the volume of the total perfusion defect (15.7% and 5.8%, respectively) compared to manual post-processing (31.0% and 12.2%, respectively). Automated post-processing yielded lower coefficients of variation for PCT values (11.3% for CBV, 9.7% for CBF, and 9.5% for MTT) compared to manual post-processing (23.7% for CBV, 32.8% for CBF, and 16.7% for MTT). Automated post-processing of PCT data improves interobserver agreement in measurements of CBV, CBF and MTT, as well as volume of infarct core and penumbra.

Neuroimaging plays a critical role in the setting in traumatic brain injury (TBI). Diffusion tensor imaging (DTI) is an advanced magnetic resonance imaging technique that is capable of providing rich information on the brain's neuroanatomic connectome. The purpose of this article is to systematically review the role of DTI and advanced diffusion techniques in the setting of TBI, including diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging, diffusion spectrum imaging, and q-ball imaging. We discuss clinical applications of DTI and review the DTI literature as it pertains to TBI. Despite the continued advancements in DTI and related diffusion techniques over the past 20 years, DTI techniques are sensitive for TBI at the group level only and there is insufficient evidence that DTI plays a role at the individual level. We conclude by discussing future directions in DTI research in TBI including the role of machine learning in the pattern classification of TBI.

BACKGROUND AND PURPOSE: Published cohorts of children with arterial ischemic stroke (AIS) in the 1990s to early 2000s reported 5-year cumulative recurrence rates approaching 20%. Since then, utilization of antithrombotic agents for secondary stroke prevention in children has increased. We sought to determine rates and predictors of recurrent stroke in the current era. METHODS: The Vascular Effects of Infection in Pediatric Stroke (VIPS) study enrolled 355 children with AIS at 37 international centers from 2009 to 2014 and followed them prospectively for recurrent stroke. Index and recurrent strokes underwent central review and confirmation, as well as central classification of causes of stroke, including arteriopathies. Other predictors were measured via parental interview or chart review. RESULTS: Of the 355 children, 354 survived their acute index stroke, and 308 (87%) were treated with an antithrombotic medication. During a median follow-up of 2.0 years (interquartile range, 1.0-3.0), 40 children had a recurrent AIS, and none had a hemorrhagic stroke. The cumulative stroke recurrence rate was 6.8% (95% confidence interval, 4.6%-10%) at 1 month and 12% (8.5%-15%) at 1 year. The sole predictor of recurrence was the presence of an arteriopathy, which increased the risk of recurrence 5-fold when compared with an idiopathic AIS (hazard ratio, 5.0; 95% confidence interval, 1.8-14). The 1-year recurrence rate was 32% (95% confidence interval, 18%-51%) for moyamoya, 25% (12%-48%) for transient cerebral arteriopathy, and 19% (8.5%-40%) for arterial dissection. CONCLUSIONS: Children with AIS, particularly those with arteriopathy, remain at high risk for recurrent AIS despite increased utilization of antithrombotic agents. Therapies directed at the arteriopathies themselves are needed.