Anterior cerebral artery (ACA) stroke is less frequent when compared with middle cerebral artery (MCA) occlusion, and consequently, mechanical thrombectomies, perfusion studies, pial collateral system or clinical consequences based on the topography of the lesion are less known. With the aim of evaluating the role of the circle of Willis (CoW) and leptomeningeal anastomoses (LA) in modifying regional variation in infarct topography following occlusion of the anterior cerebral artery and its branches, Thirugnanachandran and colleagues employed voxel-based imaging in conjunction with computer model of cerebral circulation to understand the temporal and spatial evolution of the topography of ACA stroke following vessel occlusion. The experiments included occlusion of successive branches of the anterior cerebral artery while the configurations of the CoW were varied.
Time is brain; however, there are patients for whom that time runs faster. Penumbra brain tissue, due to large vessel occlusion, tends to progress to ischemia in the absence of intracranial reperfusion. However, there are a number of conditions that cause a faster progression (rapid progressors) or not, even in those who will receive endovascular treatment. To identify acute ischemic stroke patients with rapid infarct growth, Ospel and colleagues performed a post hoc analysis of the ESCAPE trial (Endovascular Treatment for Small Core and Proximal Occlusion Ischemic Stroke) in order to investigate baseline clinical and imaging characteristics of fast progressors stroke patients.
The authors included control arm patients if they had follow-up imaging at 2-8 hours without substantial recanalization, and if their baseline Alberta Stroke Program Early CT Score was ≥9. Fast infarct progression was defined as Alberta Stroke Program Early CT Score decay ≥3 points from baseline to 2- to 8-hour follow-up imaging.
Computed tomography perfusion (CTP) has become widely accepted as the imaging modality for the estimation of the infarct core and subsequent selection for endovascular treatment (EVT) in ischemic stroke due to large vessel occlusion (LVO), especially in the late time window. The radiological correlate for the core in CTP is usually the volume of tissue with a (compared with the contralateral hemisphere) reduction in cerebral blood flow (CBF) <30%. Overestimation of the core in CTP is thought to be time-dependent and may be a concern, especially with rapid imaging after symptom onset and fast reperfusion after imaging.
García-Tornel et al. addressed the question of the influence of time and collateral status on ischemic core overestimation. They retrospectively evaluated patients with anterior circulation LVO strokes with successful reperfusion after EVT. The core was considered to be the tissue with CBF <30% in CTP. Collateral status was assessed by the hypoperfusion intensity ratio (time to maximum of tissue residue function >6 seconds/time to maximum of tissue residue function >10 seconds). The reference for the final infarct volume was the non-contrast CT after 24 to 48 hours.
Ohara et al. conducted a post hoc analysis of data collected in the INTERRSeCT study (Identifying New Approaches to Optimize Thrombus Characterization for Predicting Early Recanalization and Reperfusion With IV Alteplase and Other Treatments Using Serial CT Angiography) to study thrombus changes in ICA or MCA occlusions following IV t-PA and whether this affected clinical outcomes. The 427 INTERRSeCT study patients underwent baseline CTA as well as repeat CTA or conventional angiogram following IV t-PA administration. The investigators compared the proximal position of the clot on baseline and repeat imaging, and if it migrated, they graded the degree of movement on a 0-3 scale with higher grades indicating more distal movement. If there was no change in proximal position, they assessed thrombus fragmentation determined by the presence of a new thrombus in a distal artery. A 90-day modified Rankin Scale score ≤2 was considered a good outcome.
Atrial fibrillation (AF) is an established risk factor for thromboembolic events, including ischemic stroke. Therefore, identification of patients with this arrhythmia is important to facilitate the implementation of stroke prevention therapy using oral anticoagulation. Nonetheless, as a significant proportion of patients with AF remain asymptomatic, it remains largely under-diagnosed in the general population. Given that the source of emboli in the majority of AF-related strokes originates from the left atrial appendage (LAA), inclusion of this structure in imaging protocols may have a role in aiding the diagnosis of AF.
In a recent retrospective study of consecutive patients with ischemic stroke or transient ischemic attack, Senadeera and colleagues investigated the prevalence of computed tomography angiography (CTA)-detected LAA thrombus during hyperacute stroke imaging and evaluated the association between LAA thrombus and AF. The imaging protocol consisted of non-contrast CT, followed by CT perfusion and CTA from aortic arch to vertex. Two experienced physicians and pre-defined measures were used to assess for LAA thrombus on these scans.
Limitations in our knowledge of cerebral amyloid angiopathy (CAA) persist due to relatively small study sample sizes and a requirement for pathological specimens to confirm a diagnosis. The Edinburgh criteria is the most recent decision instrument developed to assist in the pre-mortem diagnosis of this disorder. In their logistic regression model, Rodrigues et al. utilized genetic factors (APOE ε4 genotype) and computed tomography (CT) findings (finger-like projections and subarachnoid hemorrhage) to attain a high degree of sensitivity and specificity.1 However, their cohort relied on autopsy specimens for confirmation of moderate-to-severe CAA.
To this end, van Etten et al. recruited patients with Dutch-type CAA (D-CAA) for analysis. D-CAA, a hereditary variant of CAA, causes similar radiographic features as CAA with an accelerated clinical course, and most importantly, does not require a tissue-based confirmation. Using patients with D-CAA, the investigators evaluated the aforementioned CT variables in this validation study.
Collateral status has been related to impact on infarct size after ischemic stroke (IS) recanalization. However, the smaller final infarct size is not always related to a good clinical situation, which seems to be related to the eloquence of the affected area, rather than the volume of the ischemic area itself. The present scientific work aims to evaluate the relation between collateral status and reperfusion degree on final infarct distribution and clinical outcome after IS due to large vessel occlusion (LVO).
Al-Dasuqi and colleagues performed a single center retrospective analysis of all patients with LVO who were treated with endovascular treatment between 2013-2019. The authors collected clinical, demographic and radiological data. They applied a multivariate voxel-wise general linear model to correlate the distribution of final infarction with collateral status and degree of reperfusion. Early favorable outcome was defined as a discharge modified Rankin Scale score ≤2.
International Stroke Conference 2021 March 17–19, 2021 Session: Advanced Imaging in Intracranial Atherosclerotic Disease: Misnomer or Game-Changer? (24, OnDemand)
A common stroke mechanism accounting for 20-30% of the ischemic strokes worldwide, intracranial atherosclerotic disease (ICAD) is a diagnosis that primarily relies on visualization of luminal narrowing on CTA/MRA. This session expanded upon cutting-edge advances in imaging of ICAD, specifically in revealing plaque morphology, collateral status, and cerebrovascular reserve distal to the stenosis of the culprit lesion.
Before the panel delved into discussion of the advanced imaging, Dr. Achala Vagal provided a comprehensive overview highlighting the limitations of the current conventional lumen-based imaging: failure to detect on-stenosing plaque, compensatory remodeling, and the status of distal flow and collateralization.
Endovascular treatment (EVT) is a fundamental component of acute therapy for ischemic stroke due to large vessel occlusion. Although the clinical decision for or against EVT is individual and multifactorial, it is mainly based on radiological parameters, especially the ischemic core’s visualization on neuroimaging. In their review, Goyal et al. address the practical difficulties in attributing image-defined core significance to EVT.
Clinically, the term “core” is commonly used as a synonym for infarcted brain tissue that can no longer be saved. However, the concept of a homogeneous infarction core is increasingly being challenged. The reality seems to be much more complicated, since the susceptibility of different cell and tissue types is variable, and, depending on the speed of reperfusion, there may be no, partial, or complete necrosis of the core area.
A conversation with Dr. Rajat Dhar, MD, Associate Professor of Neurology and Neuro-critical care, Washington University School of Medicine, St. Louis, MO.
Interviewed by Dr. Saurav Das, MD, Fellow in Vascular Neurology, Washington University School of Medicine, St. Louis, MO.
Dr. Das:Dr. Dhar, on behalf of the Blogging Stroke team, we welcome you to this author interview. I read with great interest your paper pertaining to the automated estimation of selective sulcal volume (SSV) to quantify global cerebral edema (GCE) from early brain injury (EBI) in aneurysmal subarachnoid hemorrhage (aSAH). This is an important paper as our understanding of clinical outcomes following aSAH is shifting from vasospasm induced delayed cerebral ischemia (DCI) towards GCE from EBI. Also, we currently do not have the tools to measure GCE accurately.
This research uses a “deep learning-based approach” for the analysis of serial CT scans to measure SSV. Many of our readers may not be familiar with the use of artificial intelligence (AI) in image analysis. I will begin by requesting you to explain what deep learning is.
Dr. Dhar: Applications of artificial intelligence, specifically machine learning, to the realm of biomedical image analysis have been growing exponentially over the past few years. AI is well-suited to image analysis because, at its core, machine learning seeks to find patterns in data, and images are just patterns of intensity and location data. Machine learning algorithms can be trained to learn from labeled data. For example, to determine what regions of a scan represent blood vs. brain vs. CSF is called a segmentation task. We can use machine learning to perform a segmentation task on new imaging data. AI algorithms can perform image analysis in a fast and reproducible way, eliminating the need for time-intensive human input. They can measure volumes of similar brain structures over serial time points more objectively and accurately than one or more humans may be able to.
