Background and Purpose- Hyperacute assessment and management of patients with stroke, termed code stroke, is a time-sensitive and high-stakes clinical scenario. In the context of the current ...coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-CoV-2 virus, the ability to deliver timely and efficacious care must be balanced with the risk of infectious exposure to the clinical team. Furthermore, rapid and effective stroke care remains paramount to achieve maximal functional recovery for those needing admission and to triage care appropriately for those who may be presenting with neurological symptoms but have an alternative diagnosis. Methods- Available resources, COVID-19-specific infection prevention and control recommendations, and expert consensus were used to identify clinical screening criteria for patients and provide the required nuanced considerations for the healthcare team, thereby modifying the conventional code stroke processes to achieve a protected designation. Results- A protected code stroke algorithm was developed. Features specific to prenotification and clinical status of the patient were used to define precode screening. These include primary infectious symptoms, clinical, and examination features. A focused framework was then developed with regard to a protected code stroke. We outline the specifics of personal protective equipment use and considerations thereof including aspects of crisis resource management impacting team role designation and human performance factors during a protected code stroke. Conclusions- We introduce the concept of a protected code stroke during a pandemic, as in the case of COVID-19, and provide a framework for key considerations including screening, personal protective equipment, and crisis resource management. These considerations and suggested algorithms can be utilized and adapted for local practice.
Stroke lesion volume is a key radiologic measurement in assessing prognosis of acute ischemic stroke (AIS) patients. The aim of this paper is to develop an automated segmentation method for ...accurately segmenting follow-up ischemic and hemorrhagic lesion from multislice non-contrast CT (NCCT) volumes of AIS patients. This paper proposes a 2D dense multi-path contextual generative adversarial network (MPC-GAN) where a dense multi-path 2D U-Net is utilized as the generator and a discriminator network is applied to regularize the generator. Contextual information (i.e. bilateral intensity difference, distance map and lesion location probability) are input into the generator and discriminator. The proposed method is validated separately on follow-up NCCT volumes of 60 patients with ischemic infarcts and NCCT volumes of 70 patients with hemorrhages. Quantitative results demonstrated that the proposed MPC-GAN method obtained a Dice coefficient (DC) of 70.6% for ischemic infarct segmentation and a DC of 76.5% for hemorrhage segmentation compared with manual segmented lesions, outperforming several benchmark methods. Additional volumetric analyses demonstrated that the MPC-GAN segmented lesion volume correlated well with manual measurements (Pearson correlation coefficients were 0.926 and 0.927 for ischemic infarcts and hemorrhages, respectively). The proposed MPC-GAN method can accurately segment ischemic infarcts and hemorrhages from NCCT volumes of AIS patients.
New imaging techniques have advanced our ability to capture thrombus characteristics and burden in real time. An improved understanding of recanalization rates with thrombolysis and endovascular ...thrombectomy based on thrombus characteristics has spurred interest in new therapies for acute stroke.
This article reviews the biochemical, structural, and imaging characteristics of intracranial thrombi in acute ischemic stroke; the relationship between thrombus composition and response to lytic and endovascular therapies; and current and future directions for improving outcomes in patients with acute stroke based on thrombus characteristics.
Thrombus composition, size, location, and timing from stroke onset correlate with imaging findings in acute ischemic stroke and are associated with clinical outcome. Further research across multiple domains could assist in better applying our knowledge of thrombi to patient selection and individualization of acute therapies.
Revascularization after endovascular therapy for acute ischemic stroke is measured by the Thrombolysis In Cerebral Infarction (TICI) scale, yet variability exists in scale definitions. We examined ...the degree of reperfusion with the expanded TICI (eTICI) scale and association with outcomes in the HERMES collaboration of recent endovascular trials.
The HERMES Imaging Core, blind to all other data, evaluated angiography after endovascular therapy in HERMES. A battery of TICI scores (mTICI, TICI, TICI2C) was used to define reperfusion of the initial target occlusion defined by non-invasive imaging and conventional angiography.
Angiography of 801 subjects was available, including 797 defined by non-invasive imaging (154 internal carotid artery (ICA), 583 M1, 60 M2) and 748 by conventional angiography (195 ICA, 459 M1, 94 M2). Among 729 subjects in whom the reperfusion grade could be established, using eTICI (3=100%, 2C=90-99%, 2b67=67-89%, 2b50=50-66%) of the conventional angiography target occlusion, there were 63 eTICI 3 (9%), 166 eTICI 2c (23%), 218 eTICI 2b67 (30%), 103 eTICI 2b50 (14%), 100 eTICI 2a (14%), 19 eTICI 1 (3%), and 60 eTICI 0 (8%). Modified Rankin Scale shift analyses from baseline to 90 days showed that increasing TICI grades were linked with better outcomes, with significant distinctions between TICI 0/1 versus 2a (p=0.028), 2a versus 2b50 (p=0.017), and 2b50 versus 2b67 (p=0.014).
The benefit of endovascular therapy in HERMES was strongly associated with increasing degrees of reperfusion defined by eTICI. The eTICI metric identified meaningful distinctions in clinical outcomes and may be used in future studies and routine practice.
Studies of endovascular treatment for acute ischemic stroke have identified general anesthesia as a predictor for poor outcome in comparison with local anesthesia/sedation. This retrospective study ...attempts to identify modifiable factors associated with poor outcome, while adjusting for baseline stroke severity, in patients receiving general anesthesia.
We reviewed charts of 129 patients treated between January 2003 and September 2009. The primary outcome was the modified Rankin Score of 0-2 for 3 months poststroke. Predictors of neurologic outcome included baseline National Institutes of Health Stroke Scale score, blood glucose concentration, and age. Additional risk factors evaluated were prolonged stroke onset-treatment interval and systolic blood pressure less than 140 mmHg. Choice of local anesthesia or general anesthesia was recorded.
The study group was 96 out of 129 patients for whom modified Rankin Scale scores were available; 48 patients received general anesthesia and 48 local anesthesia. The proportion of patients with "good" outcomes were 15% and 60% in the general anesthesia group and local anesthesia group, respectively (P < 0.001). Lowest systolic blood pressure and general anesthesia were correlated (r = -0.7, P < 0.001). Independent predictors for good neurologic outcome were local anesthesia, systolic blood pressure greater than 140 mmHg, and low baseline stroke scores.
Adjusted for stroke severity, patients who received general anesthesia for treatment are less likely to have a good outcome than those managed with local anesthesia. This may be due to preintervention risk not included in the stroke severity measures. Hypotension, more frequent in the general anesthesia patients, may also contribute.
To describe the use of an imaging selection tool, multiphase computed tomographic (CT) angiography, in patients with acute ischemic stroke (AIS) and to demonstrate its interrater reliability and ...ability to help determine clinical outcome.
The local ethics board approved this study. Data are from the pilot phase of PRoveIT, a prospective observational study analyzing utility of multimodal imaging in the triage of patients with AIS. Patients underwent baseline unenhanced CT, single-phase CT angiography of the head and neck, multiphase CT angiography, and perfusion CT. Multiphase CT angiography generates time-resolved images of pial arteries. Pial arterial filling was scored on a six-point ordinal scale, and interrater reliability was tested. Clinical outcomes included a 50% or greater decrease in National Institutes of Health Stroke Scale (NIHSS) over 24 hours and 90-day modified Rankin Scale (mRS) score of 0-2. The ability to predict clinical outcomes was compared between single-phase CT angiography, multiphase CT angiography, and perfusion CT by using receiver operating curve analysis, Akaike information criterion (AIC), and Bayesian information criterion (BIC).
A total of 147 patients were included. Interrater reliability for multiphase CT angiography is excellent (n = 30, κ = 0.81, P < .001). At receiver operating characteristic curve analysis, the ability to predict clinical outcome is modest (C statistic = 0.56, 95% confidence interval CI: 0.52, 0.63 for ≥50% decrease in NIHSS over 24 hours; C statistic = 0.6, 95% CI: 0.53, 0.68 for 90-day mRS score of 0-2) but better than that of models using single-phase CT angiography and perfusion CT (P < .05 overall). With AIC and BIC, models that use multiphase CT angiography are better than models that use single-phase CT angiography and perfusion CT for a decrease of 50% or more in NIHSS over 24 hours (AIC = 166, BIC = 171.7; values were lowest for multiphase CT angiography) and a 90-day mRS score of 0-2 (AIC = 132.1, BIC = 137.4; values were lowest for multiphase CT angiography).
Multiphase CT angiography is a reliable tool for imaging selection in patients with AIS.
•A unified network to segment early infarct and score ASPECTS.•A triplet CNN with three encoders of original NCCT, mirrored NCCT and atlas.•Comparison disparity block to abstract and enhance image ...contexts between encoders.•Multi-level attention gate module to fuse the features at different levels.•An auxiliary network to score ASPECTS using multi-task learning strategy.
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Detecting early infarct (EI) plays an essential role in patient selection for reperfusion therapy in the management of acute ischemic stroke (AIS). EI volume at acute or hyper-acute stage can be measured using advanced pre-treatment imaging, such as MRI and CT perfusion. In this study, a novel multi-task learning approach, EIS-Net, is proposed to segment EI and score Alberta Stroke Program Early CT Score (ASPECTS) simultaneously on baseline non-contrast CT (NCCT) scans of AIS patients. The EIS-Net comprises of a 3D triplet convolutional neural network (T-CNN) for EI segmentation and a multi-region classification network for ASPECTS scoring. T-CNN has triple encoders with original NCCT, mirrored NCCT, and atlas as inputs, as well as one decoder. A comparison disparity block (CDB) is designed to extract and enhance image contexts. In the decoder, a multi-level attention gate module (MAGM) is developed to recalibrate the features of the decoder for both segmentation and classification tasks. Evaluations using a high-quality dataset comprising of baseline NCCT and concomitant diffusion weighted MRI (DWI) as reference standard of 260 patients with AIS show that the proposed EIS-Net can accurately segment EI. The EIS-Net segmented EI volume strongly correlates with EI volume on DWI (r=0.919), and the mean difference between the two volumes is 8.5 mL. For ASPECTS scoring, the proposed EIS-Net achieves an intraclass correlation coefficient of 0.78 for total 10-point ASPECTS and a kappa of 0.75 for dichotomized ASPECTS (≤ 4 vs. >4). Both EI segmentation and ASPECTS scoring tasks achieve state-of-the-art performances.
Purpose
Cerebral infarct volume observed in follow‐up noncontrast computed tomography (NCCT) scans of acute ischemic stroke (AIS) patients is as an important radiologic outcome measure of the ...effectiveness of endovascular therapy (EVT). In this paper, our aim is to propose a semiautomated segmentation approach that can accurately measure ischemic infarct volume from NCCT images of AIS patients.
Methods
A novel cascaded random forest (RF) learning is first employed to classify each voxel into normal or ischemic voxel, leading to an infarct probability map. Four types of features: intensity, statistical information in local region, symmetric difference compared to the contralateral side, and spatial probability of infarct occurrence generated by the STAPLE method, are extracted. These features are input into RF to train a first‐stage classifier. The coarse segmentation results generated by the first‐stage classifier are then used to train a fine second‐stage classifier with fivefold cross validation. The RF estimated infarct probability map obtained in the second‐stage testing as well as user input high‐level knowledge are then incorporated into a convex optimization function to obtain final segmentation. One hundred AIS patients were collected in this study, of which 70 patient images were used for evaluation while the remaining 30 patient images were used for RF training.
Results
Quantitative results show that the proposed approach is capable of yielding a dice coefficient (DC) of 79.42%, significantly outperforming some state‐of‐the‐art automated segmentation methods, such as the RF‐based methods and convolutional neural network (CNN)‐based segmentation method, U‐net. The infarct volume obtained by the proposed method is strongly correlated with the manually segmented volume. In addition, interobserver variability analysis initialized by two observers suggests low user dependency.
Conclusions
Our proposed semiautomated segmentation method can accurately segment infarct from NCCT of AIS patients.
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