Teaching NeuroImages: Crying thalamus: Manifestation of acute ischemic stroke.
Ontology highlight
ABSTRACT:
SUBMITTER: Obeidat AZ
PROVIDER: S-EPMC5331865 | biostudies-literature | 2017 Feb
REPOSITORIES: biostudies-literature
Publications
Teaching Neuro<i>Images</i>: Crying thalamus: Manifestation of acute ischemic stroke.
Obeidat Ahmed Z AZ Broderick Joseph P JP
Neurology 20170201 9
Similar Datasets
Project description:Stroke is a major threat to life and health in modern society, especially in the aging population. Stroke may cause sudden death or severe sequela-like hemiplegia. Although computed tomography (CT) and magnetic resonance imaging (MRI) are standard diagnosis methods, and artificial intelligence models have been built based on these images, shortage in medical resources and the time and cost of CT/MRI imaging hamper fast detection, thus increasing the severity of stroke. Here, we developed a convolutional neural network model by integrating four networks, Xception, ResNet50, VGG19, and EfficientNetb1, to recognize stroke based on 2D facial images with a cross-validation area under curve (AUC) of 0.91 within the training set of 185 acute ischemic stroke patients and 551 age- and sex-matched controls, and AUC of 0.82 in an independent data set regardless of age and sex. The model computed stroke probability was quantitatively associated with facial features, various clinical parameters of blood clotting indicators and leukocyte counts, and, more importantly, stroke incidence in the near future. Our real-time facial image artificial intelligence model can be used to rapidly screen and prediagnose stroke before CT scanning, thus meeting the urgent need in emergency clinics, potentially translatable to routine monitoring.
| S-EPMC11320352 | biostudies-literature
Project description:Magnetic resonance (MR) imaging (MRI) is commonly used to diagnose, assess and monitor stroke. Accurate and timely segmentation of stroke lesions provides the anatomico-structural information that can aid physicians in predicting prognosis, as well as in decision making and triaging for various rehabilitation strategies. To segment stroke lesions, MR protocols, including diffusion-weighted imaging (DWI) and T2-weighted fluid attenuated inversion recovery (FLAIR) are often utilized. These imaging sequences are usually acquired with different spatial resolutions due to time constraints. Within the same image, voxels may be anisotropic, with reduced resolution along slice direction for diffusion scans in particular. In this study, we evaluate the ability of 2D and 3D U-Net Convolutional Neural Network (CNN) architectures to segment ischemic stroke lesions using single contrast (DWI) and dual contrast images (T2w FLAIR and DWI). The predicted segmentations correlate with post-stroke motor outcome measured by the National Institutes of Health Stroke Scale (NIHSS) and Fugl-Meyer Upper Extremity (FM-UE) index based on the lesion loads overlapping the corticospinal tracts (CST), which is a neural substrate for motor movement and function. Although the four methods performed similarly, the 2D multimodal U-Net achieved the best results with a mean Dice of 0.737 (95% CI: 0.705, 0.769) and a relatively high correlation between the weighted lesion load and the NIHSS scores (both at baseline and at 90 days). A monotonically constrained quintic polynomial regression yielded R2 = 0.784 and 0.875 for weighted lesion load versus baseline and 90-Days NIHSS respectively, and better corrected Akaike information criterion (AICc) scores than those of the linear regression. In addition, using the quintic polynomial regression model to regress the weighted lesion load to the 90-Days FM-UE score results in an R2 of 0.570 with a better AICc score than that of the linear regression. Our results suggest that the multi-contrast information enhanced the accuracy of the segmentation and the prediction accuracy for upper extremity motor outcomes. Expanding the training dataset to include different types of stroke lesions and more data points will help add a temporal longitudinal aspect and increase the accuracy. Furthermore, adding patient-specific data may improve the inference about the relationship between imaging metrics and functional outcomes.
| S-EPMC9949513 | biostudies-literature
Project description: Not available
| S-EPMC10493383 | biostudies-literature
Project description:Stroke remains a leading cause of disability in the United States. Despite recent advances, interventions to reduce damage and enhance recovery after stroke are lacking. P2X4R, a receptor for adenosine triphosphate (ATP), regulates activation of myeloid immune cells (infiltrating monocytes/macrophages and brain-resident microglia) after stroke injury. However, over-stimulation of P2X4Rs due to excessive ATP release from dying or damaged neuronal cells can contribute to ischemic injury. Therefore, we pharmacologically inhibited P2X4R to limit the over-stimulated myeloid cell immune response and improve both acute and chronic stroke recovery. We subjected 8-12-week-old male and female wild type mice to a 60 min right middle cerebral artery occlusion (MCAo) followed by 3 or 30 days of reperfusion. We performed histological, RNA sequencing, behavioral (sensorimotor, anxiety, and depressive), and biochemical (Evans blue dye extravasation, western blot, quantitative PCR, and flow cytometry) analyses to determine the acute (3 days after MCAo) and chronic (30 days after MCAo) effects of P2X4R antagonist 5-BDBD (1 mg/kg P.O. daily x 3 days post 4 h of MCAo) treatment. 5-BDBD treatment significantly (p < .05) reduced infarct volume, neurological deficit (ND) score, levels of cytokine interleukin-1 beta (IL-1β) and blood brain barrier (BBB) permeability in the 3-day group. Chronically, 5-BDBD treatment also conferred progressive recovery (p < .05) of motor balance and coordination using a rotarod test, as well as reduced anxiety-like behavior over 30 days. Interestingly, depressive-type behavior was not observed in mice treated with 5-BDBD for 3 days. In addition, flow cytometric analysis revealed that 5-BDBD treatment decreased the total number of infiltrated leukocytes, and among those infiltrated leukocytes, pro-inflammatory cells of myeloid origin were specifically reduced. 5-BDBD treatment reduced the cell surface expression of P2X4R in flow cytometry-sorted monocytes and microglia without reducing the total P2X4R level in brain tissue. In summary, acute P2X4R inhibition protects against ischemic injury at both acute and chronic time-points after stroke. Reduced numbers of infiltrating pro-inflammatory myeloid cells, decreased surface P2X4R expression, and reduced BBB disruption are likely its mechanism of neuroprotection and neuro-rehabilitation.
| S-EPMC7242087 | biostudies-literature
Project description: Not available
| S-EPMC5562967 | biostudies-literature
Project description:Background and purposeStudies have demonstrated that computed tomography (CT) angiography source images (CTA-SI) acquired under near-steady-state contrast concentration provide infarct core estimates equivalent to diffusion-weighted images (DWI). We sought to test this relationship using our current CTA protocol optimized for faster scan acquisition.MethodsForty-eight consecutive acute ischemic stroke patients met the following criteria: fast-acquisition CTA and magnetic resonance imaging (MRI) within 9 hours of symptom onset, CTA-to-MRI interval under 2 hours, and anterior circulation vessel occlusion. Collaterals were graded on CTA, and lesion volumes were calculated on CTA-SI, DWI, and MR mean transit time (MTT) maps.ResultsThe mean CTA-to-MRI interval was 36 minutes (± 18 minutes). In paired analysis, lesion volumes on CTA-SI were significantly larger than on DWI (45.6 cm3 vs. 29.9 cm3; P < .0001). In 14 (29.2%) cases, there was major CTA-SI overestimation (>25 cm3 difference) of the DWI lesion. Lower collateral score (P = .001), higher National Institutes of Health stroke scale (NIHSS) score (P = .01), older age (P = .01), and proximal occlusion (P < .05) were univariate predictors of major overestimation, with collateral score being the only independent predictor. The interobserver agreement was worse for CTA-SI than for DWI (P < .001 for limits of agreement).ConclusionsCTA-SI performed using a fast-acquisition protocol overestimates the infarct core on DWI. Substantial differences are observed in over 25% of cases, and are associated with reduced collateralization.
| S-EPMC3248622 | biostudies-literature
Project description:PurposeTo test whether the relationship between acute ischemic infarct size on concurrent computed tomographic (CT) angiography source images and diffusion-weighted (DW) magnetic resonance images is dependent on the parameters of CT angiography acquisition protocols.Materials and methodsThis retrospective study had institutional review board approval, and all records were HIPAA compliant. Data in 100 patients with anterior-circulation acute ischemic stroke and large vessel occlusion who underwent concurrent CT angiography and DW imaging within 9 hours of symptom onset were analyzed. Measured areas of hyperintensity at acute DW imaging were used as the standard of reference for infarct size. Information regarding lesion volumes and CT angiography protocol parameters was collected for each patient. For analysis, patients were divided into two groups on the basis of CT angiography protocol differences (patients in group 1 were imaged with the older, slower protocol). Intermethod agreement for infarct size was evaluated by using the Wilcoxon signed rank test, as well as by using Spearman correlation and Bland-Altman analysis. Multivariate analysis was performed to identify predictors of marked (?20%) overestimation of infarct size on CT angiography source images.ResultsIn group 1 (n=35), median hypoattenuation volumes on CT angiography source images were slightly underestimated compared with DW imaging hyperintensity volumes (33.0 vs 41.6 mL, P=.01; ratio=0.83), with high correlation (?=0.91). In group 2 (n=65), median volume on CT angiography source images was much larger than that on DW images (94.8 vs 17.8 mL, P<.0001; ratio=3.5), with poor correlation (?=0.49). This overestimation on CT angiography source images would have inappropriately excluded from reperfusion therapy 44.4% or 90.3% of patients eligible according to DW imaging criteria on the basis of a 100-mL absolute threshold or a 20% or greater mismatch threshold, respectively. Atrial fibrillation and shorter time from contrast material injection to image acquisition were independent predictors of marked (?20%) infarct size overestimation on CT angiography source images.ConclusionCT angiography protocol changes designed to speed imaging and optimize arterial opacification are associated with significant overestimation of infarct size on CT angiography source images.
| S-EPMC3267077 | biostudies-literature
Project description: Not available
| S-EPMC4486328 | biostudies-literature
Project description:ObjectivesConcise "synthetic" review of the state of the art of management of acute ischemic stroke.Data sourcesAvailable literature on PubMed.Study selectionWe selected landmark studies, recent clinical trials, observational studies, and professional guidelines on the management of stroke including the last 10 years.Data extractionEligible studies were identified and results leading to guideline recommendations were summarized.Data synthesisStroke mortality has been declining over the past 6 decades, and as a result, stroke has fallen from the second to the fifth leading cause of death in the United States. This trend may follow recent advances in the management of stroke, which highlight the importance of early recognition and early revascularization. Recent studies have shown that early recognition, emergency interventional treatment of acute ischemic stroke, and treatment in dedicated stroke centers can significantly reduce stroke-related morbidity and mortality. However, stroke remains the second leading cause of death worldwide and the number one cause for acquired long-term disability, resulting in a global annual economic burden.ConclusionsAppropriate treatment of ischemic stroke is essential in the reduction of mortality and morbidity. Management of stroke involves a multidisciplinary approach that starts and extends beyond hospital admission.
| S-EPMC7540624 | biostudies-literature
Project description:Localization of early infarction on first-line Non-contrast computed tomogram (NCCT) guides prompt treatment to improve stroke outcome. Our previous study has shown a good performance in the identification of ischemic injury on NCCT. In the present study, we developed a deep learning (DL) localization model to help localize the early infarction sign on NCCT. This retrospective study included consecutive 517 ischemic stroke (IS) patients who received NCCT within 12 h after stroke onset. A total of 21,436 infarction patches and 20,391 non-infarction patches were extracted from the slice pool of 1,634 NCCT according to brain symmetricity property. The generated patches were fed into different pretrained convolutional neural network (CNN) models such as Visual Geometry Group 16 (VGG16), GoogleNet, Residual Networks 50 (ResNet50), Inception-ResNet-v2 (IR-v2), Inception-v3 and Inception-v4. The selected VGG16 model could detect the early infarction in both supratentorial and infratentorial regions to achieve an average area under curve (AUC) 0.73 after extensive customization. The properly tuned-VGG16 model could identify the early infarction in the cortical, subcortical and cortical plus subcortical areas of supratentorial region with the mean AUC > 0.70. Further, the model could attain 95.6% of accuracy on recognizing infarction lesion in 494 out of 517 IS patients.
| S-EPMC10636036 | biostudies-literature