Ischemic Stroke Lesion Research Articles

Stagewise treatment of tandem lesions of the internal carotid artery in patients with ischaemic stroke

The study was aimed at assessing the results of a stagewise approach to surgical treatment of patients with ischaemic stroke and tandem lesions of coronary arteries. We analysed the results of treating a total of 19 patients with ischaemic stroke having endured in 2017-2018 surgical interventions for tandem lesions of the internal carotid artery. All 19 patients with tandem lesions underwent as the first stage of surgical treatment recanalization of the intracranial portion of the internal carotid artery. Nine patients were subjected to thromboextraction followed by carotid stenting (Group One), ten underwent thromboextraction and carotid endarterectomy (Group Two). One patient was subjected to simultaneous endovascular intervention at the intra- and extracranial level, in 18 patients reconstruction of the internal carotid artery at the extracranial level (second stage) was performed within a postponed period (3-5 days). In the postoperative period we assessed neurological deficit and the results of 90-day survival. No deterioration of neurological deficit was observed after the first stage of surgical treatment. There were statistically significant differences in the degree of regression of a stagewise treatment at discharge, with predominance in the second group. The 90-day survival rate had no prevalence in either group, despite 1 lethal outcome in a patient with simultaneous thromboextraction and stenting of the extracranial portion of the internal carotid artery. Adequate prevention of haemorrhagic transformation of the damaged zone of cerebral tissue in patients with ischaemic stroke caused by tandem lesions of carotid arteries may be ensured by means of selecting stagewise treatment, i. e., postponed elimination of extracranial stenosis of the internal carotid artery. Given a small sample size, the study requires further examination and assessment of the obtained results.

Knowledge transfer between brain lesion segmentation tasks with increased model capacity.

Convolutional neural networks (CNNs) have become an increasingly popular tool for brain lesion segmentation in recent years due to its accuracy and efficiency. However, CNN-based brain lesion segmentation generally requires a large amount of annotated training data, which can be costly for medical imaging. In many scenarios, only a few annotations of brain lesions are available. One common strategy to address the issue of limited annotated data is to transfer knowledge from a different yet relevant source task, where training data is abundant, to the target task of interest. Typically, a model can be pretrained for the source task, and then fine-tuned with the scarce training data associated with the target task. However, classic fine-tuning tends to make small modifications to the pretrained model, which could hinder its adaptation to the target task. Fine-tuning with increased model capacity has been shown to alleviate this negative impact in image classification problems. In this work, we extend the strategy of fine-tuning with increased model capacity to the problem of brain lesion segmentation, and then develop an advanced version that is better suitable for segmentation problems. First, we propose a vanilla strategy of increasing the capacity, where, like in the classification problem, the width of the network is augmented during fine-tuning. Second, because unlike image classification, in segmentation problems each voxel is associated with a labeling result, we further develop a spatially adaptive augmentation strategy during fine-tuning. Specifically, in addition to the vanilla width augmentation, we incorporate a module that computes a spatial map of the contribution of the information given by width augmentation in the final segmentation. For demonstration, the proposed method was applied to ischemic stroke lesion segmentation, where a model pretrained for brain tumor segmentation was fine-tuned, and the experimental results indicate the benefit of our method.

Combining unsupervised and supervised learning for predicting the final stroke lesion.

Predicting the final ischaemic stroke lesion provides crucial information regarding the volume of salvageable hypoperfused tissue, which helps physicians in the difficult decision-making process of treatment planning and intervention. Treatment selection is influenced by clinical diagnosis, which requires delineating the stroke lesion, as well as characterising cerebral blood flow dynamics using neuroimaging acquisitions. Nonetheless, predicting the final stroke lesion is an intricate task, due to the variability in lesion size, shape, location and the underlying cerebral haemodynamic processes that occur after the ischaemic stroke takes place. Moreover, since elapsed time between stroke and treatment is related to the loss of brain tissue, assessing and predicting the final stroke lesion needs to be performed in a short period of time, which makes the task even more complex. Therefore, there is a need for automatic methods that predict the final stroke lesion and support physicians in the treatment decision process. We propose a fully automatic deep learning method based on unsupervised and supervised learning to predict the final stroke lesion after 90 days. Our aim is to predict the final stroke lesion location and extent, taking into account the underlying cerebral blood flow dynamics that can influence the prediction. To achieve this, we propose a two-branch Restricted Boltzmann Machine, which provides specialized data-driven features from different sets of standard parametric Magnetic Resonance Imaging maps. These data-driven feature maps are then combined with the parametric Magnetic Resonance Imaging maps, and fed to a Convolutional and Recurrent Neural Network architecture. We evaluated our proposal on the publicly available ISLES 2017 testing dataset, reaching a Dice score of 0.38, Hausdorff Distance of 29.21 mm, and Average Symmetric Surface Distance of 5.52 mm.

GAMER MRI: Gated-attention mechanism ranking of multi-contrast MRI in brain pathology

IntroductionDuring the last decade, a multitude of novel quantitative and semiquantitative MRI techniques have provided new information about the pathophysiology of neurological diseases. Yet, selection of the most relevant contrasts for a given pathology remains challenging. In this work, we developed and validated a method, Gated-Attention MEchanism Ranking of multi-contrast MRI in brain pathology (GAMER MRI), to rank the relative importance of MR measures in the classification of well understood ischemic stroke lesions. Subsequently, we applied this method to the classification of multiple sclerosis (MS) lesions, where the relative importance of MR measures is less understood. MethodsGAMER MRI was developed based on the gated attention mechanism, which computes attention weights (AWs) as proxies of importance of hidden features in the classification. In the first two experiments, we used Trace-weighted (Trace), apparent diffusion coefficient (ADC), Fluid-Attenuated Inversion Recovery (FLAIR), and T1-weighted (T1w) images acquired in 904 acute/subacute ischemic stroke patients and in 6,230 healthy controls and patients with other brain pathologies to assess if GAMER MRI could produce clinically meaningful importance orders in two different classification scenarios. In the first experiment, GAMER MRI with a pretrained convolutional neural network (CNN) was used in conjunction with Trace, ADC, and FLAIR to distinguish patients with ischemic stroke from those with other pathologies and healthy controls. In the second experiment, GAMER MRI with a patch-based CNN used Trace, ADC and T1w to differentiate acute ischemic stroke lesions from healthy tissue. The last experiment explored the performance of patch-based CNN with GAMER MRI in ranking the importance of quantitative MRI measures to distinguish two groups of lesions with different pathological characteristics and unknown quantitative MR features. Specifically, GAMER MRI was applied to assess the relative importance of the myelin water fraction (MWF), quantitative susceptibility mapping (QSM), T1 relaxometry map (qT1), and neurite density index (NDI) in distinguishing 750 juxtacortical lesions from 242 periventricular lesions in 47 MS patients. Pair-wise permutation t-tests were used to evaluate the differences between the AWs obtained for each quantitative measure. ResultsIn the first experiment, we achieved a mean test AUC of 0.881 and the obtained AWs of FLAIR and the sum of AWs of Trace and ADC were 0.11 and 0.89, respectively, as expected based on previous knowledge. In the second experiment, we achieved a mean test F1 score of 0.895 and a mean AW of Trace = 0.49, of ADC = 0.28, and of T1w = 0.23, thereby confirming the findings of the first experiment. In the third experiment, MS lesion classification achieved test balanced accuracy = 0.777, sensitivity = 0.739, and specificity = 0.814. The mean AWs of T1map, MWF, NDI, and QSM were 0.29, 0.26, 0.24, and 0.22 (p < 0.001), respectively. ConclusionsThis work demonstrates that the proposed GAMER MRI might be a useful method to assess the relative importance of MRI measures in neurological diseases with focal pathology. Moreover, the obtained AWs may in fact help to choose the best combination of MR contrasts for a specific classification problem.

Real-world Independent Testing of e-ASPECTS Software (RITeS): statistical analysis plan

Background: Artificial intelligence-based software may automatically detect ischaemic stroke lesions and provide an Alberta Stroke Program Early CT score (ASPECTS) on CT, and identify arterial occlusion and provide a collateral score on CTA. Large-scale independent testing will inform clinical use, but is lacking. We aim to test e-ASPECTS and e-CTA (Brainomix, Oxford UK) using CT scans obtained from a range of clinical studies. Methods: Using prospectively collected baseline CT and CTA scans from 10 national/international clinical stroke trials or registries (total >6600 patients), we will select a large clinically representative sample for testing e-ASPECTS and e-CTA compared to previously acquired independent expert human interpretation (reference standard). Our primary aims are to test agreement between software-derived and masked human expert ASPECTS, and the diagnostic accuracy of e-ASPECTS for identifying all causes of stroke symptoms using follow-up imaging and final clinical opinion as diagnostic ground truth. Our secondary aims are to test when and why e-ASPECTS is more or less accurate, or succeeds/fails to produce results, agreement between e-CTA and human expert CTA interpretation, and repeatability of e-ASPECTS/e-CTA results. All testing will be conducted on an intention-to-analyse basis. We will assess agreement between software and expert-human ratings and test the diagnostic accuracy of software. Conclusions: RITeS will provide comprehensive, robust and representative testing of e-ASPECTS and e-CTA against the current gold-standard, expert-human interpretation.

Novel insights toward human stroke-related epigenetics: circular RNA and its impact in poststroke processes.

Aim: Circular RNAs (circRNAs) are dysregulated in complex diseases, so we investigated their global expression profile in stroke. Material & methods: Public RNA-Seq data of human ischemic stroke lesion tissues and controls were used to perform the global expression analysis. Target RNA binding proteinsand microRNAswere predicted in silico. Functional enrichment analysis was performed to infer the circRNAs' potential roles. Results: We found that circRNAs are potentially involved in synaptic components and transmission, inflammation and ataxia. An integrative analysis revealed that hsa_circ_0078299 and FXN may be major players in the molecular stroke-context. Conclusion: Our results suggest a broad involvement of circRNAs in some stroke-related processes, indicating their potential as therapeutic targets to allow neuroprotection and brain recovery.

Attention convolutional neural network for accurate segmentation and quantification of lesions in ischemic stroke disease

Ischemic stroke lesion and white matter hyperintensity (WMH) lesion appear as regions of abnormally signal intensity on magnetic resonance image (MRI) sequences. Ischemic stroke is a frequent cause of death and disability, while WMH is a risk factor for stroke. Accurate segmentation and quantification of ischemic stroke and WMH lesions are important for diagnosis and prognosis. However, radiologists have a difficult time distinguishing these two types of similar lesions. A novel deep residual attention convolutional neural network (DRANet) is proposed to accurately and simultaneously segment and quantify ischemic stroke and WMH lesions in the MRI images. DRANet inherits the advantages of the U-net design and applies a novel attention module that extracts high-quality features from the input images. Moreover, the Dice loss function is used to train DRANet to address data imbalance in the training data set. DRANet is trained and evaluated on 742 2D MRI images which are produced from the sub-acute ischemic stroke lesion segmentation (SISS) challenge. Empirical tests demonstrate that DRANet outperforms several other state-of-the-art segmentation methods. It accurately segments and quantifies both ischemic stroke lesion and WMH. Ablation experiments reveal that attention modules improve the predictive performance of DRANet.

Automatic ischemic stroke lesion segmentation from computed tomography perfusion images by image synthesis and attention-based deep neural networks

Ischemic stroke lesion segmentation from Computed Tomography Perfusion (CTP) images is important for accurate diagnosis of stroke in acute care units. However, it is challenged by low image contrast and resolution of the perfusion parameter maps, in addition to the complex appearance of the lesion. To deal with this problem, we propose a novel framework based on synthesized pseudo Diffusion-Weighted Imaging (DWI) from perfusion parameter maps to obtain better image quality for more accurate segmentation. Our framework consists of three components based on Convolutional Neural Networks (CNNs) and is trained end-to-end. First, a feature extractor is used to obtain both a low-level and high-level compact representation of the raw spatiotemporal Computed Tomography Angiography (CTA) images. Second, a pseudo DWI generator takes as input the concatenation of CTP perfusion parameter maps and our extracted features to obtain the synthesized pseudo DWI. To achieve better synthesis quality, we propose a hybrid loss function that pays more attention to lesion regions and encourages high-level contextual consistency. Finally, we segment the lesion region from the synthesized pseudo DWI, where the segmentation network is based on switchable normalization and channel calibration for better performance. Experimental results showed that our framework achieved the top performance on ISLES 2018 challenge and: (1) our method using synthesized pseudo DWI outperformed methods segmenting the lesion from perfusion parameter maps directly; (2) the feature extractor exploiting additional spatiotemporal CTA images led to better synthesized pseudo DWI quality and higher segmentation accuracy; and (3) the proposed loss functions and network structure improved the pseudo DWI synthesis and lesion segmentation performance. The proposed framework has a potential for improving diagnosis and treatment of the ischemic stroke where access to real DWI scanning is limited.

Preliminary Experience Using a Covered Stent Graft in Patients with Acute Ischemic Stroke and Carotid Tandem Lesion.

Endovascular treatment with a covered heparin-bonded stent graft has been shown to be feasible and safe for treatment of peripheral artery disease, but its role in carotid disease remains unclear. The purpose of this study was to determine the feasibility and safety of a covered stent graft in treating cervical carotid artery disease in a consecutive series of patients with acute ischemic stroke (AIS) due to tandem lesion in a single high-volume Comprehensive Stroke Center. A retrospective review of all patients that underwent carotid endovascular interventions during mechanical thrombectomy for AIS using a self-expanding covered stent graft at Vall d'Hebron University Hospital between 2016 and 2018 was conducted. Patient clinical and angiographical characteristics as well as postoperative outcome and follow-up were recorded. A total of eight patients were treated with the covered stents, and we observed significant improvement in stroke severity at 24h in 5/8 patients (mean NIHSS 18 ± 5 vs 12 ± 8, p = 0.02). Successful recanalization (TICI 2b-3) after thrombectomy was achieved in 5/8 (62.5%) patients. One stent occluded during the procedure. At 3-month follow-up, stents were patent in three (37.5%) patients (two with mRS 3 and one with mRS 1). The stents of the remaining four patients (62.5%) were occluded (one with mRS 2 and three with mRS 4), although the patients did not show signs of stroke recurrence. The preliminary results of our study show that the self-expanding covered stent graft did not achieve satisfactory patency at 90-days among patients with AIS and tandem lesions, suggesting that its use in the carotid circulation may not be recommended in the context of AIS.

Diffusion-Weighted Imaging, MR Angiography, and Baseline Data in a Systematic Multicenter Analysis of 3,301 MRI Scans of Ischemic Stroke Patients-Neuroradiological Review Within the MRI-GENIE Study.

Background: Magnetic resonance imaging (MRI) serves as a cornerstone in defining stroke phenotype and etiological subtype through examination of ischemic stroke lesion appearance and is therefore an essential tool in linking genetic traits and stroke. Building on baseline MRI examinations from the centralized and structured radiological assessments of ischemic stroke patients in the Stroke Genetics Network, the results of the MRI-Genetics Interface Exploration (MRI-GENIE) study are described in this work.Methods: The MRI-GENIE study included patients with symptoms caused by ischemic stroke (N = 3,301) from 12 international centers. We established and used a structured reporting protocol for all assessments. Two neuroradiologists, using a blinded evaluation protocol, independently reviewed the baseline diffusion-weighted images (DWIs) and magnetic resonance angiography images to determine acute lesion and vascular occlusion characteristics.Results: In this systematic multicenter radiological analysis of clinical MRI from 3,301 acute ischemic stroke patients according to a structured prespecified protocol, we identified that anterior circulation infarcts were most prevalent (67.4%), that infarcts in the middle cerebral artery (MCA) territory were the most common, and that the majority of large artery occlusions 0 to 48 h from ictus were in the MCA territory. Multiple acute lesions in one or several vascular territories were common (11%). Of 2,238 patients with unilateral DWI lesions, 52.6% had left-sided infarct lateralization (P = 0.013 for χ2 test).Conclusions: This large-scale analysis of a multicenter MRI-based cohort of AIS patients presents a unique imaging framework facilitating the relationship between imaging and genetics for advancing the knowledge of genetic traits linked to ischemic stroke.

Tandem Carotid Lesions in Acute Ischemic Stroke: Mechanisms, Therapeutic Challenges, and Future Directions.

Approximately 15% of patients undergoing endovascular thrombectomy for anterior circulation acute ischemic stroke have a tandem lesion, defined as a severe stenosis or occlusion of the cervical internal carotid artery ipsilateral to its intracranial occlusion. Patients with tandem lesions have worse outcomes than patients with isolated intracranial occlusions, but the optimal management of their carotid lesions during endovascular thrombectomy remains controversial. The main options commonly used in current practice include acute stent placement in the carotid lesion versus thrombectomy alone without definitive revascularization of the carotid artery. While treatment decisions for these patients are often complex and strategies vary according to clinical, anatomic, and technical considerations, only results from randomized trials comparing these approaches are likely to strengthen current recommendations and optimize patient care.

Risk of acute brain lesions in dizzy patients presenting to the emergency room: who needs imaging and who does not?

The usefulness of brain imaging studies in dizzy patients presenting to the emergency department (ED) is controversial. We aimed to assess the ‘real-world’ probability of ischemic stroke and other acute brain lesions (ABLs) in these patients to create an algorithm that helps decision-making on whether which and when brain imaging is needed. By reviewing medical records, we identified 610 patients presenting with dizziness, vertigo or imbalance to our university hospital’s ED and receiving neurological workup. We collected timing/triggers of symptoms, ABCD2 score, focal neurological abnormalities, HINTS (head impulse, nystagmus, test-of-skew) and other central oculomotor signs. ABLs were extracted from CT/MRI reports. Uni-/multivariate logistic regression analyses investigated associations between clinical parameters and ABLs. Finally, the likelihood of ABLs was assessed for different clinically defined subgroups (‘dizziness syndromes’). Early CT (day 1) was performed in 539 (88%) and delayed MR imaging (median: day 4) in 299 (49%) patients. ABLs (89% ischemic stroke) were revealed in 75 (24%) of 318 patients with adequate imaging (MRI or lesion-positive CT). The risk for ABLs increased with the presence of central oculomotor signs (odds ratio 2.8, 95% confidence interval 1.5–5.2) or focal abnormalities (OR 3.3, 95% CI 1.8–6.2). The likelihood of ABLs differed between dizziness syndromes, e.g., HINTS-negative acute vestibular syndrome: 0%, acute imbalance syndrome with ABCD2-score ≥ 4: 50%. We propose a clinical pathway, according to which patients with HINTS-negative acute vestibular syndrome should not receive brain imaging, whereas imaging is suggested in dizzy patients with acute imbalance, central oculomotor signs or focal abnormalities.

Ischemic stroke lesion detection, characterization and classification in CT images with optimal features selection.

Ischemic stroke is the dominant disorder for mortality and morbidity. For immediate diagnosis and treatment plan of ischemic stroke, computed tomography (CT) images are used. This paper proposes a histogram bin based novel algorithm to segment the ischemic stroke lesion using CT and optimal feature group selection to classify normal and abnormal regions. Steps followed are pre-processing, segmentation, extracting texture features, feature ranking, feature grouping, classification and optimal feature group (FG) selection. The first order features, gray level run length matrix features, gray level co-occurrence matrix features and Hu's moment features are extracted. Classification is done using logistic regression (LR), support vector machine classifier (SVMC), random forest classifier (RFC) and neural network classifier (NNC). This proposed approach effectively detects ischemic stroke lesion with a classification accuracy of 88.77%, 97.86%, 99.79% and 99.79% obtained by the LR, SVMC, RFC and NNC when FG12 is opted, which is validated by fourfold cross validation.

Acute and sub-acute stroke lesion segmentation from multimodal MRI

Background and objective. Acute stroke lesion segmentation tasks are of great clinical interest as they can help doctors make better informed time-critical treatment decisions. Magnetic resonance imaging (MRI) is time demanding but can provide images that are considered the gold standard for diagnosis. Automated stroke lesion segmentation can provide with an estimate of the location and volume of the lesioned tissue, which can help in the clinical practice to better assess and evaluate the risks of each treatment.Methods. We propose a deep learning methodology for acute and sub-acute stroke lesion segmentation using multimodal MR imaging. We pre-process the data to facilitate learning features based on the symmetry of brain hemispheres. The issue of class imbalance is tackled using small patches with a balanced training patch sampling strategy and a dynamically weighted loss function. Moreover, a combination of whole patch predictions, using a U-Net based CNN architecture, and high degree of overlapping patches reduces the need for additional post-processing.Results. The proposed method is evaluated using two public datasets from the 2015 Ischemic Stroke Lesion Segmentation challenge (ISLES 2015). These involve the tasks of sub-acute stroke lesion segmentation (SISS) and acute stroke penumbra estimation (SPES) from multiple diffusion, perfusion and anatomical MRI modalities. The performance is compared against state-of-the-art methods with a blind online testing set evaluation on each of the challenges. At the time of submitting this manuscript, our approach is the first method in the online rankings for the SISS (DSC=0.59 ± 0.31) and SPES sub-tasks (DSC=0.84 ± 0.10). When compared with the rest of submitted strategies, we achieve top rank performance with a lower Hausdorff distance.Conclusions. Better segmentation results are obtained by leveraging the anatomy and pathophysiology of acute stroke lesions and using a combined approach to minimize the effects of class imbalance. The same training procedure is used for both tasks, showing the proposed methodology can generalize well enough to deal with different unrelated tasks and imaging modalities without hyper-parameter tuning. In order to promote the reproducibility of our results, a public version of the proposed method has been released to the scientific community.

CSNet: A new DeepNet framework for ischemic stroke lesion segmentation

Background and objectives: Acute stroke lesion segmentation is of paramount importance as it can aid medical personnel to render a quicker diagnosis and administer consequent treatment. Automation of this task is technically exacting due to the variegated appearance of lesions and their dynamic development, medical discrepancies, unavailability of datasets, and the requirement of several MRI modalities for imaging. In this paper, we propose a composite deep learning model primarily based on the self-similar fractal networks and the U-Net model for performing acute stroke diagnosis tasks automatically to assist as well as expedite the decision-making process of medical practitioners.Methods: We put forth a new deep learning architecture, the Classifier-Segmenter network (CSNet), involving a hybrid training strategy with a self-similar (fractal) U-Net model, explicitly designed to perform the task of segmentation. In fractal networks, the underlying design strategy is based on the repetitive generation of self-similar fractals in place of residual connections. The U-Net model exploits both spatial as well as semantic information along with parameter sharing for a faster and efficient training process. In this new architecture, we exploit the benefits of both by combining them into one hybrid training scheme and developing the concept of a cascaded architecture, which further enhances the model’s accuracy by removing redundant parts from the Segmenter’s input. Lastly, a voting mechanism has been employed to further enhance the overall segmentation accuracy.Results: The performance of the proposed architecture has been scrutinized against the existing state-of-the-art deep learning architectures applied to various biomedical image processing tasks by submission on the publicly accessible web platform provided by the MICCAI Ischemic Stroke Lesion Segmentation (ISLES) challenge. The experimental results demonstrate the superiority of the proposed method when compared to similar submitted strategies, both qualitatively and quantitatively in terms of some of the well known evaluation metrics, such as Accuracy, Dice-Coefficient, Recall, and Precision.Conclusions: We believe that our method may find use as a handy tool for doctors to identify the location and extent of irreversibly damaged brain tissue, which is said to be a critical part of the decision-making process in case of an acute stroke.

Current Synthesis and Systematic Review of Main Effects of Calf Blood Deproteinized Medicine (Actovegin®) in Ischemic Stroke.

Background: Stroke is one of the largest problems and clinical-social challenges within neurology and, in general, pathology. Here, we briefly reviewed the main pathophysiological mechanisms of ischemic stroke, which represent targets for medical interventions, including for a calf blood deproteinized hemodialysate/ultrafiltrate. Methods: We conducted a systematic review of current related literature concerning the effects of Actovegin®, of mainly the pleiotropic type, applied to the injury pathways of ischemic stroke. Results: The bibliographic resources regarding the use of Actovegin® in ischemic stroke are scarce. The main Actovegin® actions refer to the ischemic stroke lesion items’ ensemble, targeting tissue oxidation, energy metabolism, and glucose availability through their augmentation, combating ischemic processes and oxidative stress, and decreasing inflammation (including with modulatory connotations, by the nuclear factor-κB pathway) and apoptosis-like processes, counteracting them by mitigating the caspase-3 activation induced by amyloid β-peptides. Conclusion: Since no available therapeutic agents are capable of curing the central nervous system’s lesions, any contribution, such as that of Actovegin® (with consideration of a positive balance between benefits and risks), is worthy of further study and periodic reappraisal, including investigation into further connected aspects.

Real-world Independent Testing of e-ASPECTS Software (RITeS): statistical analysis plan.

Background: Artificial intelligence-based software may automatically detect ischaemic stroke lesions and provide an Alberta Stroke Program Early CT score (ASPECTS) on CT, and identify arterial occlusion and provide a collateral score on CTA. Large-scale independent testing will inform clinical use, but is lacking. We aim to test e-ASPECTS and e-CTA (Brainomix, Oxford UK) using CT scans obtained from a range of clinical studies. Methods: Using prospectively collected baseline CT and CTA scans from 10 national/international clinical stroke trials or registries (total >6600 patients), we will select a large clinically representative sample for testing e-ASPECTS and e-CTA compared to previously acquired independent expert human interpretation (reference standard). Our primary aims are to test agreement between software-derived and masked human expert ASPECTS, and the diagnostic accuracy of e-ASPECTS for identifying all causes of stroke symptoms using follow-up imaging and final clinical opinion as diagnostic ground truth. Our secondary aims are to test when and why e-ASPECTS is more or less accurate, or succeeds/fails to produce results, agreement between e-CTA and human expert CTA interpretation, and repeatability of e-ASPECTS/e-CTA results. All testing will be conducted on an intention-to-analyse basis. We will assess agreement between software and expert-human ratings and test the diagnostic accuracy of software. Conclusions: RITeS will provide comprehensive, robust and representative testing of e-ASPECTS and e-CTA against the current gold-standard, expert-human interpretation.

Use of Deep Learning to Predict Final Ischemic Stroke Lesions From Initial Magnetic Resonance Imaging

Predicting infarct size and location is important for decision-making and prognosis in patients with acute stroke. To determine whether a deep learning model can predict final infarct lesions using magnetic resonance images (MRIs) acquired at initial presentation (baseline) and to compare the model with current clinical prediction methods. In this multicenter prognostic study, a specific type of neural network for image segmentation (U-net) was trained, validated, and tested using patients from the Imaging Collaterals in Acute Stroke (iCAS) study from April 14, 2014, to April 15, 2018, and the Diffusion Weighted Imaging Evaluation for Understanding Stroke Evolution Study-2 (DEFUSE-2) study from July 14, 2008, to September 17, 2011 (reported in October 2012). Patients underwent baseline perfusion-weighted and diffusion-weighted imaging and MRI at 3 to 7 days after baseline. Patients were grouped into unknown, minimal, partial, and major reperfusion status based on 24-hour imaging results. Baseline images acquired at presentation were inputs, and the final true infarct lesion at 3 to 7 days was considered the ground truth for the model. The model calculated the probability of infarction for every voxel, which can be thresholded to produce a prediction. Data were analyzed from July 1, 2018, to March 7, 2019. Area under the curve, Dice score coefficient (DSC) (a metric from 0-1 indicating the extent of overlap between the prediction and the ground truth; a DSC of ≥0.5 represents significant overlap), and volume error. Current clinical methods were compared with model performance in subgroups of patients with minimal or major reperfusion. Among the 182 patients included in the model (97 women [53.3%]; mean [SD] age, 65 [16] years), the deep learning model achieved a median area under the curve of 0.92 (interquartile range [IQR], 0.87-0.96), DSC of 0.53 (IQR, 0.31-0.68), and volume error of 9 (IQR, -14 to 29) mL. In subgroups with minimal (DSC, 0.58 [IQR, 0.31-0.67] vs 0.55 [IQR, 0.40-0.65]; P = .37) or major (DSC, 0.48 [IQR, 0.29-0.65] vs 0.45 [IQR, 0.15-0.54]; P = .002) reperfusion for which comparison with existing clinical methods was possible, the deep learning model had comparable or better performance. The deep learning model appears to have successfully predicted infarct lesions from baseline imaging without reperfusion information and achieved comparable performance to existing clinical methods. Predicting the subacute infarct lesion may help clinicians prepare for decompression treatment and aid in patient selection for neuroprotective clinical trials.

Determination the Ischemic Stroke of Brain MRI Based On Apparent Diffusion Coefficient (ADC) with b Value Variation

The Research to Determination the Ischemic Stroke of Brain MRI Based on Apparent Diffusion Coefficient (ADC) with b Value variations. The purposes of this study were to find b valuevariations of Diffusion Weighted Image (DWI), determining the optimum b value to find out the Contrast to Noise Ratio (CNR) ischemic stroke lesion in DWI image, and To determine the Diffusion Coefficient range obtained from the ADC (Apparent Diffusion Coefficient) value between old ischemic stroke and new ischemic stroke. In this research, used are 0, 500 and 1000. The result obtained b value influenceon the quality of DWI were analyzed in 30 patients suspected with old ischemic stroke and new ischemic stroke. Data analysis was done quantitatively with the Region Of Interest (ROI) directly on computer MRI image planes and were conducted statistical tests Signal to Noise Ratio (SNR) and Contrast to Noise Ratio (CNR). The highest Signal to Noise Ratio (SNR) found in b 0, while the highest Contrast to Noise Ratio (CNR) on the b 1000 s/mm 2. The statistics test show that significant SNR value of the Lesion, around the lesion, Liquor, WM, GM tissue is about 0,000 (p<0,05), while for the Contrast to Noise Ratio (CNR) of the lesion and around the lesion tissue is about 0,000 (p<0,05). This results showed that b value variation have a difference at lesion, around the lesion, Liquor, WM, GM tissue for MRI imaging quality. SNR and CNR are important aspect on imaging optimalization process to give the diagnose information. In this research, the diffusion coefficient value in new ischemic stroke is < 1 × 10−3, whereas the old ischemic stroke is > 1 × 10−3, the results showed that15 patients have new ischemic stroke and 15 patients have old ischemic stroke.

Brain SegNet: 3D local refinement network for brain lesion segmentation

MR images (MRIs) accurate segmentation of brain lesions is important for improving cancer diagnosis, surgical planning, and prediction of outcome. However, manual and accurate segmentation of brain lesions from 3D MRIs is highly expensive, time-consuming, and prone to user biases. We present an efficient yet conceptually simple brain segmentation network (referred as Brain SegNet), which is a 3D residual framework for automatic voxel-wise segmentation of brain lesion. Our model is able to directly predict dense voxel segmentation of brain tumor or ischemic stroke regions in 3D brain MRIs. The proposed 3D segmentation network can run at about 0.5s per MRIs - about 50 times faster than previous approaches Med Image Anal 43: 98–111, 2018, Med Image Anal 36:61–78, 2017. Our model is evaluated on the BRATS 2015 benchmark for brain tumor segmentation, where it obtains state-of-the-art results, by surpassing recently published results reported in Med Image Anal 43: 98–111, 2018, Med Image Anal 36:61–78, 2017. We further applied the proposed Brain SegNet for ischemic stroke lesion outcome prediction, with impressive results achieved on the Ischemic Stroke Lesion Segmentation (ISLES) 2017 database.