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Background Perfusion imaging is important to identify a target mismatch in stroke but requires contrast agents and postprocessing software. Purpose To use a deep learning model to predict the hypoperfusion lesion in stroke and identify patients with a target mismatch profile from diffusion-weighted imaging (DWI) and clinical information alone, using perfusion MRI as the reference standard. Materials and Methods Imaging data sets of patients with acute ischemic stroke with baseline perfusion MRI and DWI were retrospectively reviewed from multicenter data available from 2008 to 2019 (Imaging Collaterals in Acute Stroke, Diffusion and Perfusion Imaging Evaluation for Understanding Stroke Evolution 2, and University of California, Los Angeles stroke registry). For perfusion MRI, rapid processing of perfusion and diffusion software automatically segmented the hypoperfusion lesion (time to maximum, ≥6 seconds) and ischemic core (apparent diffusion coefficient [ADC], ≤620 × 10 -6 mm 2 /sec). A three-dimensional U-Net deep learning model was trained using baseline DWI, ADC, National Institutes of Health Stroke Scale score, and stroke symptom sidedness as inputs, with the union of hypoperfusion and ischemic core segmentation serving as the ground truth. Model performance was evaluated using the Dice score coefficient (DSC). Target mismatch classification based on the model was compared with that of the clinical-DWI mismatch approach defined by the DAWN trial by using the McNemar test. Results Overall, 413 patients (mean age, 67 years ± 15 [SD]; 207 men) were included for model development and primary analysis using fivefold cross-validation (247, 83, and 83 patients in the training, validation, and test sets, respectively, for each fold). The model predicted the hypoperfusion lesion with a median DSC of 0.61 (IQR, 0.45-0.71). The model identified patients with target mismatch with a sensitivity of 90% (254 of 283; 95% CI: 86, 93) and specificity of 77% (100 of 130; 95% CI: 69, 83) compared with the clinical-DWI mismatch sensitivity of 50% (140 of 281; 95% CI: 44, 56) and specificity of 89% (116 of 130; 95% CI: 83, 94) ( P < .001 for all). Conclusion A three-dimensional U-Net deep learning model predicted the hypoperfusion lesion from diffusion-weighted imaging (DWI) and clinical information and identified patients with a target mismatch profile with higher sensitivity than the clinical-DWI mismatch approach. ClinicalTrials.gov registration nos. NCT02225730, NCT01349946, NCT02586415 © RSNA, 2022 Supplemental material is available for this article. See also the editorial by Kallmes and Rabinstein in this issue.

Background: The potential heterogeneity in occlusive thrombi caused by in situ propagation by secondary thrombosis after embolic occlusion could obscure the characteristics of original thrombi, preventing the clarification of a specific thrombus signature for the etiology of ischemic stroke. We aimed to investigate the heterogeneity of occlusive thrombi by pretreatment imaging., Methods: Among consecutive stroke patients with acute embolic anterior circulation large vessel occlusion treated with thrombectomy, we retrospectively reviewed 104 patients with visible occlusive thrombi on pretreatment non-contrast computed tomography admitted from January 2015 to December 2018. A region of interest was set on the whole thrombus on non-contrast computed tomography under the guidance of computed tomography angiography. The region of interest was divided equally into the proximal and distal segments and the difference in Hounsfield unit densities between the two segments was calculated., Results: Hounsfield unit density in the proximal segment was higher than that in the distal segment (mean difference 4.45; p  < 0.001), regardless of stroke subtypes. On multivariate analysis, thrombus length was positively correlated (β = 0.25; p  < 0.001) and time from last-known-well to imaging was inversely correlated (β = -0.0041; p  = 0.002) with the difference in Hounsfield unit densities between the proximal and distal segments., Conclusions: The difference in density between the proximal and distal segments increased as thrombi became longer and decreased as thrombi became older after embolic occlusion. This time/length-dependent thrombus heterogeneity between the two segments is suggestive of secondary thrombosis initially occurring on the proximal side of the occlusion.

Background and Purpose: Acute leptomeningeal collateral flow is vital for maintaining perfusion to penumbral tissue in acute ischemic stroke caused by large-vessel occlusion. In this study, we aimed to investigate the clinically available indicators of leptomeningeal collateral variability in embolic large-vessel occlusion., Materials and Methods: Among prospectively registered consecutive patients with acute embolic anterior circulation large-vessel occlusion treated with thrombectomy, we analyzed 108 patients admitted from January 2015 to December 2019 who underwent evaluation of leptomeningeal collateral status on pretreatment CTA. Clinical characteristics, extent of leukoaraiosis on MR imaging, embolic stroke subtype, time of imaging, occlusive thrombus characteristics, presenting stroke severity, and clinical outcome were collected. The clinical indicators of good collateral status (>50% collateral filling of the occluded territory) were analyzed using multivariate logistic regression analysis., Results: Good collateral status was present in 67 patients (62%) and associated with independent functional outcomes at 3 months. Reduced leukoaraiosis (total Fazekas score, 0-2) was positively related to good collateral status (OR, 9.57; 95% CI, 2.49-47.75), while the cardioembolic stroke mechanism was inversely related to good collateral status (OR, 0.17; 95% CI, 0.02-0.87). In 82 patients with cardioembolic stroke, shorter thrombus length (OR, 0.91 per millimeter increase; 95% CI, 0.82-0.99) and reduced leukoaraiosis (OR, 5.79; 95% CI, 1.40-29.61) were independently related to good collateral status., Conclusions: Among patients with embolic large-vessel occlusion, reduced leukoaraiosis, noncardiac embolism mechanisms including embolisms of arterial or undetermined origin, and shorter thrombus length in cardioembolism are indicators of good collateral flow., (© 2022 by American Journal of Neuroradiology.)

In stroke care, the extent of irreversible brain injury, termed infarct core, plays a key role in determining eligibility for acute treatments, such as intravenous thrombolysis and endovascular reperfusion therapies. Many of the pivotal randomized clinical trials testing those therapies used MRI Diffusion-Weighted Imaging (DWI) or CT Perfusion (CTP) to define infarct core. Unfortunately, these modalities are not available 24/7 outside of large stroke centers. As such, there is a need for accurate infarct core determination using faster and more widely available imaging modalities including Non-Contrast CT (NCCT) and CT Angiography (CTA). Prior studies have suggested that CTA provides improved predictions of infarct core relative to NCCT; however, this assertion has never been numerically quantified by automatic medical image computing pipelines using acquisition protocols not confounded by different scanner manufacturers, or other protocol settings such as exposure times, kilovoltage peak, or imprecision due to contrast bolus delays. In addition, single-phase CTA protocols are at present designed to optimize contrast opacification in the arterial phase. This approach works well to maximize the sensitivity to detect vessel occlusions, however, it may not be the ideal timing to enhance the ischemic infarct core signal (ICS). In this work, we propose an image analysis pipeline on CT-based images of 88 acute ischemic stroke (AIS) patients drawn from a single dynamic acquisition protocol acquired at the acute ischemic phase. We use the first scan at the time of the dynamic acquisition as a proxy for NCCT, and the rest of the scans as a proxy for CTA scans, with bolus imaged at different brain enhancement phases. Thus, we use the terms "NCCT" and "CTA" to refer to them. This pipeline enables us to answer the questions "Does the injection of bolus enhance the infarct core signal?" and "What is the ideal bolus timing to enhance the infarct core signal?" without being influenced by aforementioned factors such as scanner model, acquisition settings, contrast bolus delay, and human reader errors. We use reference MRI DWI images acquired after successful recanalization acting as our gold standard for infarct core. The ICS is quantified by calculating the difference in intensity distribution between the infarct core region and its symmetrical healthy counterpart on the contralateral hemisphere of the brain using a metric derived from information theory, the Kullback-Leibler divergence (KL divergence). We compare the ICS provided by NCCT and CTA and retrieve the optimal timing of CTA bolus to maximize the ICS. In our experiments, we numerically confirm that CTAs provide greater ICS compared to NCCT. Then, we find that, on average, the ideal CTA acquisition time to maximize the ICS is not the current target of standard CTA protocols, i.e., during the peak of arterial enhancement, but a few seconds afterward (median of 3 s; 95% CI [1.5, 3.0]). While there are other studies comparing the prediction potential of ischemic infarct core from NCCT and CTA images, to the best of our knowledge, this analysis is the first to perform a quantitative comparison of the ICS among CT based scans, with and without bolus injection, acquired using the same scanning sequence and a precise characterization of the bolus uptake, hence, reducing potential confounding factors., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Treatment of acute ischemic strokes (AIS) is largely contingent upon the time since stroke onset (TSS). However, TSS may not be readily available in up to 25% of patients with unwitnessed AIS. Current clinical guidelines for patients with unknown TSS recommend the use of MRI to determine eligibility for thrombolysis, but radiology assessments have high inter-reader variability. In this work, we present deep learning models that leverage MRI diffusion series to classify TSS based on clinically validated thresholds. We propose an intra-domain task-adaptive transfer learning method, which involves training a model on an easier clinical task (stroke detection) and then refining the model with different binary thresholds of TSS. We apply this approach to both 2D and 3D CNN architectures with our top model achieving an ROC-AUC value of 0.74, with a sensitivity of 0.70 and a specificity of 0.81 for classifying TSS < 4.5 h. Our pretrained models achieve better classification metrics than the models trained from scratch, and these metrics exceed those of previously published models applied to our dataset. Furthermore, our pipeline accommodates a more inclusive patient cohort than previous work, as we did not exclude imaging studies based on clinical, demographic, or image processing criteria. When applied to this broad spectrum of patients, our deep learning model achieves an overall accuracy of 75.78% when classifying TSS < 4.5 h, carrying potential therapeutic implications for patients with unknown TSS., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Background and Purpose- Selection of patients with acute ischemic stroke for endovascular treatment generally relies on dynamic susceptibility contrast magnetic resonance imaging or computed tomography perfusion. Dynamic susceptibility contrast magnetic resonance imaging requires injection of contrast, whereas computed tomography perfusion requires high doses of ionizing radiation. The purpose of this work was to develop and evaluate a deep learning (DL)-based algorithm for assisting the selection of suitable patients with acute ischemic stroke for endovascular treatment based on 3-dimensional pseudo-continuous arterial spin labeling (pCASL). Methods- A total of 167 image sets of 3-dimensional pCASL data from 137 patients with acute ischemic stroke scanned on 1.5T and 3.0T Siemens MR systems were included for neural network training. The concurrently acquired dynamic susceptibility contrast magnetic resonance imaging was used to produce labels of hypoperfused brain regions, analyzed using commercial software. The DL and 6 machine learning (ML) algorithms were trained with 10-fold cross-validation. The eligibility for endovascular treatment was determined retrospectively based on the criteria of perfusion/diffusion mismatch in the DEFUSE 3 trial (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke). The trained DL algorithm was further applied on twelve 3-dimensional pCASL data sets acquired on 1.5T and 3T General Electric MR systems, without fine-tuning of parameters. Results- The DL algorithm can predict the dynamic susceptibility contrast-defined hypoperfusion region in pCASL with a voxel-wise area under the curve of 0.958, while the 6 ML algorithms ranged from 0.897 to 0.933. For retrospective determination for subject-level endovascular treatment eligibility, the DL algorithm achieved an accuracy of 92%, with a sensitivity of 0.89 and specificity of 0.95. When applied to the GE pCASL data, the DL algorithm achieved a voxel-wise area under the curve of 0.94 and a subject-level accuracy of 92% for endovascular treatment eligibility. Conclusions- pCASL perfusion magnetic resonance imaging in conjunction with the DL algorithm provides a promising approach for assisting decision-making for endovascular treatment in patients with acute ischemic stroke.

Current clinical practice relies on clinical history to determine the time since stroke (TSS) onset. Imaging-based determination of acute stroke onset time could provide critical information to clinicians in deciding stroke treatment options, such as thrombolysis. The patients with unknown or unwitnessed TSS are usually excluded from thrombolysis, even if their symptoms began within the therapeutic window. In this paper, we demonstrate a machine learning approach for TSS classification using routinely acquired imaging sequences. We develop imaging features from the magnetic resonance (MR) images and train machine learning models to classify the TSS. We also propose a deep-learning model to extract hidden representations for the MR perfusion-weighted images and demonstrate classification improvement by incorporating these additional deep features. The cross-validation results show that our best classifier achieved an area under the curve of 0.765, with a sensitivity of 0.788 and a negative predictive value of 0.609, outperforming existing methods. We show that the features generated by our deep-learning algorithm correlate with the MR imaging features, and validate the robustness of the model on imaging parameter variations (e.g., year of imaging). This paper advances magnetic resonance imaging analysis one-step-closer to an operational decision support tool for stroke treatment guidance.

Objective: To investigate whether hemodynamic features of symptomatic intracranial atherosclerotic stenosis (sICAS) might correlate with the risk of stroke relapse, using a computational fluid dynamics (CFD) model., Methods: In a cohort study, we recruited patients with acute ischemic stroke attributed to 50 to 99% ICAS confirmed by computed tomographic angiography (CTA). With CTA-based CFD models, translesional pressure ratio (PR = pressure poststenotic /pressure prestenotic ) and translesional wall shear stress ratio (WSSR = WSS stenotic - throat /WSS prestenotic ) were obtained in each sICAS lesion. Translesional PR ≤ median was defined as low PR and WSSR ≥4th quartile as high WSSR. All patients received standard medical treatment. The primary outcome was recurrent ischemic stroke in the same territory (SIT) within 1 year., Results: Overall, 245 patients (median age = 61 years, 63.7% males) were analyzed. Median translesional PR was 0.94 (interquartile range [IQR] = 0.87-0.97); median translesional WSSR was 13.3 (IQR = 7.0-26.7). SIT occurred in 20 (8.2%) patients, mostly with multiple infarcts in the border zone and/or cortical regions. In multivariate Cox regression, low PR (adjusted hazard ratio [HR] = 3.16, p = 0.026) and high WSSR (adjusted HR = 3.05, p = 0.014) were independently associated with SIT. Patients with both low PR and high WSSR had significantly higher risk of SIT than those with normal PR and WSSR (risk = 17.5% vs 3.0%, adjusted HR = 7.52, p = 0.004)., Interpretation: This work represents a step forward in utilizing computational flow simulation techniques in studying intracranial atherosclerotic disease. It reveals a hemodynamic pattern of sICAS that is more prone to stroke relapse, and supports hypoperfusion and artery-to-artery embolism as common mechanisms of ischemic stroke in such patients. Ann Neurol 2019;85:752-764., (© 2019 American Neurological Association.)

Background: The pivotal impact of collateral circulation on outcomes in endovascular therapy has fueled the development of numerous CTA collateral scales, yet synchronized validation with conventional angiography has never occurred. We validated multiphase flat-detector CTA (mpFDCTA) for collateral imaging in patients undergoing endovascular stroke treatment., Materials and Methods: Consecutive acute ischemic stroke patient data, including mpFDCTA shortly followed by digital subtraction angiography (DSA), in the setting of acute ICA- or MCA-occlusions were analyzed. An independent core lab scored mpFDCTA with an established collateral scale and separately graded American Society of Interventional and Therapeutic Neuroradiology (ASITN) collateral score on DSA, blind to all other data., Results: 24 consecutive cases (age 76.7 ± 7.3 years; 58.3% women; baseline NIHSS median 17 (4-23)) of acute ICA- or MCA-occlusion were analyzed. Time from mpFDCTA to intracranial DSA was 23.04 ± 7.6 minutes. Median mpFDCTA collateral score was 3 (0-5) and median DSA ASITN collateral score was 2 (0-3), including the full range of potential collateral grades. mpFDCTA and ASITN collateral score were strongly correlated (r = 0.86, p<0.001). mpFDCTA provided more complete collateral data compared to selective DSA injections in cases of ICA-occlusion. ROC analyses for prediction of clinical outcomes revealed an AUC of 0.76 for mpFDCTA- and 0.70 for DSA ASITN collaterals., Conclusions: mpFDCTA in the angiography suite provides a validated measure of collaterals, offering distinct advantages over conventional angiography. Direct patient transfer to the angiography suite and mpFDCTA collateral grading provides a novel and reliable triage paradigm for acute ischemic stroke., Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: D.S. Liebeskind: Consultant/Advisory Board; Significant; Consultant to Medtronic (Imaging Core Lab), Consultant to Stryker Neurovascular (Imaging Core Lab). D. Behme: Speakers' Bureau; Modest; Stryker. M. Psychogios: Honoraria; Modest; Siemens Healthineers. The Department of Neuroradiology has a research agreement with Siemens Medical Solutions, Forchheim, Germany. There are no other conflicts of interests to be reported for the other authors. This does not alter our adherence to PLOS ONE policies on sharing data and materials. There are no other conflicts of interests to be reported for the other authors.

The purpose of this study was to develop and evaluate a scoring system for assessing reperfusion status based on arterial spin labeled (ASL) perfusion MRI in acute ischemic stroke (AIS) patients receiving thrombolysis and/or endovascular treatment. Pseudo-continuous ASL with background suppressed 3D GRASE was acquired along with DWI in 90 patients within 24 h post-treatment. An automatic reperfusion scoring system (auto-RPS) was devised based on the Alberta Stroke Program Early CT Score (ASPECTS) template, and compared with manual RPS and DWI-ASPECTS. TICI (thrombolysis in cerebral infarction) scores were graded in 48 patients who received endovascular treatment. Favorable outcomes were defined by a modified Rankin Scale score of 0-2 at three months. Auto-RPS was positively correlated with DWI-ASPECTS (ρ = 0.6, P < 0.001) and was on average 1 point lower than DWI-ASPECTS ( P < 0.001). The area under the receiver operating characteristic curve for discriminating poor functional outcome (n = 90) was 0.75 (95% CI, 0.64-0.86) for manual RPS, 0.85 (95% CI, 0.76-0.94) for auto-RPS, and 0.81 (95% CI, 0.71-0.90) for DWI-ASPECTS. Multiple logistic regression analysis in the TICI-graded patients (n = 48) showed that auto-RPS is highly associated with functional outcome (OR = 25.2, 95% CI 4.02-496, P < 0.01). Post treatment auto-RPS within 24 h provides a useful tool to predict functional outcome in AIS patients.

Background and Purpose: Multi-delay arterial spin-labeling (ASL) perfusion imaging has been used as a promising modality to evaluate cerebral perfusion. Our aim was to assess the association of leptomeningeal collateral perfusion scores based on ASL parameters with outcome of endovascular treatment in patients with acute ischemic stroke (AIS) in the middle cerebral artery (MCA) territory., Materials and Methods: ASL data at 4 post-labeling delay (PLD) times (PLD = 1.5, 2, 2.5, 3 s) were acquired during routine clinical magnetic resonance examination on AIS patients prior to endovascular treatment. A 3-point scale of leptomeningeal collateral perfusion grade on 10 anatomic regions was determined based on arterial transit times (ATT), cerebral blood flow (CBF), and arterial cerebral blood volume (CBV), estimated by the multi-delay ASL protocol. Based on a 90-day modified Rankin Scale (mRS), the patients were dichotomized to moderate/good (mRS 0-3) and poor outcome (mRS 4-6) and the regional collateral flow scores were compared., Results: Fifty-five AIS patients with unilateral MCA stroke (mean 73.95±14.82 years) including 23 males were enrolled. Compared with poor outcome patients, patients with moderate to good outcomes had a significantly higher leptomeningeal collateral perfusion scores on CBV (3.01±2.11 vs. 1.82±1.51, p=0.024) but no differences on scores on CBF (2.31±1.61 vs. 1.66±1.32, p=0.231) and ATT (2.67±2.33 vs. 3.42±3.37, p=0.593)., Conclusions: Higher leptomeningeal collateral perfusion scores on CBV images by ASL may be a specific marker of clinical outcome after endovascular treatment in patients with acute MCA ischemic stroke. Further study with larger sample size is warranted.

Visualization and quantification of blood flow are essential for the diagnosis and treatment evaluation of cerebrovascular diseases. For rapid imaging of the cerebrovasculature, digital subtraction angiography (DSA) remains the gold standard as it offers high spatial resolution. This paper lays out a methodological framework, named perfusion angiography, for the quantitative analysis and visualization of blood flow parameters from DSA images. The parameters, including cerebral blood flow (CBF) and cerebral blood volume (CBV), mean transit time (MTT), time-to-peak (TTP), and T max, are computed using a bolus tracking method based on the deconvolution of the time-density curve on a pixel-by-pixel basis. The method is tested on 66 acute ischemic stroke patients treated with thrombectomy and/or tissue plasminogen activator (tPA) and also evaluated on an estimation task with known ground truth. This novel imaging tool provides unique insights into flow mechanisms that cannot be observed directly in DSA sequences and might be used to evaluate the impact of endovascular interventions more precisely.

Permeability images derived from magnetic resonance (MR) perfusion images are sensitive to blood-brain barrier derangement of the brain tissue and have been shown to correlate with subsequent development of hemorrhagic transformation (HT) in acute ischemic stroke. This paper presents a multi-center retrospective study that evaluates the predictive power in terms of HT of six permeability MRI measures including contrast slope (CS), final contrast (FC), maximum peak bolus concentration (MPB), peak bolus area (PB), relative recirculation (rR), and percentage recovery (%R). Dynamic T2*-weighted perfusion MR images were collected from 263 acute ischemic stroke patients from four medical centers. An essential aspect of this study is to exploit a classifier-based framework to automatically identify predictive patterns in the overall intensity distribution of the permeability maps. The model is based on normalized intensity histograms that are used as input features to the predictive model. Linear and nonlinear predictive models are evaluated using a cross-validation to measure generalization power on new patients and a comparative analysis is provided for the different types of parameters. Results demonstrate that perfusion imaging in acute ischemic stroke can predict HT with an average accuracy of more than 85% using a predictive model based on a nonlinear regression model. Results also indicate that the permeability feature based on the percentage of recovery performs significantly better than the other features. This novel model may be used to refine treatment decisions in acute stroke., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Early and accurate prediction of tissue outcome is essential to the clinical decision-making process in acute ischemic stroke. We present a quantitative predictive model of tissue fate that combines regional imaging features available after onset. A key component is the use of cuboids randomly sampled during the learning process. Models trained with time-to-maximum feature (Tmax) computed from perfusion weighted images (PWI) are compared to the ones obtained from the apparent diffusion coefficient (ADC). The prediction task is formalized as a regression problem where the inputs are the local cuboids extracted from Tmax or ADC images at onset, and the output is the segmented FLAIR intensity of the tissue 4 days after intervention. Experiments on 25 acute stroke patients demonstrate the effectiveness of the proposed approach in predicting tissue fate. Results on our dataset show the superiority of the regional model vs. a single-voxel-based approach, indicate that PWI regional models outperform ADC models, and demonstrates that a nonlinear regression model significantly improves the results in comparison to a linear model.

Background and purposeAcute leptomeningeal collateral flow is vital for maintaining perfusion to penumbral tissue in acute ischemic stroke caused by large-vessel occlusion. In this study, we aimed to investigate the clinically available indicators of leptomeningeal collateral variability in embolic large-vessel occlusion.Materials and methodsAmong prospectively registered consecutive patients with acute embolic anterior circulation large-vessel occlusion treated with thrombectomy, we analyzed 108 patients admitted from January 2015 to December 2019 who underwent evaluation of leptomeningeal collateral status on pretreatment CTA. Clinical characteristics, extent of leukoaraiosis on MR imaging, embolic stroke subtype, time of imaging, occlusive thrombus characteristics, presenting stroke severity, and clinical outcome were collected. The clinical indicators of good collateral status (>50% collateral filling of the occluded territory) were analyzed using multivariate logistic regression analysis.ResultsGood collateral status was present in 67 patients (62%) and associated with independent functional outcomes at 3 months. Reduced leukoaraiosis (total Fazekas score, 0-2) was positively related to good collateral status (OR, 9.57; 95% CI, 2.49-47.75), while the cardioembolic stroke mechanism was inversely related to good collateral status (OR, 0.17; 95% CI, 0.02-0.87). In 82 patients with cardioembolic stroke, shorter thrombus length (OR, 0.91 per millimeter increase; 95% CI, 0.82-0.99) and reduced leukoaraiosis (OR, 5.79; 95% CI, 1.40-29.61) were independently related to good collateral status.ConclusionsAmong patients with embolic large-vessel occlusion, reduced leukoaraiosis, noncardiac embolism mechanisms including embolisms of arterial or undetermined origin, and shorter thrombus length in cardioembolism are indicators of good collateral flow.

BACKGROUND AND PURPOSE:Vessel wall imaging can identify intracranial atherosclerotic plaque and give clues about its components. We aimed to investigate whether the plaque hyperintensity in the middle cerebral artery on T2-weighted vessel wall imaging is associated with ischemic stroke. MATERIALS AND METHODS:We retrospectively reviewed our institutional vessel wall MR imaging data base. Patients with an acute ischemic stroke within 7-day onset in the MCA territory were enrolled. Patients with stroke and stenotic MCA plaque (stenosis degree, ≥50%) were included for analysis. Ipsilateral MCA plaque was defined as symptomatic, and contralateral plaque, as asymptomatic. Plaque was manually delineated on T2-weighted vessel wall imaging. The plaque signal was normalized to the ipsilateral muscle signal. The thresholds and volume of normalized plaque signal were investigated using logistic regression and receiver operating characteristic analysis to determine the association between normalized plaque signal and stroke. RESULTS:One hundred eight stenotic MCAs were analyzed (from 88 patients, 66 men; mean age, 58 ± 15 years), including 72 symptomatic and 36 asymptomatic MCA plaques. Symptomatic MCA plaque showed larger plaque hyperintensity volume compared with asymptomatic MCA plaque. The logistic regression model incorporating stenosis degree, remodeling ratio, and normalized plaque signal 1.3-1.4 (OR, 6.25; 95% CI, 1.90-20.57) had a higher area under curve in differentiating symptomatic/asymptomatic MCA plaque, compared with a model with only stenosis degree and remodeling ratio (area under curve, 0.884 versus 0.806; P =.008). CONCLUSIONS:The MCA plaque hyperintensity on T2-weighted vessel wall imaging is independently associated with ischemic stroke and adds value to symptomatic MCA plaque classification. Measuring the normalized signal intensity may serve as a practical and integrative approach to the analysis of intracranial atherosclerotic plaque.