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  • 1
    Online Resource
    Online Resource
    Admission Diffusion-Weighted Imaging Lesion Volume in Patients With Large Vessel Occlusion Stroke and Alberta Stroke Program Early CT Score of ≥6 Points : Serial Computed Tomography-Magnetic Resonance Imaging Collateral Measurements
    Ovid Technologies (Wolters Kluwer Health) ; 2019
    In:  Stroke Vol. 50, No. 11 ( 2019-11), p. 3115-3120
    Details
    In: Stroke, Ovid Technologies (Wolters Kluwer Health), Vol. 50, No. 11 ( 2019-11), p. 3115-3120
    Abstract: We hypothesized that the pial collateral status at the time of presentation could predict the infarct size on magnetic resonance imaging in patients with similar degrees of early ischemic changes on computed tomography. We tested the association between serial changes in collateral status and infarct volume defined on diffusion-weighted imaging (DWI) in patients with large vessel occlusion and small core. Methods— Consecutive patients who were candidates for endovascular treatment (Alberta Stroke Program Early CT Score [ASPECTS] of ≥6 points) and who underwent both pretreatment multiphasic computed tomography angiography (mCTA) and multimodal magnetic resonance imaging were enrolled. The baseline early ischemic changes and collateral status were determined using both mCTA and magnetic resonance imaging–based collateral maps. Multivariable linear regression was used to evaluate adjusted estimates of the effect of collateral status on predicting MR DWI lesion volume before endovascular treatment. Results— Of 65 patients (39 men; median age, 76 years; median ASPECTS, 8 points [range, 6–10]), 10 (15.4%), 8 (12.3%), and 47 (72.3%) presented poor, intermediate, and good collaterals on mCTA, respectively. After adjusting for the initial stroke severity, ASPECTS, time to DWI, and mismatch volume, the mCTA collateral grade was the only factor independently associated with the DWI lesion volume (β=−35.657, SE mean=3.539; P 〈 0.0001). An excellent correlation between the mCTA- and magnetic resonance imaging-based collateral grades was observed (matching grade seen in 92.3%), suggesting a collateral status persistence during the hyperacute stroke phase. Conclusions— The mCTA assessed collateral adequacy is the sole predictor of eventual DWI lesion volume before endovascular treatment. The added value of collateral assessment in early ischemic changes and large vessel occlusion for decision-making regarding more aggressive revascularizations requires further evaluation. Clinical Trial Registration— URL: https://www.clinicaltrials.gov . Unique identifier: NCT03234634 and NCT02668627.
    Type of Medium: Online Resource
    ISSN: 0039-2499 , 1524-4628
    URL: Article
    DOI: 10.1161/STROKEAHA.119.026229
    RVK:
    XA 10000
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2019
    detail.hit.zdb_id: 1467823-8
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  • 2
    Online Resource
    Online Resource
    Evaluation of Diffusion Lesion Volume Measurements in Acute Ischemic Stroke Using Encoder-Decoder Convolutional Network
    Ovid Technologies (Wolters Kluwer Health) ; 2019
    In:  Stroke Vol. 50, No. 6 ( 2019-06), p. 1444-1451
    Details
    In: Stroke, Ovid Technologies (Wolters Kluwer Health), Vol. 50, No. 6 ( 2019-06), p. 1444-1451
    Abstract: Automatic segmentation of cerebral infarction on diffusion-weighted imaging (DWI) is typically performed based on a fixed apparent diffusion coefficient (ADC) threshold. Fixed ADC threshold methods may not be accurate because ADC values vary over time after stroke onset. Deep learning has the potential to improve the accuracy, provided that a large set of correctly annotated lesion data is used for training. The purpose of this study was to evaluate deep learning–based methods and compare them with commercial software in terms of lesion volume measurements. Methods— U-net, an encoder-decoder convolutional neural network, was adopted to train segmentation models. Two U-net models were developed: a U-net (DWI+ADC) model, trained on DWI and ADC data, and a U-net (DWI) model, trained on DWI data only. A total of 296 subjects were used for training and 134 for external validation. An expert neurologist manually delineated the stroke lesions on DWI images, which were used as the ground-truth reference. Lesion volume measurements from the U-net methods were compared against the expert’s manual segmentation and Rapid Processing of Perfusion and Diffusion (RAPID; iSchemaView Inc) analysis. Results— In external validation, U-net (DWI+ADC) showed the highest intraclass correlation coefficient with manual segmentation (intraclass correlation coefficient, 1.0; 95% CI, 0.99–1.00) and sufficiently high correlation with the RAPID results (intraclass correlation coefficient, 0.99; 95% CI, 0.98–0.99). U-net (DWI+ADC) and manual segmentation resulted in the smallest 95% Bland-Altman limits of agreement (−5.31 to 4.93 mL) with a mean difference of −0.19 mL. Conclusions— The presented deep learning–based method is fully automatic and shows a high correlation of diffusion lesion volume measurements with manual segmentation and commercial software. The method has the potential to be used in patient selection for endovascular reperfusion therapy in the late time window of acute stroke.
    Type of Medium: Online Resource
    ISSN: 0039-2499 , 1524-4628
    URL: Article
    DOI: 10.1161/STROKEAHA.118.024261
    RVK:
    XA 10000
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2019
    detail.hit.zdb_id: 1467823-8
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
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