Incorporating ADC temporal profiles to predict ischemic tissue fate in acute stroke.
نویسندگان
چکیده
Algorithms to predict ischemic tissue fate based on acute stroke MRI typically utilized data at a single time point. The goal of this study was to investigate the potential improvement in prediction accuracy when incorporating MRI diffusion data from multiple time points during acute phase to improve prediction accuracy. This study was carried out using MRI data from rats subjected to permanent, 60-min and 30-min of middle cerebral artery occlusion (MCAO). The sensitivity and specificity of prediction accuracy were calculated. In the permanent MCAO group, prediction with multiple time-point diffusion data improved sensitivity and specificity compared with prediction using a single time point. In the 60-min MCAO group, multiple time-point analysis improved specificity but decreased sensitivity compared to the single time-point analysis. In the 30-min MCAO group, multiple time-point analysis showed no statistically significant improvement in specificity and sensitivity compared with the single time point analysis. This is because reperfusion transiently or permanently reversed the decline in ADC values, resulting in increased uncertainty and thus decreased prediction accuracy. Incorporating this a priori information could further improve prediction accuracy in the reperfusion group. These findings suggest that incorporating MRI data from multiple time points could improve prediction accuracy under certain ischemic conditions.
منابع مشابه
Incorporating ADC temporal profiles in acute stroke to predict ischemic tissue fate
INTRODUCTION The mismatch between the perfusion and diffusion abnormality – widely considered to approximate the ischemic penumbra – indicates tissue at risk for infarction but potentially salvageable [1]. The perfusion-diffusion mismatch has been utilized to guide thrombolytic therapy and offers predictive value of ischemic tissue fate. Predictive models have employed multiple acute MRI parame...
متن کاملStatistical prediction of tissue fate in acute ischemic brain injury.
An algorithm was developed to statistically predict ischemic tissue fate on a pixel-by-pixel basis. Quantitative high-resolution (200 x 200 microm) cerebral blood flow (CBF) and apparent diffusion coefficient (ADC) were measured on acute stroke rats subjected to permanent middle cerebral artery occlusion and an automated clustering (ISODATA) technique was used to classify ischemic tissue types....
متن کاملMore accurate identification of reversible ischemic injury in human stroke by cerebrospinal fluid suppressed diffusion-weighted imaging.
BACKGROUND AND PURPOSE The apparent diffusion coefficient (ADC) derived from diffusion-weighted (DWI) MRI has been used to differentiate reversible from irreversible ischemic injury. However, the ADC can be falsely elevated by partial volume averaging of cerebrospinal fluid (CSF) with parenchyma, limiting the accuracy of this approach. This study tested the hypothesis that the accuracy of diffe...
متن کاملArtificial neural network prediction of ischemic tissue fate in acute stroke imaging.
Multimodal magnetic resonance imaging of acute stroke provides predictive value that can be used to guide stroke therapy. A flexible artificial neural network (ANN) algorithm was developed and applied to predict ischemic tissue fate on three stroke groups: 30-, 60-minute, and permanent middle cerebral artery occlusion in rats. Cerebral blood flow (CBF), apparent diffusion coefficient (ADC), and...
متن کاملQuantitative prediction of acute ischemic tissue fate using support vector machine.
Accurate and quantitative prediction of ischemic tissue fate could improve decision-making in the clinical treatment of acute stroke. The goal of the present study is to explore the novel use of support vector machine (SVM) to predict infarct on a pixel-by-pixel basis using only acute cerebral blood flow (CBF), apparent diffusion coefficient (ADC) MRI data. The efficacy of SVM prediction model ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Brain research
دوره 1458 شماره
صفحات -
تاریخ انتشار 2012