Metabolic abnormality in acute stroke-like lesion and its relationship with focal cerebral blood flow in patients with MELAS: Evidence from proton MR spectroscopy and arterial spin labeling

Background: and Purpose: The aim of this study was to investigate whether pseudo-continuous arterial spin-labeling (ASL) in combination with an appropriate cerebral blood flow (CBF) threshold can reliably detect tissue at-risk for infarction. Methods: The retrospective study included 50 patients with acute stroke in the middle cerebral artery (MCA) territory who underwent perfusion-weighted magnetic resonance imaging (PW-MRI) within 24 hr of symptom onset and again in the subacute phase. After image co-registration the core and mismatch were segmented on ASL maps. Receiver operating characteristic (ROC) curve analysis was performed to calculate the optimal ASL-CBF threshold. Volumes on PW-MRI were recorded based on their ASL-CBF threshold (≧19-, 20-, 21-, 22-, 23-, 24- and 25 sec). Then the correlation between the penumbral salvage area and infarct growth, defined as the size difference between the lesion on baseline PWI- and the baseline diffusion-weighted images, and the final infarct volume were determined. Results: ROC analysis showed that the optimal threshold was 21.2 mL/100 g/min (area under the curve 0.88). The correlation between infarct growth and the penumbra salvage volume was significantly better for PWI lesions defined by ASL≦21 mL/100 g/min (r=-0.66; p<0.001). In 9 patients without recanalization on MRI obtained in the subacute phase, the ASL≦21 mL/100 g/min threshold more closely predicted the final infarct volume (r=0.82; p=0.006). Conclusion: PWI (ASL≧21 ml/100 g/min) volumes may provide an approximation of the volume of tissue at-risk for infarction in patients with acute stroke in the MCA territory.

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Abstract WMP15: Hyperperfusion on Arterial Spin Labeling: Objective Decision Support Using Pattern Recognition

Stroke ◽

10.1161/str.47.suppl_1.wmp15 ◽

2016 ◽

Vol 47 (suppl_1) ◽

Author(s):

Fabien Scalzo ◽

Songlin Yu ◽

Sagar Patel ◽

David Liebeskind ◽

Danny Wang

Keyword(s):

Pattern Recognition ◽

Blood Flow ◽

Cerebral Blood Flow ◽

Decision Support ◽

Acute Stroke ◽

Arterial Spin Labeling ◽

Objective Measure ◽

Spin Labeling ◽

Hemorrhagic Transformation ◽

Stroke Patients

Background: Hyperperfusion detected on arterial spin labeling (ASL) images acquired after stroke onset has been shown to correlate with subsequent hemorrhagic transformation (HT). Presence of hyperperfusion is typically detected by visual review of arterial spin labeling cerebral blood flow (CBF). Such a review is subjective as it is challenged by inter-reader variability, noise, and lack of standard threshold. We present in this study a quantitative hyperperfusion detection model that can provide an objective decision support for the interpretation of ASL CBF maps and rapidly delineate hyperperfusion regions. Methods: ASL Cerebral blood flow (CBF) maps of acute stroke patients presenting with an occlusion in the MCA territory were coregistered to a standardized atlas space. To achieve reliable detection of ASL hyperperfusion, we formalize the problem as a nonlinear classification that relates regional voxel intensity values to the corresponding binary label (normal or hyperperfused). Our method takes into account the healthy contralateral hemisphere and its CBF intensity values during the determination of hyperperfusion of a voxel. Each input feature vector combines the regional intensity values at the voxel of interest, its contralateral matched region, and the distribution of the difference between them. Each input vector is associated to a label corresponding to the presence of hyperperfusion that was manually established by consensus between experts. The predicted hyperperfusion regions were compared to a groundtruth that manually established by two researchers. Results: A total of 361 ASL scans were collected from 221 patients (age=72±17 years; 45% males). Hyperperfusion was detected in 76 patients that were subsequently used in our analysis. An AUC of 83±5% was reached after a leave-one-out cross-validation, which corresponds to the accuracy in detecting hyperperfusion compared to manual delineation of hyperperfusion on ASL CBF maps. Conclusion: Pattern recognition based on a nonlinear regression can provide an accurate and objective measure of hyperperfusion on ASL CBF images and could therefore improve the detection of hemorrhagic transformation in acute stroke patients.

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