Search

Individualized treatment of acute stroke depends on the timely detection of ischemia and potentially salvageable tissue in the brain. Using functional MRI (fMRI), it is possible to characterize cerebral blood flow from blood-oxygen-level-dependent (BOLD) signals without the administration of exogenous contrast agents. In this study, we applied spatial independent component analysis to resting-state fMRI data of 37 stroke patients scanned within 24 hr of symptom onset, 17 of whom received follow-up scans the next day. Our analysis revealed "Hypoperfusion spatially-Independent Components" (HICs) whose spatial patterns of BOLD signal resembled regions of delayed perfusion depicted by dynamic susceptibility contrast MRI. These HICs were detected even in the presence of excessive patient motion, and disappeared following successful tissue reperfusion. The unique spatial and temporal features of HICs allowed them to be distinguished with high accuracy from other components in a user-independent manner (area under the curve = 0.93, balanced accuracy = 0.90, sensitivity = 1.00, and specificity = 0.85). Our study therefore presents a new, noninvasive method for assessing blood flow in acute stroke that minimizes interpretative subjectivity and is robust to severe patient motion., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Background and Purpose: In acute stroke, arterial-input-function (AIF) determination is essential for obtaining perfusion estimates with dynamic susceptibility-weighted contrast-enhanced magnetic resonance imaging (DSC-MRI). Standard DSC-MRI postprocessing applies single AIF selection, ie, global AIF. Physiological considerations, however, suggest that a multiple AIFs selection method would improve perfusion estimates to detect penumbral flow. In this study, we developed a framework based on comparable DSC-MRI and positron emission tomography (PET) images to compare the two AIF selection approaches and assess their performance in penumbral flow detection in acute stroke., Methods: In a retrospective analysis of 17 sub(acute) stroke patients with consecutive MRI and PET scans, voxel-wise optimized AIFs were calculated based on the kinetic model as derived from both imaging modalities. Perfusion maps were calculated based on the optimized-AIF using two methodologies: (1) Global AIF and (2) multiple AIFs as identified by cluster analysis. Performance of penumbral-flow detection was tested by receiver-operating characteristics (ROC) curve analysis, ie, the area under the curve (AUC)., Results: Large variation of optimized AIFs across brain voxels demonstrated that there is no optimal single AIF. Subsequently, the multiple-AIF method (AUC range over all maps: .82-.90) outperformed the global AIF methodology (AUC .72-.85) significantly., Conclusions: We provide PET imaging-based evidence that a multiple AIF methodology is beneficial for penumbral flow detection in comparison with the standard global AIF methodology in acute stroke., (Copyright © 2017 by the American Society of Neuroimaging.)

Background and Purpose: Identification of salvageable penumbra tissue by dynamic susceptibility contrast magnetic resonance imaging is a valuable tool for acute stroke patient stratification for treatment. However, prior studies have not attempted to combine the different perfusion maps into a predictive model. In this study, we established a multiparametric perfusion imaging model and cross-validated it using positron emission tomography perfusion for detection of penumbral flow., Methods: In a retrospective analysis of 17 subacute stroke patients with consecutive magnetic resonance imaging and H2O15 positron emission tomography scans, perfusion maps of cerebral blood flow, cerebral blood volume, mean transit time, time-to-maximum, and time-to-peak were constructed and combined using a generalized linear model (GLM). Both the GLM maps and the single perfusion maps alone were cross-validated with positron emission tomography-cerebral blood flow scans to predict penumbral flow on a voxel-wise level. Performance was tested by receiver-operating characteristics curve analysis, that is, the area under the curve, and the models' fits were compared using the likelihood ratio test., Results: The GLM demonstrated significantly improved model fit compared with each of the single perfusion maps ( P <1×e-5) and demonstrated higher performance, with an area under the curve of 0.91. However, the absolute difference between the performance of GLM and the best-performing single perfusion parameter (time-to-maximum) was relatively low (area under the curve difference =0.04)., Conclusions: Our results support a dynamic susceptibility contrast magnetic resonance imaging-based GLM as an improved model for penumbral flow prediction in stroke patients. With given perfusion maps, this model is a straightforward and observer-independent alternative for therapy stratification., (© 2017 American Heart Association, Inc.)