Background and Purpose Large admission DWI lesion volumes are associated with poor outcomes despite acute stroke treatment. The primary aims of our study were to determine whether CTA collaterals correlate with admission DWI lesion volumes in patients with AIS with proximal occlusions, and whether a CTA collateral profile could identify large DWI volumes with high specificity. Materials and Methods We studied 197 patients with AIS with M1 and/or intracranial ICA occlusions. We segmented admission and follow-up DWI lesion volumes, and categorized CTA collaterals by using a 5-point CS system. ROC analysis was used to determine CS accuracy in predicting DWI lesion volumes >100 mL. Patients were dichotomized into 2 categories: CS = 0 (malignant profile) or CS>0. Univariate and multivariate analyses were performed to compare imaging and clinical variables between these 2 groups Results There was a negative correlation between CS and admission DWI lesion volume rho=−0.54, p<0.0001). ROC analysis revealed that CTA CS was a good discriminator of DWI lesion volume >100 mL (AUC=0.84, p<0.001). CS = 0 had 97.6% specificity and 54.5% sensitivity for DWI volume >100 mL. CS = 0 patients had larger mean admission DWI volumes (165.8 mL versus 32.7 mL, P <0.001), higher median NIHSS scores (21 versus 15, P <0.001), and were more likely to become functionally dependent at 3 months (95.5% versus 64.0%, P =0.003). Admission NIHSS score was the only independent predictor of a malignant CS (P=0.007). Conclusion In patients with AIS with PAOs, CTA collaterals correlate with admission DWI infarct size. A malignant collateral profile is highly specific for large admission DWI lesion size and poor functional outcome.

BACKGROUND AND PURPOSE:To safeguard patient health, there is great interest in CT radiation-dose reduction. The purpose of this study was to evaluate the impact of an iterative-reconstruction algorithm, ASIR, on image-quality measures in reduced-dose head CT scans for adult patients.

Purpose To characterize the spatial pattern of cerebral ischemic vulnerability to hypoperfusion in stroke patients. Methods We included 90 patients who underwent admission CT perfusion (CTP) and MRI within 12 hours of ischemic stroke onset. Infarcted brain lesions (“core”) were segmented from admission diffusion-weighted-imaging (DWI), and - along with the CTP parameter maps - coregistered onto MNI-152 brain space, which was parcellated into 125 mirror cortical and subcortical regions per hemisphere. We tested the hypothesis that the percent infarction increment per unit relative cerebral blood flow (rCBF) reduction differs statistically between regions using regression analysis to assess the interaction between regional rCBF and region variables. Next, for each patient, a “vulnerability index” (VI) map was constructed with voxel values equaling the product of that voxel’s rCBF and infarction probability (derived from the MNI-152-transformed, binary, segmented DWI lesions). Voxel-based rCBF threshold for core was determined within the upper 20th percentile of VI map voxel values. Results Different regions had different percent infarction increase per unit rCBF reduction (p=0.001). The caudate body, putamen, insular ribbon, paracentral lobule, precentral, middle and inferior frontal gyri had the highest ischemic vulnerability to hypoperfusion. A voxel-based rCBF threshold of <0.42 optimally distinguished infarct core in the highly-vulnerable regions, whereas rCBF <0.16 distinguished core in the remainder of the brain. Conclusion We demonstrated regional ischemic vulnerability of the brain to hypoperfusion in acute stroke patients. Location specific - rather than whole-brain - rCBF thresholds may provide a more accurate metric for estimating infarct core using CTP maps.

Purpose To construct a multivariate model for prediction of early aphasia improvement in stroke patients using admission CT perfusion (CTP) and CT angiography (CTA). Methods Fifty-eight consecutive patients with aphasia due to first-time ischemic stroke of the left hemisphere were included. Language function was assessed based on patients’ admission and discharge NIHSS and clinical records. All patients had brain CTP and CTA within 9 hours of symptom onset. For image analysis, all CTPs were automatically coregistered to MNI-152 brain space and parcellated into mirrored cortical and subcortical regions. Multiple logistic regression analysis was used to find independent imaging and clinical predictors of language recovery. Results By the time of discharge, 21 (36%) patients demonstrated improvement of language. Independent factors predicting improvement in language included relative cerebral blood flow of angular gyrus gray matter (Brodmann’s area 39) and lower third of insular ribbon, proximal cerebral artery occlusion on admission CTA, and aphasia score on admission NIHSS exam. Using these 4 variables, we developed a multivariate logistic regression model that could estimate the probability of early improvement in stroke patients presenting with aphasia and predict functional outcome with 91% accuracy. Conclusion An imaging-based location weighted multivariate model is developed to predict early language improvement of aphasic patients using admission data collected within 9-hours of stroke onset. This pilot model should be validated in a larger, prospective study; however, the semi-automated atlas-based analysis of brain CTP, along with the statistical approach, could be generalized for prediction of other outcome measures in stroke patients.

Background: The utility of admission CT perfusion (CTP) to that of diffusion-weighted imaging (DWI) as a predictor of hemorrhagic transformation (HT) in acute stroke was compared. Methods: We analyzed the admission CTP and DWI scans of 96 consecutive stroke patients. HT was present in 22 patients (23%). Infarct core was manually segmented on the admission DWI. We determined the: (1) hypoperfused tissue volume in the ischemic hemisphere using a range of thresholds applied to multiple different CTP parameter maps, and (2) mean relative CTP (rCTP) voxel values within both the DWI-segmented lesions and the thresholded CTP parameter maps. Receiver operating characteristic area under curve (AUC) analysis and multivariate regression were used to evaluate the test characteristics of each set of volumes and mean rCTP parameter values as predictors of HT. Results: The hypoperfused tissue volumes with either relative cerebral blood flow (rCBF) <0.48 (AUC = 0.73), or relative mean transit time (rMTT) >1.3 (AUC = 0.70), had similar accuracy to the DWI-segmented core volume (AUC = 0.68, p = 0.2 and p = 0.1, respectively) as predictors of HT. The mean rMTT voxel values within the rMTT >1.3 segmented lesion (AUC = 0.71) had similar accuracy to the mean rMTT voxel values (AUC = 0.65, p = 0.24) and mean rCBF voxel values (AUC = 0.64, p = 0.22) within the DWI-segmented lesion. The only independent predictors of HT were: (1) mean rMTT with rMTT >1.3, and (2) mechanical thrombectomy. Conclusion: Admission CTP-based hypoperfused tissue volumes and thresholded mean voxel values are markers of HT in acute stroke, with similar accuracy to DWI. This could be of value when MRI cannot be obtained.