BackgroundCT perfusion (CTP) infarct and penumbra estimations determine the eligibility of patients with acute ischemic stroke (AIS) for endovascular intervention. This study aimed to determine volumetric and spatial agreement of predicted RAPID, Vitrea, and Sphere CTP infarct with follow-up fluid attenuation inversion recovery (FLAIR) MRI infarct.Methods108 consecutive patients with AIS and large vessel occlusion were included in the study between April 2019 and January 2020 . Patients were divided into two groups: endovascular intervention (n=58) and conservative treatment (n=50). Intervention patients were treated with mechanical thrombectomy and achieved successful reperfusion (Thrombolysis in Cerebral Infarction 2b/2 c/3) while patients in the conservative treatment group did not receive mechanical thrombectomy or intravenous thrombolysis. Intervention and conservative treatment patients were included to assess infarct and penumbra estimations, respectively. It was assumed that in all patients treated conservatively, penumbra converted to infarct. CTP infarct and penumbra volumes were segmented from RAPID, Vitrea, and Sphere to assess volumetric and spatial agreement with follow-up FLAIR MRI.ResultsMean infarct differences (95% CIs) between each CTP software and FLAIR MRI for each cohort were: intervention cohort: RAPID=9.0±7.7 mL, Sphere=−0.2±8.7 mL, Vitrea=−7.9±8.9 mL; conservative treatment cohort: RAPID=−31.9±21.6 mL, Sphere=−26.8±17.4 mL, Vitrea=−15.3±13.7 mL. Overlap and Dice coefficients for predicted infarct were (overlap, Dice): intervention cohort: RAPID=(0.57, 0.44), Sphere=(0.68, 0.60), Vitrea=(0.70, 0.60); conservative treatment cohort: RAPID=(0.71, 0.56), Sphere=(0.73, 0.60), Vitrea=(0.72, 0.64).ConclusionsSphere proved the most accurate in patients who had intervention infarct assessment as Vitrea and RAPID overestimated and underestimated infarct, respectively. Vitrea proved the most accurate in penumbra assessment for patients treated conservatively although all software overestimated penumbra.

BACKGROUND AND PURPOSE: Brain CTP is used to estimate infarct and penumbra volumes to determine endovascular treatment eligibility for patients with acute ischemic stroke. We aimed to assess the accuracy of a Bayesian CTP algorithm in determining penumbra and final infarct volumes. MATERIALS AND METHODS: Data were retrospectively collected for 105 patients with acute ischemic stroke (55 patients with successful recanalization [TICI 2b/2c/3] and large-vessel occlusions and 50 patients without interventions). Final infarct volumes were calculated using DWI and FLAIR 24 hours following CTP imaging. RAPID and the Vitrea Bayesian CTP algorithm (with 3 different settings) predicted infarct and penumbra volumes for comparison with final infarct volumes to assess software performance. Vitrea settings used different combinations of perfusion maps (MTT, TTP, CBV, CBF, delay time) for infarct and penumbra quantification. Patients with and without interventions were included for assessment of predicted infarct and penumbra volumes, respectively. RESULTS: RAPID and Vitrea default setting had the most accurate final infarct volume prediction in patients with interventions ([Spearman correlation coefficient, mean infarct difference] default versus FLAIR:

BackgroundAngiographic parametric imaging (API) is an imaging method that uses digital subtraction angiography (DSA) to characterize contrast media dynamics throughout the vasculature. This requires manual placement of a region of interest over a lesion (eg, an aneurysm sac) by an operator.ObjectiveThe purpose of our work was to determine if a convolutional neural network (CNN) was able to identify and segment the intracranial aneurysm (IA) sac in a DSA and extract API radiomic features with minimal errors compared with human user results.MethodsThree hundred and fifty angiographic images of IAs were retrospectively collected. The IAs and surrounding vasculature were manually contoured and the masks put to a CNN tasked with semantic segmentation. The CNN segmentations were assessed for accuracy using the Dice similarity coefficient (DSC) and Jaccard index (JI). Area under the receiver operating characteristic curve (AUROC) was computed. API features based on the CNN segmentation were compared with the human user results.ResultsThe mean JI was 0.823 (95% CI 0.783 to 0.863) for the IA and 0.737 (95% CI 0.682 to 0.792) for the vasculature. The mean DSC was 0.903 (95% CI 0.867 to 0.937) for the IA and 0.849 (95% CI 0.811 to 0.887) for the vasculature. The mean AUROC was 0.791 (95% CI 0.740 to 0.817) for the IA and 0.715 (95% CI 0.678 to 0.733) for the vasculature. All five API features measured inside the predicted masks were within 18% of those measured inside manually contoured masks.ConclusionsCNN segmentation of IAs and surrounding vasculature from DSA images is non-inferior to manual contours of aneurysms and can be used in parametric imaging procedures.