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Background: Computed tomography perfusion (CTP)-estimated core volume is associated with functional outcomes in acute ischemic stroke. This relationship might differ among patients, depending on brain volume. Materials and Methods: We retrospectively included patients from the MR CLEAN Registry. Cerebrospinal fluid (CSF) and intracranial volume (ICV) were automatically segmented on NCCT. We defined the proportion of the ICV and total brain volume (TBV) affected by the ischemic core as ICVcore and TBVcore. Associations between the core volume, ICVcore, TBVcore, and functional outcome are reported per interquartile range (IQR). We calculated the area under the curve (AUC) to assess diagnostic accuracy. Results: In 200 patients, the median core volume was 13 (5–41) mL. Median ICV and TBV were 1377 (1283–1456) mL and 1108 (1020–1197) mL. Median ICVcore and TBVcore were 0.9 (0.4–2.8)% and 1.7 (0.5–3.6)%. Core volume (acOR per IQR 0.48 [95%CI 0.33–0.69]), ICVcore (acOR per IQR 0.50 [95%CI 0.35–0.69]), and TBVcore (acOR per IQR 0.41 95%CI 0.33–0.67]) showed a lower likelihood of achieving improved functional outcomes after 90 days. The AUC was 0.80 for the prediction of functional independence at 90 days for the CTP-estimated core volume, the ICVcore, and the TBVcore. Conclusion: Correcting the CTP-estimated core volume for the intracranial or total brain volume did not improve the association with functional outcomes in patients who underwent EVT.

Computed tomography perfusion (CTP) is frequently used in the triage of ischemic stroke patients for endovascular thrombectomy (EVT). We aimed to quantify the volumetric and spatial agreement of the CTP ischemic core estimated with different thresholds and follow-up MRI infarct volume on diffusion-weighted imaging (DWI). Patients treated with EVT between November 2017 and September 2020 with available baseline CTP and follow-up DWI were included. Data were processed with Philips IntelliSpace Portal using four different thresholds. Follow-up infarct volume was segmented on DWI. In 55 patients, the median DWI volume was 10 mL, and median estimated CTP ischemic core volumes ranged from 10–42 mL. In patients with complete reperfusion, the intraclass correlation coefficient (ICC) showed moderate-good volumetric agreement (range 0.55–0.76). A poor agreement was found for all methods in patients with successful reperfusion (ICC range 0.36–0.45). Spatial agreement (median Dice) was low for all four methods (range 0.17–0.19). Severe core overestimation was most frequently (27%) seen in Method 3 and patients with carotid-T occlusion. Our study shows moderate–good volumetric agreement between ischemic core estimates for four different thresholds and subsequent infarct volume on DWI in EVT-treated patients with complete reperfusion. The spatial agreement was similar to other commercially available software packages.

Infarct volume (FIV) on follow-up diffusion-weighted imaging (FU-DWI) is only moderately associated with functional outcome in acute ischemic stroke patients. However, FU-DWI may contain other imaging biomarkers that could aid in improving outcome prediction models for acute ischemic stroke. We included FU-DWI data from the HERMES, ISLES, and MR CLEAN-NO IV databases. Lesions were segmented using a deep learning model trained on the HERMES and ISLES datasets. We assessed the performance of three classifiers in predicting functional independence for the MR CLEAN-NO IV trial cohort based on: (1) FIV alone, (2) the most important features obtained from a trained convolutional autoencoder (CAE), and (3) radiomics. Furthermore, we investigated feature importance in the radiomic-feature-based model. For outcome prediction, we included 206 patients: 144 scans were included in the training set, 21 in the validation set, and 41 in the test set. The classifiers that included the CAE and the radiomic features showed AUC values of 0.88 and 0.81, respectively, while the model based on FIV had an AUC of 0.79. This difference was not found to be statistically significant. Feature importance results showed that lesion intensity heterogeneity received more weight than lesion volume in outcome prediction. This study suggests that predictions of functional outcome should not be based on FIV alone and that FU-DWI images capture additional prognostic information.

Thrombus imaging characteristics are associated with treatment success and functional outcomes in stroke patients. However, assessing these characteristics based on manual annotations is labor intensive and subject to observer bias. Therefore, we aimed to create an automated pipeline for consistent and fast full thrombus segmentation. We used multi-center, multi-scanner datasets of anterior circulation stroke patients with baseline NCCT and CTA for training (n = 228) and testing (n = 100). We first found the occlusion location using StrokeViewer LVO and created a bounding box around it. Subsequently, we trained dual modality U-Net based convolutional neural networks (CNNs) to segment the thrombus inside this bounding box. We experimented with: (1) U-Net with two input channels for NCCT and CTA, and U-Nets with two encoders where (2) concatenate, (3) add, and (4) weighted-sum operators were used for feature fusion. Furthermore, we proposed a dynamic bounding box algorithm to adjust the bounding box. The dynamic bounding box algorithm reduces the missed cases but does not improve Dice. The two-encoder U-Net with a weighted-sum feature fusion shows the best performance (surface Dice 0.78, Dice 0.62, and 4% missed cases). Final segmentation results have high spatial accuracies and can therefore be used to determine thrombus characteristics and potentially benefit radiologists in clinical practice.