Purpose: The assessment of collateral status may depend on the timing of image acquisition. The purpose of this study is to investigate whether there are optimal time points in CT Perfusion (CTP) for collateral status assessment, and compare collaterals scores at these time points with collateral scores from multiphase CT angiography (mCTA). Methods: Patients with an acute intracranial occlusion who underwent baseline non-contrast CT, mCTA and CT perfusion were selected. Collateral status was assessed using an automatically computed Collateral Ratio (CR) score in mCTA, and predefined time points in CTP acquisition. CRs extracted from CTP were correlated with CRs from mCTA. In addition, all CRs were related to baseline National Institutes of Health Stroke Scale (NIHSS) and Alberta Stoke Program Early CT Score (ASPECTS) with linear regression analysis to find the optimal CR. Results: In total 58 subjects (median age 74 years; interquartile range 61–83 years; 33 male) were included. When comparing the CRs from the CTP vs. mCTA acquisition, the strongest correlations were found between CR from baseline mCTA and the CR at the maximal intensity projection of time-resolved CTP (r = 0.81) and the CR at the peak of arterial enhancement point (r = 0.78). Baseline mCTA-derived CR had the highest correlation with ASPECTS (β = 0.36 (95%CI 0.11, 0.61)) and NIHSS (β = − 0.48 (95%CI − 0.72, − 0.16)). Conclusion: Collateral status assessment strongly depends on the timing of acquisition. Collateral scores obtained from mCTA imaging is close to the optimal collateral score obtained from CTP imaging.

Thrombus perviousness is strongly associated with functional outcome and intravenous alteplase treatment success in patients with acute ischemic stroke. Accuracy of thrombus attenuation increase (TAI) assessment may be compromised by a heterogeneous thrombus composition and interobserver variations of currently used manual measurements. We hypothesized that TAI is more strongly associated with clinical outcomes when evaluated on the entire thrombus. In 195 patients, five TAI measures were performed: one manual by placing three regions of interest (TAI_manual) and four automated ones assessing densities from the entire thrombus. The automated TAI measures were calculated by comparing quartiles; Q1, Q2, and Q3 of the non-contrast and contrast enhanced thrombus density distribution and using the lag of the maximum of the cross correlations (MCC). Associations with functional outcome (mRS at 90 days) were assessed with univariate and multivariable analyses. All entire TAI measures were significantly associated with functional outcome with odd ratios (OR) of 1.63(95 %CI:1.19–2.25, p = 0.003) for Q1, 1.56(95 %CI:1.16–2.10, p = 0.003) for Q2, 1.24(95 %CI:1.00–1.54, p = 0.045) for Q3, and 1.70(95 %CI:1.24–2.34, p = 0.001) for MCC per 10 HU increase in univariate models. TAI_manual was not significantly associated with functional outcome (p = 0.055). In the multivariable logistic regression models including age, NIHSS, and recanalization, only TAI measures derived from the entire thrombus were independently associated with favorable outcome; OR of 1.64(95 %CI:1.01–2.66, p = 0.048) for Q2 and 1.82(1.13–2.95, p = 0.014) for MCC per 10 HU increase of thrombus attenuation. The novel perviousness measures of the entire thrombus are more strongly associated with functional outcome than the traditional manual perviousness assessments.

The Thrombolysis in Cerebral Infarction (TICI) score is an important metric for reperfusion therapy assessment in acute ischemic stroke. It is commonly used as a technical outcome measure after endovascular treatment (EVT). Existing TICI scores are defined in coarse ordinal grades based on visual inspection, leading to inter-and intra-observer variation. In this work, we present autoTICI, an automatic and quantitative TICI scoring method. First, each digital subtraction angiography (DSA) acquisition is separated into four phases (non-contrast, arterial, parenchymal and venous phase) using a multi-path convolutional neural network (CNN), which exploits spatio-temporal features. The network also incorporates sequence level label dependencies in the form of a state-transition matrix. Next, a minimum intensity map (MINIP) is computed using the motion corrected arterial and parenchymal frames. On the MINIP image, vessel, perfusion and background pixels are segmented. Finally, we quantify the autoTICI score as the ratio of reperfused pixels after EVT. On a routinely acquired multi-center dataset, the proposed autoTICI shows good correlation with the extended TICI (eTICI) reference with an average area under the curve (AUC) score of 0.81. The AUC score is 0.90 with respect to the dichotomized eTICI. In terms of clinical outcome prediction, we demonstrate that autoTICI is overall comparable to eTICI.

The collateral score is an important biomarker in decision making for endovascular treatment (EVT) of patients with ischemic stroke. The existing collateral grading systems are based on visual inspection and prone to subjective interpretation and interobserver variation. The purpose of our work is the development of an automatic collateral scoring method. In this work, we present a method that is inspired by human collateral scoring. Firstly, we define an anatomical region by atlas-based registration and extract vessel structures using a deep convolutional neural network. From this, high-level features based on the ratios of vessel length and volume of the occluded and the contralateral side are defined. Multi-class classification models are used to map the feature space to a four-grade collateral score and a quantitative score. The dataset used for training, validation and testing is from a registry of images acquired in clinical routine at multiple medical centers. The model performance is tested on 269 subjects, achieving an accuracy of 0.8. The dichotomized collateral score accuracy is 0.9. The error is comparable to the interobserver variation, the results are comparable to the performance of two radiologists with 10 to 30 years of experience.