Automatic centerline extraction of coronary arteries in coronary computed tomographic angiography

Coronary computed tomographic angiography (CCTA) is a non-invasive imaging modality for the visualization of the heart and coronary arteries. To fully exploit the potential of the CCTA datasets and apply it in clinical practice, an automated coronary artery extraction approach is needed. The purpose of this paper is to present and validate a fully automatic centerline extraction algorithm for coronary arteries in CCTA images. The algorithm is based on an improved version of Frangi’s vesselness filter which removes unwanted step-edge responses at the boundaries of the cardiac chambers. Building upon this new vesselness filter, the coronary artery extraction pipeline extracts the centerlines of main branches as well as side-branches automatically. This algorithm was first evaluated with a standardized evaluation framework named Rotterdam Coronary Artery Algorithm Evaluation Framework used in the MICCAI Coronary Artery Tracking challenge 2008 (CAT08). It includes 128 reference centerlines which were manually delineated. The average overlap and accuracy measures of our method were 93.7% and 0.30 mm, respectively, which ranked at the 1st and 3rd place compared to five other automatic methods presented in the CAT08. Secondly, in 50 clinical datasets, a total of 100 reference centerlines were generated from lumen contours in the transversal planes which were manually corrected by an expert from the cardiology department. In this evaluation, the average overlap and accuracy were 96.1% and 0.33 mm, respectively. The entire processing time for one dataset is less than 2 min on a standard desktop computer. In conclusion, our newly developed automatic approach can extract coronary arteries in CCTA images with excellent performances in extraction ability and accuracy

Predicting Poor Outcome Before Endovascular Treatment in Patients With Acute Ischemic Stroke

Background: Although endovascular treatment (EVT) has greatly improved outcomes in acute ischemic stroke, still one third of patients die or remain severely disabled after stroke. If we could select patients with poor clinical outcome despite EVT, we could prevent futile treatment, avoid treatment complications, and further improve stroke care. We aimed to determine the accuracy of poor functional outcome prediction, defined as 90-day modified Rankin Scale (mRS) score ≥5, despite EVT treatment. Methods: We included 1,526 patients from the MR CLEAN Registry, a prospective, observational, multicenter registry of ischemic stroke patients treated with EVT. We developed machine learning prediction models using all variables available at baseline before treatment. We optimized the models for both maximizing the area under the curve (AUC), reducing the number of false positives. Results: From 1,526 patients included, 480 (31%) of patients showed poor outcome. The highest AUC was 0.81 for random forest. The highest area under the precision recall curve was 0.69 for the support vector machine. The highest achieved specificity was 95% with a sensitivity of 34% for neural networks, indicating that all models contained false positives in their predictions. From 921 mRS 0–4 patients, 27–61 (3–6%) were incorrectly classified as poor outcome. From 480 poor outcome patients in the registry, 99–163 (21–34%) were correctly identified by the models. Conclusions: All prediction models showed a high AUC. The best-performing models correctly identified 34% of the poor outcome patients at a cost of misclassifying 4% of non-poor outcome patients. Further studies are necessary to determine whether these accuracies are reproducible before implementation in clinical practice

Association of follow-up infarct volume with functional outcome in acute ischemic stroke: a pooled analysis of seven randomized trials.

BACKGROUND: Follow-up infarct volume (FIV) has been recommended as an early indicator of treatment efficacy in patients with acute ischemic stroke. Questions remain about the optimal imaging approach for FIV measurement. OBJECTIVE: To examine the association of FIV with 90-day modified Rankin Scale (mRS) score and investigate its dependency on acquisition time and modality. METHODS: Data of seven trials were pooled. FIV was assessed on follow-up (12 hours to 2 weeks) CT or MRI. Infarct location was defined as laterality and involvement of the Alberta Stroke Program Early CT Score regions. Relative quality and strength of multivariable regression models of the association between FIV and functional outcome were assessed. Dependency of imaging modality and acquisition time (≤48 hours vs >48 hours) was evaluated. RESULTS: Of 1665 included patients, 83% were imaged with CT. Median FIV was 41 mL (IQR 14-120). A large FIV was associated with worse functional outcome (OR=0.88(95% CI 0.87 to 0.89) per 10 mL) in adjusted analysis. A model including FIV, location, and hemorrhage type best predicted mRS score. FIV of ≥133 mL was highly specific for unfavorable outcome. FIV was equally strongly associated with mRS score for assessment on CT and MRI, even though large differences in volume were present (48 mL (IQR 15-131) vs 22 mL (IQR 8-71), respectively). Associations of both early and late FIV assessments with outcome were similar in strength (ρ=0.60(95% CI 0.56 to 0.64) and ρ=0.55(95% CI 0.50 to 0.60), respectively). CONCLUSIONS: In patients with an acute ischemic stroke due to a proximal intracranial occlusion of the anterior circulation, FIV is a strong independent predictor of functional outcome and can be assessed before 48 hours, oneither CT or MRI