Anatomy- and Time-Based Post-Processing with CT Perfusion-Derived Time–Vessel Maps to Reduce Incorrect Occlusion Detections in Late-Phase CT Angiography
Year: 2026
Type: article
Abstract: Background/Objectives: Computer-aided vessel occlusion detection for acute ischemic stroke is typically developed on early-phase CT angiography (CTA). In late-phase CTA, enhanced venous contrast can mimic arterial occlusions and lead to incorrect occlusion detections. To counteract this, we propose a constraint-based post-processing framework that leverages anatomical and temporal vascular information from CT perfusion (CTP) scans. Methods: Vascular anatomy and relative contrast-arrival times were encoded as time–vessel maps. Three complementary constraints worked on these maps to remove incorrect detections based on distance-to-vessel truncation points, contrast-arrival time, and spatial priors. Constraints were fixed on a 32-case late-phase development subset and applied to an external held-out cohort of late-phase CTA (N = 354). To enable application without CTP, an auxiliary CTA-to-time–vessel map generator was designed to estimate maps directly from CTA. Results: In the external cohort, the framework reduced incorrect occlusions per scan from 3.9 (95% CI 3.6–4.2) to 2.1 (95% CI 1.9–2.3), preserving occlusion-level sensitivity at 80% (95% CI 69–91). The patient-level AUROC was 83 for the baseline versus 88 with post-processing. Improvements were also observed in detectors exposed to late-phase CTA, where post-processing reduced incorrect occlusions per scan from 2.8 to 1.9. As only 51 occlusion-positive cases were present in the external cohort, confidence intervals were wide. Consequently, these estimates should be interpreted with caution. Conclusions: Anatomically and temporally informed post-processing reduced incorrect occlusion detections in late-phase CTA without measurable loss of sensitivity, potentially improving robustness against CTA phase changes in external institutions.
Source: Diagnostics
Authors: Andrés Martínez Mora, Mahsa Mojtahedi, Lucas de Vries, Michael Baumgartner, Yannick Kirchhoff, Maximilian Zenk, Katharina Eckstein, Jessica Kächele, Gianluca Brugnara, Martin Bendszus, Alexander Radbruch, Yvo Roos, Wim van Zwam, Robert van Oostenbrugge, Diederik W. J. Dippel, Bart J. Emmer, Charles Majoie, Philipp Vollmuth, Clara I. Sánchez, Klaus H. Maier-Hein, Henk A. Marquering
Institutions: German Cancer Research Center, Heidelberg University, Amsterdam UMC Location University of Amsterdam, Technische Universität Darmstadt, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Neuroscience, Amsterdam Cardiovascular Sciences, Karlsruhe Institute of Technology, Deutsches Konsortium für Translationale Krebsforschung, University Hospital Bonn, University Hospital Heidelberg, University of Bonn, Maastricht University Medical Centre, Maastricht University, Erasmus MC, National Center for Tumor Diseases
Language: English
Subfield: Clinical Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7212915313