Vessel correspondence in pre-post-intervention DSA images of ischemic stroke patients
M.A. Berrospi (TU Delft - Mechanical Engineering)
Theo Van Walsum – Mentor (Erasmus MC)
F.G. te Nijenhuis – Mentor (Erasmus MC)
Ruisheng Su – Mentor (Erasmus MC)
F. Vos – Mentor (TU Delft - ImPhys/Computational Imaging)
Qian Tao – Graduation committee member
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Abstract
Ischemic stroke, a leading cause of death and disability worldwide, occurs when a blood vessel is occluded by a thrombus. Current therapies for ischemic stroke, include Intravenous thrombolysis (IVT) and Endovascular thrombectomy (EVT). EVT relies on a Thrombolysis in Cerebral Infarction (TICI) score for assessing treatment effectiveness. This score, based on the visual evaluation of medical images by physicians, suffers from inter- and intra-observer variability, as it is influenced by the individual rater’s judgment. Digital Subtraction Angiography (DSA) imaging is commonly utilized both before therapy to identify the occluded vessel and after therapy to evaluate the treatment outcome. Accurate vessel correspondence before and after treatment is crucial for a reliable assessment. To enhance current evaluation methods and address this challenging task, we propose two automated approaches for determining vessel correspondence in pre- and post-EVT DSA imaging. The proposed methods
utilize graphical representations of the cerebral vascular network and distinct matching procedures. We refer to the methods as registration-based vessel matching (RB-VM) and graph based vessel matching (GB-VM). The methods were evaluated using manually annotated data with the RB-VM and GB-VM methods achieving a recall of 82.7% (78.2; 85.7) and 51.3% (47.4; 54.4) respectively. This work marks a significant step towards automatic stroke therapy assessment and showcases the potential benefits of graph based algorithms for this task, paving the way for more reliable and objective treatment assessments.