Purpose : Stroke remains a leading cause of morbidity and mortality worldwide, despite advances in treatment modalities. Endovascular thrombectomy (EVT), a revolutionary intervention for ischemic stroke, is limited by its reliance on 2D fluoroscopic imaging, which lacks depth and comprehensive vascular detail. We propose a novel AI-driven pipeline for 3D CTA to 2D DSA cross-modality registration, termed DeepIterReg. Methods : The proposed pipeline integrates neural network-based initialization with iterative optimization to align pre-intervention and peri-intervention data. Our approach addresses the challenges of cross-modality alignment, particularly in scenarios involving limited shared vascular structures, by leveraging synthetic data, vein-centric anchoring, and differentiable rendering techniques. Results : We assess the efficacy of DeepIterReg through quantitative analysis of capture ranges and registration accuracy. Results show that our method can accurately register 70% of a test set of 20 patients and can improve capture ranges when performing an initial pose estimation using a convolutional neural network. Conclusions : DeepIterReg demonstrates promising performance for 3D-to-2D stroke intervention image registration, potentially aiding clinicians by improving spatial understanding during EVT and reducing dependence on manual adjustments.

The efficacy of endovascular therapy in large vessel occlusion (LVO) of the anterior circulation is dependent to a high degree on the selection of patients who are likely to benefit from this procedure. To this end, functional outcome prediction based on clinical parameters is an active area of research. In the preoperative screening of LVO patients, CT-Angiography (CTA) imaging is commonly acquired. We compare the functional outcome prediction performance of multiple deep learning based classifiers with multiple conventional methods, including the clinically validated MR PREDICTS decision tool. Using a dataset composed of 1929 preprocedural CTA images combined with clinical data, we compare a clinical baseline model with an imaging based pipeline and a combined pipeline. For the imaging model backbone we train various state-of-the-art architectures (Med3D, Vision Transformer, Voxel Transformer). These models are used to predict dichotomized modified Rankin Scale score 90 days after mechanical thrombectomy. Binary classifier outcomes are quantified using Area-Under the receiver operating characteristic Curve (AUC). The activation maps of the best performing image based model are further investigated using the GradCAM++ posthoc visualization method. Combining clinical features with information extracted from CTA images does not significantly improve the performance of functional outcome prediction methods compared to the baseline model. The information extracted from the images does not seem to
be complementary to the clinical features. This multimodal technique can however replace radiologically derived biomarkers, as its performance is non-inferior.