Automatic viewing angles prediction for endovascular treatment in intracranial aneurysm

Timely and accurate selection of working angles for 2D digital subtraction angiography (DSA) on biplane C-arms system is critical for successful endovascular treatments of intracranial saccular aneurysms. Image guidance under fluoroscopy acquired at these working angles ensure clear visualization of coil positioning and allow the detection of coil protrusion into the parent vessel. Currently, these angles are manually chosen, which can be time-consuming and subject to variability, particularly among less experienced clinicians. In this work, we propose an automatic method to predict two working angles using deep learning and rule-based geometric criteria. Aneurysms are first segmented from parent vessels using a point transformer-based model. The two working angles constituting the “neck” and “barrel” views are then predicted based on geometric criteria. For the neck view, a weighted voting strategy considers maximizing neck diameter, minimizing aneurysm-vessel overlap, minimizing vessel overlap, and maximizing neck shoulder clearance. For the barrel view, a similar strategy considers minimizing aneurysm-vessel overlap and optimizing parent vessel alignment. Finally, joint optimization ensures that all vessel branches are clearly visible in at least one view. The algorithm was evaluated on 84 aneurysm models from an open source dataset. The predicted angles showed strong agreement with ground truth annotated by an experienced neurosurgeon, with ≤ 20° differences in 75% of the neck views and 90% of the barrel views. The method has the potential for clinical integration to improve the efficiency and consistency in aneurysm treatment planning.