NERVE: A Near-Real-Time Three-Dimensional Reconstruction Approach for Interventional Devices and Surrounding Vessels Using a Monoplane C-arm Cone-Beam CT

第一作者

Real-time three-dimensional (3D) reconstruction of interventional devices, such as stents and embolization coils, together with surrounding vessels, using a monoplane C-arm cone-beam CT (CBCT) has emerged as a critical capability in modern neurovascular interventions. Due to the slow gantry rotation speed of current CBCT systems, real-time 3D recon struction of interventional devices and surrounding vessels has never been accomplished. In this work, we propose a framework for near-real-time 3D reconstruction of interventional devices and vasculature, termed NERVE, which integrates deep structure extraction (DSE) model and deep backprojection filtration (DBF) model together to accomplish 3D reconstruction of interventional devices and vasculature using data acquired at three projection views over a 60◦ angular range. DSE is trained to extract targets from acquired projection data by removing irrelevant anatomical background. DBF is trained to transform direction backpro jection image volumes to final 3D image volumes accordingly. DBF is designed to learn prior regularization that captures the distribution-level characteristics of ideal interventional devices and vasculature to eliminating limited view angle artifacts and high-attenuation-induced artifacts. Extensive numerical simula tions (1000 samples for testing) validate the feasibility and effec tiveness of NERVE. NERVE explicitly leverages subject-specific measured data, ensuring near-real-time 3D reconstruction with high fidelity, potentially facilitating image-guided interventional navigation for patients with neurovascular diseases.

工具：Interventional Devices; Real-Time Three Dimensional Reconstruction; Deep Learning