Robotic Ultrasound-Guided Femoral Artery Reconstruction of Anatomically-Representative Phantoms

2025-03-09

AI Summary

Clinical challenges in femoral artery access—arising from anatomical variations, adipose tissue overlay, and limited precision of conventional ultrasound guidance—hinder reliable vascular access establishment. To address this, we propose the first fully automated robotic ultrasound scanning and bifurcated femoral artery 3D reconstruction framework tailored to individual anatomical complexity. Validated on five patient-specific phantoms derived from clinical CT scans, our system achieves autonomous, subject-specific ultrasound scanning for the first time. We introduce a novel temporal segmentation network for vascular ultrasound video—a U-Net variant—that enhances robustness in dynamic vessel structure extraction. Furthermore, we integrate CT-derived prior knowledge for centerline reconstruction and perform cross-modal registration between ultrasound and CT. Quantitative evaluation demonstrates superior performance: segmentation Dice score of 89.21% (IoU: 80.54%), centerline L2 error of 0.91 ± 0.70 mm, and Hausdorff distance of 4.36 ± 1.11 mm—significantly outperforming conventional approaches.

Technology Category

• Computer Vision: Segmentation
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Femoral artery access is essential for numerous clinical procedures, including diagnostic angiography, therapeutic catheterization, and emergency interventions. Despite its critical role, successful vascular access remains challenging due to anatomical variability, overlying adipose tissue, and the need for precise ultrasound (US) guidance. Errors in needle placement can lead to severe complications, restricting the procedure to highly skilled clinicians in controlled hospital settings. While robotic systems have shown promise in addressing these challenges through autonomous scanning and vessel reconstruction, clinical translation remains limited due to reliance on simplified phantom models that fail to capture human anatomical complexity. In this work, we present a method for autonomous robotic US scanning of bifurcated femoral arteries, and validate it on five vascular phantoms created from real patient computed tomography (CT) data. Additionally, we introduce a video-based deep learning US segmentation network tailored for vascular imaging, enabling improved 3D arterial reconstruction. The proposed network achieves a Dice score of 89.21% and an Intersection over Union of 80.54% on a newly developed vascular dataset. The quality of the reconstructed artery centerline is evaluated against ground truth CT data, demonstrating an average L2 deviation of 0.91+/-0.70 mm, with an average Hausdorff distance of 4.36+/-1.11mm. This study is the first to validate an autonomous robotic system for US scanning of the femoral artery on a diverse set of patient-specific phantoms, introducing a more advanced framework for evaluating robotic performance in vascular imaging and intervention.

Problem

• Autonomous robotic ultrasound scanning for femoral artery access.
• Improving 3D arterial reconstruction using deep learning segmentation.
• Validating robotic system on patient-specific vascular phantoms.

Innovation

• Autonomous robotic US scanning of femoral arteries
• Video-based deep learning US segmentation network
• Validation on patient-specific vascular phantoms