Shape Completion and Real-Time Visualization in Robotic Ultrasound Spine Acquisitions

2025-08-12

AI Summary

Ultrasound imaging in spinal surgery suffers from shadow artifacts, obscuring deep vertebral structures; conventional CT–ultrasound registration relies on recent CT scans, struggles with inter-subject spinal curvature variations, and entails complex workflows, while existing offline shape completion methods lack reproducibility. This paper introduces the first integrated robotic ultrasound system with real-time deep learning–based shape completion: leveraging a pre-trained CT prior, it jointly optimizes robot control and ultrasound image processing to enable online vertebral surface extraction and complete 3D lumbar reconstruction. The system supports interactive visualization, automated re-scanning, and target navigation. Phantom experiments demonstrate sub-millimeter reconstruction accuracy and robustness across multiple scanning protocols. In vivo volunteer studies show significantly improved anatomical visualization quality, enhancing intraoperative navigation consistency and anatomical interpretability.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Ultrasound (US) imaging is increasingly used in spinal procedures due to its real-time, radiation-free capabilities; however, its effectiveness is hindered by shadowing artifacts that obscure deeper tissue structures. Traditional approaches, such as CT-to-US registration, incorporate anatomical information from preoperative CT scans to guide interventions, but they are limited by complex registration requirements, differences in spine curvature, and the need for recent CT imaging. Recent shape completion methods can offer an alternative by reconstructing spinal structures in US data, while being pretrained on large set of publicly available CT scans. However, these approaches are typically offline and have limited reproducibility. In this work, we introduce a novel integrated system that combines robotic ultrasound with real-time shape completion to enhance spinal visualization. Our robotic platform autonomously acquires US sweeps of the lumbar spine, extracts vertebral surfaces from ultrasound, and reconstructs the complete anatomy using a deep learning-based shape completion network. This framework provides interactive, real-time visualization with the capability to autonomously repeat scans and can enable navigation to target locations. This can contribute to better consistency, reproducibility, and understanding of the underlying anatomy. We validate our approach through quantitative experiments assessing shape completion accuracy and evaluations of multiple spine acquisition protocols on a phantom setup. Additionally, we present qualitative results of the visualization on a volunteer scan.

Problem

• Overcome shadowing artifacts in spinal ultrasound imaging
• Replace CT-to-US registration with real-time shape completion
• Enhance consistency and reproducibility in spine visualization

Innovation

• Robotic ultrasound for autonomous spine scans
• Deep learning-based real-time shape completion
• Interactive visualization with repeatable scan capability