机器人CBCT与超声融合，实现无注册精准穿刺引导。
Robotic CBCT Meets Robotic Ultrasound
- 机器人系统预标定后动态配准，实现跨模态图像融合。
- 穿刺误差仅1.72±0.62mm，效率与成功率提升约50%。
- 适合需要高精度穿刺的临床场景，如肿瘤介入治疗。
多模态成像系统在现代临床实践中为安全精确的干预提供最优融合图像，例如用于穿刺引导的计算机断层扫描-超声（CT-US）导航。然而，现有成像设备灵活性和移动性有限，阻碍其融入标准工作流程，并限制全自动干预系统的进展。本文提出一种新型临床方案，将机器人锥形束计算机断层扫描（CBCT）与机器人超声（US）预先标定并动态配准，支持新临床应用。该系统实现无需注册的刚性配准，在无组织变形条件下促进多模态引导操作。首先进行一次系统间预标定；为确保穿刺路径安全，利用自动生成的多普勒信号作为提示，通过SAM2从B模式图像中分割血管，并将其映射至3D CBCT，生成细节丰富的融合图像。为验证系统性能，使用特制仿体，包含被肋骨覆盖的病灶及模拟流动的多条血管。结果显示，超声与CBCT间的映射平均偏差为1.72±0.62 mm。用户研究证实，基于CBCT-US融合的穿刺引导显著提升时间效率、准确性和成功率，相比传统超声引导流程，穿刺表现提升约50%。本研究首次构建面向临床应用的机器人双模态成像系统，结果表明其性能显著优于传统手动干预。
原文摘要 · Abstract (English)
The multi-modality imaging system offers optimal fused images for safe and precise interventions in modern clinical practices, such as computed tomography - ultrasound (CT-US) guidance for needle insertion. However, the limited dexterity and mobility of current imaging devices hinder their integration into standardized workflows and the advancement toward fully autonomous intervention systems. In this paper, we present a novel clinical setup where robotic cone beam computed tomography (CBCT) and robotic US are pre-calibrated and dynamically co-registered, enabling new clinical applications. This setup allows registration-free rigid registration, facilitating multi-modal guided procedures in the absence of tissue deformation. First, a one-time pre-calibration is performed between the systems. To ensure a safe insertion path by highlighting critical vasculature on the 3D CBCT, SAM2 segments vessels from B-mode images, using the Doppler signal as an autonomously generated prompt. Based on the registration, the Doppler image or segmented vessel masks are then mapped onto the CBCT, creating an optimally fused image with comprehensive detail. To validate the system, we used a specially designed phantom, featuring lesions covered by ribs and multiple vessels with simulated moving flow. The mapping error between US and CBCT resulted in an average deviation of 1.72+-0.62 mm. A user study demonstrated the effectiveness of CBCT-US fusion for needle insertion guidance, showing significant improvements in time efficiency, accuracy, and success rate. Needle intervention performance improved by approximately 50% compared to the conventional US-guided workflow. We present the first robotic dual-modality imaging system designed to guide clinical applications. The results show significant performance improvements compared to traditional manual interventions.
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