Robot-Assisted Deep Venous Thrombosis Ultrasound Examination using Virtual Fixture

TL;DR

This work addresses operator-dependent variability in deep venous thrombosis ultrasound compression exams by introducing a semi-autonomous robot-assisted US system. It combines a novel hybrid force-motion control framework with a 6D path-based virtual fixture and a coarse-to-fine path planning pipeline that fuses external RGB-D cues with US image segmentation. An arc-length based path fitting method yields a continuous 6D trajectory that enables a robust path VF for clinician-guided interaction. Experimental validation on a human-like arm phantom demonstrates accurate force regulation, soft probe landing, and improved maintenance of the target vein within the US view, indicating potential for improved repeatability and comparability across examinations.

Abstract

Deep Venous Thrombosis (DVT) is a common vascular disease with blood clots inside deep veins, which may block blood flow or even cause a life-threatening pulmonary embolism. A typical exam for DVT using ultrasound (US) imaging is by pressing the target vein until its lumen is fully compressed. However, the compression exam is highly operator-dependent. To alleviate intra- and inter-variations, we present a robotic US system with a novel hybrid force motion control scheme ensuring position and force tracking accuracy, and soft landing of the probe onto the target surface. In addition, a path-based virtual fixture is proposed to realize easy human-robot interaction for repeat compression operation at the lesion location. To ensure the biometric measurements obtained in different examinations are comparable, the 6D scanning path is determined in a coarse-to-fine manner using both an external RGBD camera and US images. The RGBD camera is first used to extract a rough scanning path on the object. Then, the segmented vascular lumen from US images are used to optimize the scanning path to ensure the visibility of the target object. To generate a continuous scan path for developing virtual fixtures, an arc-length based path fitting model considering both position and orientation is proposed. Finally, the whole system is evaluated on a human-like arm phantom with an uneven surface.

Figures (10)

• Figure 1: An illustration of the DVT disease and the compression-release cycle of the DVT US exam.
• Figure 2: Workflow of the robot-assisted DVT exam. Left: algorithm blocks for deriving a 6D path-based virtual fixture. Right: an illustrative guide on performing the DVT US exam using the proposed system.
• Figure 3: An illustration of system setup and coordinate frames.
• Figure 4: A plot of force control law in Eq. (\ref{['eq:ctrl_law_force']}) w.r.t.$e_f$.
• Figure 5: Comparison results of the US sweep scan performance.