Robot-Assisted Deep Venous Thrombosis Ultrasound Examination using Virtual Fixture

TL;DR

This paper introduces a robotic ultrasound system with a hybrid force control and virtual fixture to improve the accuracy and repeatability of deep venous thrombosis examinations, reducing operator dependency.

Contribution

The novel hybrid force motion control scheme and path optimization method enhance ultrasound probe positioning and imaging consistency in DVT diagnosis.

Findings

System achieves accurate force and position tracking.

Path optimization improves target visibility during scans.

System successfully tested on a human-like arm phantom.

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.