Robot-assisted automatic ultrasound calibration.

Abstract

Ultrasound (US) calibration is the process of determining the unknown transformation from a coordinate frame such as the robot's tooltip to the US image frame and is a necessary task for any robotic or tracked US system. US calibration requires submillimeter-range accuracy for most applications, but it is a time-consuming and repetitive task. We provide a new framework for automatic US calibration with robot assistance and without the need for temporal calibration. US calibration based on active echo (AE) phantom was previously proposed, and its superiority over conventional cross-wire phantom-based calibration was shown. In this work, we use AE to guide the robotic arm motion through the process of data collection; we combine the capability of the AE point to localize itself in the frame of the US image with the automatic motion of the robotic arm to provide a framework for calibrating the arm to the US image automatically. We demonstrated the efficacy of the automated method compared to the manual method through experiments. To highlight the necessity of frequent ultrasound calibration, it is demonstrated that the calibration precision changed from 1.67 to 3.20mm if the data collection is not repeated after a dismounting/mounting of the probe holder. In a large data set experiment, similar reconstruction precision of automatic and manual data collection was observed, while the time was reduced by 58%. In addition, we compared ten automatic calibrations with ten manual ones, each performed in 15min, and showed that all the automatic ones could converge in the case of setting the initial matrix as identity, while this was not achieved by manual data sets. Given the same initial matrix, the repeatability of the automatic was [0.46, 0.34, 0.80, 0.47] versus [0.42, 0.51, 0.98, 1.15]mm in the manual case for the US image four corners. The submillimeter accuracy requirement of US calibration makes frequent data collections unavoidable. We proposed an automated calibration setup and showed feasibility by implementing it for a robot tooltip to US image calibration. The automated method showed a similar reconstruction precision as well as repeatability compared to the manual method, while the time consumed for data collection was reduced. The automatic method also reduces the burden of data collection for the user. Thus, the automated method can be a viable solution for applications that require frequent calibrations.