Modeling R^3 Needle Steering in Uppaal

Sascha Lehmann
Antje Rogalla
Maximilian Neidhardt
Anton Reinecke
Alexander Schlaefer
Sibylle Schupp

Medical cyber-physical systems are safety-critical, and as such, require ongoing verification of their correct behavior, as system failure during run time may cause severe (or even fatal) personal damage. However, creating a verifiable model often conflicts with other application requirements, most notably regarding data precision and model accuracy, as efficient model checking promotes discrete data (over continuous) and abstract models to reduce the state space. In this paper, we approach the task of medical needle steering in soft tissue around potential obstacles. We design a verifiable model of needle motion (implemented in Uppaal Stratego) and a framework embedding the model for online needle steering. We mitigate the conflict by imposing boundedness on both the data types, reducing from R^3 to Z^3 when needed, and the motion and environment models, reducing the set of allowed local actions and global paths. In experiments, we successfully apply the static model alone, as well as the dynamic framework in scenarios with varying environment complexity and both a virtual and real needle setting, where up to 100% of targets were reached depending on the scenario and needle.

In Clemens Dubslaff and Bas Luttik: Proceedings Fifth Workshop on Models for Formal Analysis of Real Systems (MARS 2022), Munich, Germany, 2nd April 2022, Electronic Proceedings in Theoretical Computer Science 355, pp. 40–59.
Published: 21st March 2022.

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