Synthesizing Strategies for Needle Steering in Gelatin Phantoms

Rogalla, Antje; Lehmann, Sascha; Neidhardt, Maximilian; Sprenger, Johanna; Bengs, Marcel; Schlaefer, Alexander; Schupp, Sibylle

doi:10.4204/eptcs.316.10

Cited by 3 publications

(6 citation statements)

References 19 publications

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“…4e, the CRs form a tunnel towards the TR instead. We conduct three types of experiments: In Experiment 1, we apply the Uppaal model to the data sets also used in [23] in R 2 (extended to R 3 with z = 0). For that purpose, we measure the deviation between the measured reference traces and the set of matched simulation traces, which are determined via offline synthesis of strategies leading through targets placed on the reference trace.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, we measure the consequences (i.e., readjustment and fewer TR reachings) as indicator for impacts on those requirements instead. 1: The deviation (min, avg, max) between measured reference traces [23] and matched simulation traces determined via offline strategy synthesis.…”

Section: Methodsmentioning

confidence: 99%

“…The paper and model build on and extend our previous work on needle steering and OSS. In "Synthesizing Strategies for Needle Steering in Gelatin Phantoms" [23], we provided a basic model for matching of R 2 -projected needle traces against a R 2 motion model via offline strategy synthesis. In the current paper, we extend the model to R 3 motion to fully reflect the real-world use case of needle steering, which all the more raises the question for sufficiently precise domain modeling to support formal verification in reasonable time.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modeling R^3 Needle Steering in Uppaal

Lehmann¹,

Rogalla²,

Neidhardt³

et al. 2022

Electron. Proc. Theor. Comput. Sci.

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling R^3 Needle Steering in Uppaal

Lehmann¹,

Rogalla²,

Neidhardt³

et al. 2022

Electron. Proc. Theor. Comput. Sci.

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“…Our approach is similar but applies to cognitive or physiological uncertainties. Specifically, we transfer the static model of timed game needle steering [24] based on a nonholonomic model [25] into a model that is adapted online.…”

Section: Preliminaries and Related Workmentioning

confidence: 99%

Online Strategy Synthesis for Safe and Optimized Control of Steerable Needles

Lehmann¹,

Rogalla²,

Neidhardt³

et al. 2021

Electron. Proc. Theor. Comput. Sci.

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Autonomous systems are often applied in uncertain environments, which require prospective action planning and retrospective data evaluation for future planning to ensure safe operation. Formal approaches may support these systems with safety guarantees, but are usually expensive and do not scale well with growing system complexity. In this paper, we introduce online strategy synthesis based on classical strategy synthesis to derive formal safety guarantees while reacting and adapting to environment changes. To guarantee safety online, we split the environment into region types which determine the acceptance of action plans and trigger local correcting actions. Using model checking on a frequently updated model, we can then derive locally safe action plans (prospectively), and match the current model against new observations via reachability checks (retrospectively). As use case, we successfully apply online strategy synthesis to medical needle steering, i.e., navigating a (flexible and beveled) needle through tissue towards a target without damaging its surroundings.

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“…This method improves the puncture accuracy, reduces the difficulty of surgery and the radiation hazard to medical personnel, and standardises the treatment process. It is widely used in the fields of spinal, brain and neurosurgery (Emerson et al, 2021; Rogalla et al, 2020). The needles used at present for conventional percutaneous puncture fall into three types: symmetric conical tip, asymmetric bevel tip and pre-bent tip, as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

A tube-based robust MPC for duty-cycled rotation needle steering systems with bounded disturbances

Luo

Zhang

2021

Transactions of the Institute of Measurement and Control

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In this paper, we aim to design the trajectory tracking controller for variable curvature duty-cycled rotation flexible needles with a tube-based model predictive control approach. A non-linear model is adopted according to the kinematic characteristics of the flexible needle and a bicycle method. The modeling error is assumed to be an unknown but bounded disturbance. The non-linear model is transformed to a discrete time form for the benefit of predictive controller design. From the application perspective, the flexible needle system states and control inputs are bounded within a robust invariant set when subject to disturbance. Then, the tube-based model predictive control is designed for the system with bounded state vector and inputs. Finally, the simulation experiments are carried out with tube-based model predictive control and proportional integral derivative controller based on the particle swarm optimisation method. The simulation results show that the tube-based model predictive control method is more robust and it leads to much smaller tracking errors in different scenarios.

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Synthesizing Strategies for Needle Steering in Gelatin Phantoms

Cited by 3 publications

References 19 publications

Modeling R^3 Needle Steering in Uppaal

Modeling R^3 Needle Steering in Uppaal

Online Strategy Synthesis for Safe and Optimized Control of Steerable Needles

A tube-based robust MPC for duty-cycled rotation needle steering systems with bounded disturbances

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