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Active localization and tracking of needle and target in robotic image-guided intervention systems

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Abstract

This paper describes a framework of algorithms for the active localization and tracking of flexible needles and targets during image-guided percutaneous interventions. The needle and target configurations are tracked by Bayesian filters employing models of the needle and target motions and measurements of the current system state obtained from an intra-operative imaging system which is controlled by an entropy-minimizing active localization algorithm. Versions of the system were built using particle and unscented Kalman filters and their performance was measured using both simulations and hardware experiments with real magnetic resonance imaging data of needle insertions into gel phantoms. Performance of the localization algorithms is given in terms of accuracy of the predictions and computational efficiency is discussed.

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Notes

  1. This choice of image plane configurations is not fundamental to the proposed method. Arbitrary image plane configurations can be used without any modification to the algorithm.

  2. The same measurement models are employed in both the simulation studies (Sect. 4) and the experiments with real magnetic resonance imaging data (Sect. 5).

  3. The simulation studies reported in this section, and the experiments using real magnetic-resonance imaging data reported in Sect. 5 employed the same algorithms and models, except for image processing. Specifically, no image processing was performed in simulation studies, as the simulation directly generated the output of the image processing algorithm; whereas, the experiments with real imaging data employed the image processing algorithms presented in Sect. 3.

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Acknowledgements

This work was supported in part by the National Science Foundation under grants CISE CNS-1035602 and IIS-1563805, and the National Institutes of Health under grant R01 EB018108.

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Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, USA

    Mark Renfrew & M. Cenk Çavuşoĝlu

  2. Departments of Radiology and Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA

    Mark Griswold

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  1. Mark Renfrew
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  2. Mark Griswold
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  3. M. Cenk Çavuşoĝlu
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Correspondence to Mark Renfrew.

Appendix

Appendix

This appendix presents the phases of needle motion in more detail. Needle motion consists of three phases: perturbation of the insertion point and propagation of the perturbation (Algorithm 7), axial insertion of the needle (Algorithm 8), and addition of random perturbations. Figure 10 shows an example of the axial insertion procedure.

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Fig. 10
figure 10

One step of axial needle insertion. The original spline (blue) is augmented with a spline extension (green) and a minimum-curvature final spline (red) is fitted (Color figure online)

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Renfrew, M., Griswold, M. & Çavuşoĝlu, M.C. Active localization and tracking of needle and target in robotic image-guided intervention systems. Auton Robot 42, 83–97 (2018). https://doi.org/10.1007/s10514-017-9640-2

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