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3D ultrasound registration-based visual servoing for neurosurgical navigation

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

We present a fully image-based visual servoing framework for neurosurgical navigation and needle guidance. The proposed servo-control scheme allows for compensation of target anatomy movements, maintaining high navigational accuracy over time, and automatic needle guide alignment for accurate manual insertions.

Method

Our system comprises a motorized 3D ultrasound (US) transducer mounted on a robotic arm and equipped with a needle guide. It continuously registers US sweeps in real time with a pre-interventional plan based on CT or MR images and annotations. While a visual control law maintains anatomy visibility and alignment of the needle guide, a force controller is employed for acoustic coupling and tissue pressure. We validate the servoing capabilities of our method on a geometric gel phantom and real human anatomy, and the needle targeting accuracy using CT images on a lumbar spine gel phantom under neurosurgery conditions.

Results

Despite the varying resolution of the acquired 3D sweeps, we achieved direction-independent positioning errors of \(0.35\pm 0.19\) mm and \(0.61^\circ \pm 0.45^\circ \), respectively. Our method is capable of compensating movements of around 25 mm/s and works reliably on human anatomy with errors of \(1.45\pm 0.78\) mm. In all four manual insertions by an expert surgeon, a needle could be successfully inserted into the facet joint, with an estimated targeting accuracy of \(1.33\pm 0.33\) mm, superior to the gold standard.

Conclusion

The experiments demonstrated the feasibility of robotic ultrasound-based navigation and needle guidance for neurosurgical applications such as lumbar spine injections.

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Notes

  1. Throughout this article, linear transformations and vectors are expressed in computer vision notation, i.e., using 4\(\times \)4 homogeneous matrices and 4\(\times \)1 vectors.

  2.  https://github.com/SalvoVirga/iiwa_stack.

  3.  http://www.ros.org/.

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Acknowledgements

We thank ImFusion GmbH, Munich, Germany, for providing their image processing framework and their continuous support, and the department of nuclear medicine at Klinikum Rechts der Isar for several CT acquisitions. Furthermore, we wish to thank Julia Rackerseder for the production of the used phantoms and Rüdiger Göbl for his assistance during experiments.

Funding This work was partially funded by the Bayerische Forschungsstiftung Award Number AZ-1072-13 (project RoBildOR).

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    Authors and Affiliations

    1. Computer Aided Medical Procedures, Technische Universität München, Boltzmannstr. 3, 85748, Garching, Germany

      Oliver Zettinig, Benjamin Frisch, Salvatore Virga, Marco Esposito, Christoph Hennersperger & Nassir Navab

    2. Computer Aided Medical Procedures, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA

      Oliver Zettinig, Benjamin Frisch, Salvatore Virga, Marco Esposito, Christoph Hennersperger & Nassir Navab

    3. Neurochirurgische Klinik und Poliklinik, Technische Universität München, Klinikum rechts der Isar, Ismaninger Str. 22, 81675, München, Germany

      Anna Rienmüller, Bernhard Meyer & Yu-Mi Ryang

    4. Department of Orthopedic Surgery, Medical University Vienna, General Hospital, Vienna, Austria

      Anna Rienmüller

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    1. Oliver Zettinig
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    Correspondence to Oliver Zettinig.

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    The authors declare that they have no conflict of interest.

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    Informed consent was obtained from all individual participants included in the volunteer study. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committees.

    Additional information

    Oliver Zettinig and Benjamin Frisch have contributed equally to this work. Yu-Mi Ryang and Nassir Navab have contributed equally to this work.

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    Zettinig, O., Frisch, B., Virga, S. et al. 3D ultrasound registration-based visual servoing for neurosurgical navigation. Int J CARS 12, 1607–1619 (2017). https://doi.org/10.1007/s11548-017-1536-2

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