3D ultrasound registration-based visual servoing for neurosurgical navigation

  • Oliver Zettinig
  • Benjamin Frisch
  • Salvatore Virga
  • Marco Esposito
  • Anna Rienmüller
  • Bernhard Meyer
  • Christoph Hennersperger
  • Yu-Mi Ryang
  • Nassir Navab
Original Article
  • 192 Downloads

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.

Keywords

Registration-based visual servoing 3D Ultrasound Neurosurgical navigation Needle insertion 

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Copyright information

© CARS 2017

Authors and Affiliations

  • Oliver Zettinig
    • 1
    • 2
  • Benjamin Frisch
    • 1
    • 2
  • Salvatore Virga
    • 1
    • 2
  • Marco Esposito
    • 1
    • 2
  • Anna Rienmüller
    • 3
    • 4
  • Bernhard Meyer
    • 3
  • Christoph Hennersperger
    • 1
    • 2
  • Yu-Mi Ryang
    • 3
  • Nassir Navab
    • 1
    • 2
  1. 1.Computer Aided Medical ProceduresTechnische Universität MünchenGarchingGermany
  2. 2.Computer Aided Medical ProceduresJohns Hopkins UniversityBaltimoreUSA
  3. 3.Neurochirurgische Klinik und PoliklinikTechnische Universität München, Klinikum rechts der IsarMünchenGermany
  4. 4.Department of Orthopedic SurgeryMedical University Vienna, General HospitalViennaAustria

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