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Image Guidance for Intracranial Surgery with Supervisory-Control Robots

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Robotics in Neurosurgery

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Abstract

The final accuracy of a stereotactic procedure results not only from the intrinsic characteristics of the robotic assistant but also from many other components of the global workflow. It is named “application accuracy” and depends also on the spatial resolution of planning images, the quality of registration algorithms, and the mechanic properties of surgical devices other than the robot.

Moreover, it must be stressed that even a highly accurate stereotactic procedure will not result in an adequate diagnostic yield or in a successful treatment if it is supported by a weak strategy of implantation, not adequate to patient-specific clinical requirements.

The aim of the present chapter is to provide the reader with main concepts at the basis of both strategical and technical aspects in the field of stereotactic implantations assisted by supervisory-control robots.

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Change history

  • 14 December 2022

    Chapter 4 in: J. A. González Martínez, F. Cardinale (eds.), Robotics in Neurosurgery ,

Notes

  1. 1.

    This is not a mistake: a “robotic” procedure is not always “frame-less”!

  2. 2.

    The problem is not different with stereoscopic technologies, where a pair of slightly different 2D images (taken or reconstructed from different points of view) is presented to the user in a way that allows the perception of depth. These images are not really 3D: despite the illusion of three-dimensionality, the user cannot see the hidden face of the objects. Therefore, surgical microscopes, endoscopes, and otoscopes provide the neurosurgeon with a stereoscopic view of the surgical field, and the term 3D should not be considered appropriate even if it is very popular.

  3. 3.

    A patient-to-atlas registration is common when a target is not directly visible in the patient images, as in the case of some DBS procedures. Other types of inter-subject registrations are never performed in surgical planning, but only in research studies registering different subjects to a common template.

  4. 4.

    Non-linear transformations are rarely adopted in the context of intracranial stereotaxic planning.

  5. 5.

    Many and more complex aspects of image registrations should be covered for special processing workflows such as the ones to estimate activations with functional MRI or white matter tracts from diffusion-weighted images datasets, but this is out of the scopes of the present chapter. Here we want to remain in the context of image registrations in the last mile of surgical planning.

  6. 6.

    Differently from image-to-image registrations, an image-to-patient registration includes only the computation of a rigid transformation. In fact, the surgical space is physical, it is not a discretized image space and no interpolation is required.

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Acknowledgements

We like to express or gratitude to all our coworkers from the teams of “Claudio Munari Center,” “Neuroradiology,” “Anesthesiology,” “Pediatrics,” “Pathology,” “Medical Physics,” “Clinical Engineering,” and “Information Technology” departments at Niguarda Hospital.

We would like to thank Gloria Innocenti for her contribution to bibliographic research and document retrieval, and all our friends from Politecnico di Milano and University of Genova for their constant collaboration and help in the field of biomedical engineering.

We want to thank also our close friends, Angela Catapano and Umberto Piccolo, for providing us with the drawing and the photos included in Fig. 4.2.

Finally, and most important, we would like to thank Claudio Munari, our mentor, who taught us mainly the value of team working among innumerable other things.

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Authors and Affiliations

  1. “Claudio Munari” Center for Epilepsy Surgery, Azienda Socio-Sanitaria Territoriale Grande Ospedale Metropolitano Niguarda, Milano, Italy

    Francesco Cardinale, Martina Revay, Piergiorgio d’Orio, Khalid Al Orabi & Giorgio Lo Russo

  2. Department of Medicine and Surgery, Unit of Neuroscience, Università degli Studi di Parma, Parma, Italy

    Francesco Cardinale & Piergiorgio d’Orio

  3. Department of Biomedical and Clinical Sciences “L. Sacco”, Università degli Studi di Milano, Milano, Italy

    Martina Revay

  4. Department of Neuroradiology, Azienda Socio-Sanitaria Territoriale Grande Ospedale Metropolitano Niguarda, Milano, Italy

    Sergio Raspante

  5. Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, Milano, Italy

    Lorenzo Maria Giuseppe Bianchi

  6. Department of Neurosurgery, King Abdullah Medical City, Al Mashair, Saudi Arabia

    Khalid Al Orabi

  7. Department of Medical Physics, Azienda Socio-Sanitaria Territoriale Grande Ospedale Metropolitano Niguarda, Milano, Italy

    Luca Berta

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  1. Francesco Cardinale
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Correspondence to Francesco Cardinale .

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Editors and Affiliations

  1. Department of Neurological Surgery and Epilepsy Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA

    Jorge Alvaro González Martínez

  2. “Claudio Munari” Center for Epilepsy Surgery, Azienda Socio-Sanitaria Territoriale Grande Ospedale Metropolitano Niguarda, Milano, Italy

    Francesco Cardinale

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Cardinale, F. et al. (2022). Image Guidance for Intracranial Surgery with Supervisory-Control Robots. In: González Martínez, J.A., Cardinale, F. (eds) Robotics in Neurosurgery. Springer, Cham. https://doi.org/10.1007/978-3-031-08380-8_4

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