Abstract
The purpose of the paper is to formulate the optimum robotic surgical framework to perform a high-precision neurosurgery. The DICOM data tags are meticulously identified and used to build the optimum robotic surgical framework to develop accurate neuro-registration and neuronavigation. Algorithm along with GUI is developed for neuro-registration and neuronavigation. The different Unique Identifiers (UIDs) of DICOM are studied and all the slices of the various views are classified and stored as a data series file. From a single DICOM series, the three orthographic views, an oblique view of choice and a 3D model of the patient’s brain are reconstructed and displayed. The DICOM data information of patient posture is extended to develop optimum robotic surgical framework to perform a high-precision neurosurgery. Surgical Coordinate Measuring Mechanism (SCMM)-based neuro-registration and neuronavigation are demonstrated on the various phantoms and a real human skull to validate the data preparation for neuronavigation. The test procedures are found to be in accordance with the neurosurgical standards. A neurosurgical procedure is demonstrated by utilizing workspace of the surgical robot. The SCMM-based neuro-registration procedure eliminates the ‘line of sight’ constraint as in camera-based neuro-registration procedures. The utility of optimum workspace strategy of the robot helps in bringing highest manipulability at the required region.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
T.M. Deserno, Biomedical Image Processing (Springer, 2011)
Digital Imaging and Communications in Medicine (DICOM), Part 3: Information Object Definitions. PS 3.3 (2011)
Coordinate System, http://www.slicer.org/slicerWiki/index.php/Coordinate_systems. Accessed 24 Aug 2017
I. Rozet, M.S. Vavilala, Risks and benefits of patient positioning during neurosurgical care. Anesthesiol. Clin. Neurosurg. Anesth. Crit. Care 25(3), 631–653 (2007)
R.M. Schmitz, O. Telfer, R.W. Townson, S. Zavgorodni, Generalized Coordinate Transformations for Monte Carlo (DOSXYZnrc and VMC++) Verifications of DICOM Compatible Radiotherapy Treatment Plans (2014)
G. Bhutani, T.A. Dwarakanath, Neuro-registration and navigation unit for surgical manipulation, in Proceedings of the 1st International and 16th National Conference on Machines and Mechanisms, 18–20 Dec 2013 (IIT Roorkee, India, 2013)
G. Bhutani, Modeling, Design and Development of Frameless Stereotaxy in Robot Assisted Neurosurgery, Ph.D. Thesis, Homi Bhabha National Institute, Mumbai India, November 2014
T.S. Newman, H. Yi, A survey of the marching cubes algorithm. Comput. Gr. (Elsevier) 30, 854–879 (2006)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kaushik, A., Dwarakanath, T.A., Bhutani, G., Venkata, P.P.K., Moiyadi, A. (2019). Image-Based Data Preparation for Robot-Based Neurosurgery. In: Badodkar, D., Dwarakanath, T. (eds) Machines, Mechanism and Robotics. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-10-8597-0_3
Download citation
DOI: https://doi.org/10.1007/978-981-10-8597-0_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8596-3
Online ISBN: 978-981-10-8597-0
eBook Packages: EngineeringEngineering (R0)