A Hybrid-Layered System for Image-Guided Navigation and Robot Assisted Spine Surgeries

Fuente: arXiv
Saved in:
Bibliographic Details
Main Authors: T, Suhail Ansari, Maik, Vivek, Naheem, Minhas, Ram, Keerthi, Lakshmanan, Manojkumar, Sivaprakasam, Mohanasankar
Format: Preprint
Published: 2024
Subjects:
Online Access:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866911939890774016
author T, Suhail Ansari
Maik, Vivek
Naheem, Minhas
Ram, Keerthi
Lakshmanan, Manojkumar
Sivaprakasam, Mohanasankar
author_facet T, Suhail Ansari
Maik, Vivek
Naheem, Minhas
Ram, Keerthi
Lakshmanan, Manojkumar
Sivaprakasam, Mohanasankar
contents In response to the growing demand for precise and affordable solutions for Image-Guided Spine Surgery (IGSS), this paper presents a comprehensive development of a Robot-Assisted and Navigation-Guided IGSS System. The endeavor involves integrating cutting-edge technologies to attain the required surgical precision and limit user radiation exposure, thereby addressing the limitations of manual surgical methods. We propose an IGSS workflow and system architecture employing a hybrid-layered approach, combining modular and integrated system architectures in distinctive layers to develop an affordable system for seamless integration, scalability, and reconfigurability. We developed and integrated the system and extensively tested it on phantoms and cadavers. The proposed system's accuracy using navigation guidance is 1.02 0.34 mm, and robot assistance is 1.11 0.49 mm on phantoms. Observing a similar performance in cadaveric validation where 84% of screw placements were grade A, 10% were grade B using navigation guidance, 90% were grade A, and 10% were grade B using robot assistance as per the Gertzbein-Robbins scale, proving its efficacy for an IGSS. The evaluated performance is adequate for an IGSS and at par with the existing systems in literature and those commercially available. The user radiation is lower than in the literature, given that the system requires only an average of 3 C-Arm images per pedicle screw placement and verification.
format Preprint
id arxiv_https___arxiv_org_abs_2407_01578
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle A Hybrid-Layered System for Image-Guided Navigation and Robot Assisted Spine Surgeries
T, Suhail Ansari
Maik, Vivek
Naheem, Minhas
Ram, Keerthi
Lakshmanan, Manojkumar
Sivaprakasam, Mohanasankar
Robotics
Systems and Control
Image and Video Processing
In response to the growing demand for precise and affordable solutions for Image-Guided Spine Surgery (IGSS), this paper presents a comprehensive development of a Robot-Assisted and Navigation-Guided IGSS System. The endeavor involves integrating cutting-edge technologies to attain the required surgical precision and limit user radiation exposure, thereby addressing the limitations of manual surgical methods. We propose an IGSS workflow and system architecture employing a hybrid-layered approach, combining modular and integrated system architectures in distinctive layers to develop an affordable system for seamless integration, scalability, and reconfigurability. We developed and integrated the system and extensively tested it on phantoms and cadavers. The proposed system's accuracy using navigation guidance is 1.02 0.34 mm, and robot assistance is 1.11 0.49 mm on phantoms. Observing a similar performance in cadaveric validation where 84% of screw placements were grade A, 10% were grade B using navigation guidance, 90% were grade A, and 10% were grade B using robot assistance as per the Gertzbein-Robbins scale, proving its efficacy for an IGSS. The evaluated performance is adequate for an IGSS and at par with the existing systems in literature and those commercially available. The user radiation is lower than in the literature, given that the system requires only an average of 3 C-Arm images per pedicle screw placement and verification.
title A Hybrid-Layered System for Image-Guided Navigation and Robot Assisted Spine Surgeries
topic Robotics
Systems and Control
Image and Video Processing
url https://arxiv.org/abs/2407.01578