Artikel ; Online: Intraoperative mixed-reality spinal neuronavigation system: a novel navigation technique for spinal intradural pathologies.
2023 Band 56, Heft 1, Seite(n) E2
Abstract: Objective: The objective of this study was to assess the intraoperative accuracy and feasibility of 3D-printed marker-based mixed-reality neurosurgical navigation for spinal intradural pathologies.: Methods: The authors produced 3D segmentations of ... ...
Abstract | Objective: The objective of this study was to assess the intraoperative accuracy and feasibility of 3D-printed marker-based mixed-reality neurosurgical navigation for spinal intradural pathologies. Methods: The authors produced 3D segmentations of spinal intradural tumors with neighboring structures by using combined CT and MRI, and preoperative registration of pathology and markers was successfully performed. A patient-specific, surgeon-facilitated application for mobile devices was built, and a mixed-reality light detection and ranging (LIDAR) camera on a mobile device was employed for cost-effective, high-accuracy spinal neuronavigation. Results: Mobile device LIDAR cameras can successfully overlay images of virtual tumor segmentations according to the position of a 3D-printed marker. The surgeon can visualize and manipulate 3D segmentations of the pathology intraoperatively in 3D. Conclusions: A 3D-printed marker-based mixed-reality spinal neuronavigation technique was performed in spinal intradural pathology procedures and has potential to be clinically feasible and easy to use for surgeons, as well as being time saving, cost-effective, and highly precise for spinal surgical procedures. |
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Mesh-Begriff(e) | Humans ; Neuronavigation/methods ; Augmented Reality ; Neurosurgical Procedures/methods ; Magnetic Resonance Imaging ; Spinal Neoplasms ; Spinal Cord Neoplasms/diagnostic imaging ; Spinal Cord Neoplasms/surgery ; Surgery, Computer-Assisted/methods ; Imaging, Three-Dimensional/methods |
Sprache | Englisch |
Erscheinungsdatum | 2023-12-15 |
Erscheinungsland | United States |
Dokumenttyp | Journal Article |
ZDB-ID | 2026589-X |
ISSN | 1092-0684 ; 1092-0684 |
ISSN (online) | 1092-0684 |
ISSN | 1092-0684 |
DOI | 10.3171/2023.10.FOCUS23624 |
Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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