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J Neurol Surg B Skull Base
DOI: 10.1055/a-2538-3745
Original Article

Immersive Neurosurgical Anatomy Using Photogrammetry: Technical Note and Scoping Review

Jhon E. Bocanegra-Becerra 
1   Academic Department of Surgery, School of Medicine, Universidad Peruana Cayetano Heredia, Lima, Peru
,
Daniel Ballesteros-Herrera
2   Department of Neurosurgery, Instituto Nacional de Neurología y Neurocirugia, Manuel Velasco Suárez, Mexico City, Mexico
,
Khaled Alhwaishel
3   Mansoura Manchester Program, Faculty of Medicine, Mansoura University, Mansoura, Egypt
,
Marcio Yuri Ferreira
4   Department of Neurosurgery, Lenox Hill Hospital/Northwell Health, New York, New York, United States
,
Vanessa Emanuelle Cunha Santos
5   Bahiana School of Medicine and Public Health, Salvador, Bahia, Brazil
,
Cristian D. Mendieta
6   Universidad Mayor Real y Pontificia de San Francisco Xavier de Chuquisaca, Bolivia
,
Gabriel Semione
7   University of West of Santa Catarina, Joaçaba, SC, Brazil
,
Kim Wouters
8   Open Universiteit Heerle Nederland, LI, The Netherlands
,
Sávio Batista
9   Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
,
José E. Chang
10   Neurosurgery Service, Hospital General-Salvadoran Institute of Social Security, San Salvador, El Salvador
,
Raphael Bertani
11   Department of Neurosurgery, University of Sao Paulo, Sao Paulo, Sao Paulo, Brazil
,
Miguel Angel Lopez-Gonzalez 
12   Department of Neurosurgery, Loma Linda University Medical Center, Loma Linda, California, United States
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Abstract

Introduction Photogrammetry holds promise for expanding the domains of microsurgical education. The authors present a technical note and scoping review that explore the use of photogrammetry in neurosurgical anatomy, existing technical guidelines, and areas of implementation.

Methods Photogrammetry was employed to build three-dimensional models of the anatomy of the white matter tracts, brainstem, cranial nerves, and the retrosigmoid approach using human brain and skull specimens. In addition, a scoping review was performed on three databases (PubMed, Scopus, and Embase). Information was collected regarding human models, software, hardware, assessment of high-fidelity reconstruction, and anatomic depth estimation.

Results The illustrative models achieved a high-quality representation of the white matter tracts, brainstem, cranial nerves, and anatomy in the retrosigmoid approach.

Our scoping review yielded 3,620 articles, of which 28 were included in the analysis. Photogrammetry was described in three technical stages: image acquisition, processing, and visualization. About 75% of studies reported high-fidelity image reconstruction, and only 42.9% of articles performed anatomic depth estimation. Concerning microsurgical anatomy education, photogrammetry has primarily rendered digital models of the cranial region (96.4%). During educational sessions, the most common surgical approaches described the orbitozygomatic (20%), endoscopic endonasal (20%), translabyrinthine (13.3%), retrosigmoid (13.3%), and Kawase (13.3%) approaches.

Conclusion Photogrammetry offers an innovative approach to creating portable and virtual anatomical models with high-fidelity and vivid representations of human specimens. The resulting three-dimensional models can provide real proportions to teach visuospatial skills in neurosurgery. However, significant challenges remain to achieve objective accuracy and anatomic depth perception, which are critical for microsurgical education.

Keywords

neuroanatomy - microsurgery - photogrammetry - 3D imaging - neurosurgery

Authors' Contributions

J.E.B.B. was responsible for the conception and design of the study. D.B.H. described the technical note, dissected the human specimens, and rendered the photogrammetry models. Data acquisition and revision were carried out by J.E.B.B., D.B.H., K.A., V.E.C.S., C.D.M., G.S., and K.W., while J.E.B.B., D.B.H., G.S., and J.E.C. contributed to the analysis and interpretation of the data. Manuscript writing and revision were undertaken by J.E.B.B., D.B.H., M.Y.F., K.W., S.B., R.B., and M.A.L.G. The study was supervised by R.B. and M.A.L.G.




Publication History

Received: 19 December 2024

Accepted: 12 February 2025

Accepted Manuscript online:
14 February 2025

Article published online:
11 March 2025

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