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Construction of Neuroanatomical Volumetric Models Using 3-Dimensional Scanning Techniques: Technical Note and
Roberto Rodriguez Rubio1, Joseph Shehata2, Ioannis Kournoutas2
1Department of Neurological Surgery, University of California, San Francisco, California, USA; Skull Base and Cerebrovascular Laboratory, University of California, San Francisco, California, USA; Department of Otolaryngology-Head and Neck Surgery, University of California, San Francisco, California, USA.
Photogrammetry (PGM) and structured light scanning (SLS) create high-quality 3D neuroanatomy models. These volumetric models (VMs) enhance medical education and surgical planning, with PGM offering a quicker learning curve.
Area of Science:
- Neurosurgery
- Medical Education
- 3D Imaging
Background:
- Visuospatial neuroanatomy is challenging to learn.
- Traditional 2D anatomical representations are supplemented by 3D modalities.
- Photogrammetry (PGM) and structured light scanning (SLS) are innovative 3D techniques.
Purpose of the Study:
- To describe the application of SLS and PGM for creating neuroanatomical volumetric models (VMs).
- To enhance medical education and operative planning in neurosurgery using 3D models.
Main Methods:
- Described the workflow for SLS and PGM to create VMs of neuroanatomical dissections.
- Provided step-by-step procedures for image postprocessing and refinement.
- Applied both methods to 3 dissected hemispheres to demonstrate VM quality.
Main Results:
- Both SLS and PGM produced VMs with excellent clarity and structural integrity.
- The generated VMs are suitable for anatomical education, surgical illustration, and procedural simulation.
Conclusions:
- 3D computer graphics, specifically SLS and PGM, show significant promise for neurosurgical applications.
- SLS and PGM facilitate the creation of accurate, high-quality VMs for various 3D platforms.
- Neurosgeons can quickly master these techniques for education and surgical planning; SLS is preferred for high accuracy, while PGM is a viable alternative with a shorter learning curve.
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