Department 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. Electronic address: [Email]
BACKGROUND : Visuospatial features of neuroanatomy are likely the most difficult concepts to learn in anatomy. Three-dimensional (3D) modalities have gradually begun to supplement traditional 2-dimensionanl representations of dissections and illustrations. We have introduced and described the workflow of 2 innovative methods-photogrammetry (PGM) and structured light scanning (SLS)-which have typically been used for reverse-engineering applications. In the present study, we have described a novel application of SLS and PGM that could enhance medical education and operative planning in neurosurgery. METHODS : We have described the workflow of SLS and PGM for creating volumetric models (VMs) of neuroanatomical dissections, including the requisite equipment and software. We have also provided step-by-step procedures on how users can postprocess and refine these images according to their specifications. Finally, we applied both methods to 3 dissected hemispheres to demonstrate the quality of the VMs and their applications. RESULTS : Both methods yielded VMs with suitable clarity and structural integrity for anatomical education, surgical illustration, and procedural simulation. CONCLUSIONS : The application of 3D computer graphics to neurosurgical applications has shown great promise. SLS and PGM can facilitate the construction of VMs with high accuracy and quality that can be used and shared in a variety of 3D platforms. Similarly, the technical demands are not high; thus, it is plausible that neurosurgeons could become quickly proficient and enlist their use in education and surgical planning. Although SLS is preferable in settings in which high accuracy is required, PGM is a viable alternative with a short learning curve.