Open AccessThree-Dimensionally Printed Surgical Simulation Tool for Brain Mapping Training and Preoperative Planning
Author(s) -
Faith Colaguori,
Maité Marin-Mera,
Megan McDonnell,
Jaime L Martínez,
Fidel ValeroMoreno,
Aaron Damon,
Ricardo A. Domingo,
William Clifton,
W. Christopher Fox,
Kaisorn L. Chaichana,
Erik H. Middlebrooks,
David S. Sabsevitz,
Rebecca Forry,
Alfredo QuiñonesHinojosa
Publication year - 2021
Publication title -
operative neurosurgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.791
H-Index - 21
eISSN - 2332-4260
pISSN - 2332-4252
DOI - 10.1093/ons/opab331
Subject(s) - medicine , surgical planning , biomedical engineering , neurosurgery , neuronavigation , diffusion mri , computer science , artificial intelligence , medical physics , magnetic resonance imaging , surgery , radiology
BACKGROUND Brain mapping is the most reliable intraoperative tool for identifying surrounding functional cortical and subcortical brain parenchyma. Brain mapping procedures are nuanced and require a multidisciplinary team and a well-trained neurosurgeon. Current training methodology involves real-time observation and operation, without widely available surgical simulation. OBJECTIVE To develop a patient-specific, anatomically accurate, and electrically responsive biomimetic 3D-printed model for simulating brain mapping. METHODS Imaging data were converted into a 2-piece inverse 3D-rendered polyvinyl acetate shell forming an anatomically accurate brain mold. Functional and diffusion tensor imaging data were used to guide wire placement to approximate the projection fibers from the arm and leg areas in the motor homunculus. Electrical parameters were generated, and data were collected and processed to differentiate between the 2 tracts. For validation, the relationship between the electrical signal and the distance between the probe and the tract was quantified. Neurosurgeons and trainees were interviewed to assess the validity of the model. RESULTS Material testing of the brain component showed an elasticity modulus of 55 kPa (compared to 140 kPa of cadaveric brain), closely resembling the tactile feedback a live brain. The simulator's electrical properties approximated that of a live brain with a voltage-to-distance correlation coefficient of r2 = 0.86. Following 32 neurosurgeon interviews, ∼96% considered the model to be useful for training. CONCLUSION The realistic neural properties of the simulator greatly improve representation of a live surgical environment. This proof-of-concept model can be further developed to contain more complicated tractography, blood and cerebrospinal fluid circulation, and more in-depth feedback mechanisms.