Three-Dimensionally Printed Surgical Simulation Tool for Brain Mapping Training and Preoperative Planning

Abstract

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.

Individualised 3d-printed Landmarks as Floating Duating Duoys for Poor Man's See-through Neuro-navigation in High Grade Glioma Surgery

“…Individual-case-speci c oncological surgery is based on the selection of safe landmark lines, removal of the entire tumor mass with a single surgical intervention, and removal of the intact tissue with a certain safety line [20,21]. Surgical interventions for tumors located in the brain involve risks and require different brain-speci c neuro-navigational calculations than tumors in other parts of the body [22][23][24](Figs. 1-5).…”

Quantitative assessment and objective improvement of the accuracy of neurosurgical planning through digital patient-specific 3D models

“…Neurosurgical patient-specific 3D printed models have been shown to facilitate learning, enhance planning skills and improve surgical results in brain tumor, skull base and cerebrovascular surgeries ( 14 – 19 ). However, they have certain disadvantages such as need for special equipment and materials, higher costs, lengthy production process, lack of repetitive use, which limit their utility in every-day practice and training of neurosurgical residents ( 14 , 18 , 20 , 21 ). On the other hand, high quality, multilayered digital 3D images or models can be easily produced with limited resources and adopted in the routine neurosurgical planning pipeline.…”

Commentary: Three-Dimensionally Printed Surgical Simulation Tool for Brain Mapping Training and Preoperative Planning

“…In addition, the electrical properties of the model similarly recapitulated the standard 1:1 voltage-to-distance correlation widely used with subcortical stimulation. 4 These advances in surgical modeling accurately reflect real-world surgery and will be a helpful adjunct as residents learn early techniques for surgery near eloquent cortex.…”

“…In the manuscript by Colaguori et al, 4 the authors report their experience in creating a model that replicates motor mapping of eloquent cortex and intra-axial tumor resection with a high degree of fidelity to real-world surgery. Material testing showed a similar elasticity modulus to normal brain, enabling the learner to experience near real-world tactile feedback.…”