title: Using 3D Printing to Create Personalized Brain Models for Neurosurgical Training and Preoperative Planning.
abstract: BACKGROUND
  Three-dimensional (3D) printing holds promise for a wide variety of biomedical applications, from surgical planning, practicing, and teaching to creating implantable devices. The growth of this cheap and easy additive manufacturing technology in orthopedic, plastic, and vascular surgery has been explosive; however, its potential in the field of neurosurgery remains underexplored. A major limitation is that current technologies are unable to directly print ultrasoft materials like human brain tissue.
  
  
  OBJECTIVE
  In this technical note, the authors present a new technology to create deformable, personalized models of the human brain.
  
  
  METHODS
  The method combines 3D printing, molding, and casting to create a physiologically, anatomically, and tactilely realistic model based on magnetic resonance images. Created from soft gelatin, the model is easy to produce, cost-efficient, durable, and orders of magnitude softer than conventionally printed 3D models. The personalized brain model cost $50, and its fabrication took 24 hours.
  
  
  RESULTS
  In mechanical tests, the model stiffness (E = 25.29 ± 2.68 kPa) was 5 orders of magnitude softer than common 3D printed materials, and less than an order of magnitude stiffer than mammalian brain tissue (E = 2.64 ± 0.40 kPa). In a multicenter surgical survey, model size (100.00%), visual appearance (83.33%), and surgical anatomy (81.25%) were perceived as very realistic. The model was perceived as very useful for patient illustration (85.00%), teaching (94.44%), learning (100.00%), surgical training (95.00%), and preoperative planning (95.00%).
  
  
  CONCLUSIONS
  With minor refinements, personalized, deformable brain models created via 3D printing will improve surgical training and preoperative planning with the ultimate goal to provide accurate, customized, high-precision treatment.
authors.affiliation: Department of Mechanical Engineering, Stanford University, Stanford, California, USA.
authors.forename: Caitlin C
authors.initials: CC
authors.lastname: Ploch
authors.affiliation: Department of Neurosurgery, King's College Hospital London, London, United Kingdom.
authors.forename: Chris S S A
authors.initials: CSSA
authors.lastname: Mansi
authors.affiliation: Department of Pediatric Neurosurgery, John Radcliffe Hospital, Oxford University Hospitals, Oxford, United Kingdom.
authors.forename: Jayaratnam
authors.initials: J
authors.lastname: Jayamohan
authors.affiliation: Department of Mechanical Engineering, Stanford University, Stanford, California, USA; Department of Bioengineering, Stanford University, Stanford, California, USA. Electronic address: ekuhl@stanford.edu.
authors.forename: Ellen
authors.initials: E
authors.lastname: Kuhl
mesh_terms: D000328:Adult; D001921:Brain / Q000033:anatomy & histology* / Q000000981:diagnostic imaging / Q000601:surgery; D004548:Elasticity; D005260:Female; D005780:Gelatin; D006801:Humans; D007396:Internship and Residency; D008279:Magnetic Resonance Imaging; D008422:Materials Testing; D008953:Models, Anatomic; D000069471:Neurosurgeons / Q000193:education / Q000523:psychology; D009493:Neurosurgery / Q000191:economics / Q000193:education*; D019635:Neurosurgical Procedures / Q000191:economics / Q000193:education*; D057285:Precision Medicine / Q000191:economics / Q000379:methods; D066330:Printing, Three-Dimensional / Q000191:economics; D013997:Time Factors
chemical_list: D005780:Gelatin
keywords: 3D printing; Clinical skills; Neurosurgery; Simulation; Training