The first cranial models created were used to understand bone pathology as initial commercial printers like Z Corp, ZPrinter^®450 (South Carolina, USA) were only able to print in a single material that mimicked bone very well. The next step involved was in verifying the accuracy of these models both anatomically and spatially. This was performed using standard image guidance navigation stations Medtronic StealthStation^®S7™System (Colorado, USA) and BrainLab Kolibri™ (Heimstetten, GER) to register 3D models of a patient’s skull to the actual imaging data, thus demonstrating that surgical navigation stations were unable to distinguish the model form the actual patient. We also found all the preselected anatomical points to be spatially accurate (Waran et al. 2012) (Fig. 6.2).

As surgery on an actual patient involves not just the skeletal structures but also various soft tissue components, attempts were made to create a “face” over the facial bones that accurately reflected the patient. Initial attempts were performed using latex poured into a mold. While this technique was able to accurately create the face of a person, the process was labor intensive, and after a period of time, latex had a tendency to contract and crush the underlying “bony structures” (Fig. 6.3).

The next leap in technology was the multi-material printer. This allowed models to be printed with materials of different density like bone and soft tissue therefore creating multiple interfaces between various tissues (Stratasys Objet500 Connex™). The challenge was to enable the various tissues to interact in an “anatomical or surgical way.”

Multi-material printing allowed demonstration of features such as the ability to reflect skin off bone and to allow the bone to be burred or perforated using a standard craniotome, craniotome safety clutch engagement when the bone dura interface is reached and for the dura to be separated from the skull to prevent damage to underlying structures (Fig. 6.4 and Video 1).

Due to these features, we were able to successfully create models based on imaging data from actual patients with pathological findings. Our trainees are able to carry out various standard neurosurgical procedures on these models, such as:

The advantage of these models as surgical simulators includes the presence of original pathology within the model, as well as supporting data like proper history and medical imaging. All standard surgical equipment used in day-to-day neurosurgery can be used, enhancing the realism of the simulator. These models provide tactile feedbacks that presently do not exist with basic box and complex virtual simulators.

Neurosurgical teaching models currently available include:

Despite the term multi-material, initial models worked best with one interface and two tissue densities only, for example, bone and skin.

The latest multi-material printers have allowed these models to become more dynamic. Endoscopic intraventricular models can be created with fluid-filled ventricles and intraventricular tumor. Similarly, endoscopic transsphenoidal models can be created with multiple bone ledges, intrasellar tumor, as well as cylindrical tubes cuffing the tumor to mimic carotid arteries (Figs. 6.5 and 6.6a, b).

These models have been used to run “surgical approaches workshops” and training programs for surgeons of various levels from junior trainees to senior surgeons (Narayanan et al. 2015; Waran et al. 2014b; Waran et al. 2015). With the advances in printer technology, future applications include color-printed tissues, tissues with various density, and tactile feedback that allows microdissection and cylindrical structures with pulsatile blood.

This area has fired the imagination the most in the eyes of the public for the use of 3D printers in customized medicine. 3D printers have in the last 3–4 years been used to preoperatively plan and intraoperatively aid various complex and infrequently performed procedures. They have demonstrated their usefulness in understanding the 3D anatomy of lesions that may differ widely in appearance among individuals with similar problems.

These models have been used in the planning of pediatric neurosurgical-maxillofacial teams performing complex advancement procedures in children with cranial synostosis. Customized patient-based models are useful in the planning of individual bone cuts that are required and assess the degree of advancements that may be required (Poukens et al. 2003; Gateno et al. 2003).

Customized models have also been used in complex base of skull tumors with the aim of assessing the various surgical approaches and corridors (Kondo et al. 2016; Pacione et al. 2016; Oyama et al. 2015).

More recently, these models have been used in planning the treatment of complex vascular pathology. In this instance, the model was used to understand the complex anatomical relationship of the various vessels and related brain tissue (Ryan et al. 2016; Wurm et al. 2011; Thawani et al. 2016).

3D-printed models have shown great utility for patient consent, greatly enhancing conversations with patients and enabling meaningful explanations of pathology and interventions to patients. Surgeons have used these personally created models with in situ pathology to explain complex procedures to patients and their relatives. The surgical approaches, brain tissue within the corridor of approach, and possible complication are much better explained to a nonmedical personnel by physical models. It presents as an excellent medical aid in the consent process (Liew et al. 2015; Jones et al. 2016).

The main and probably only drawback of the 3D printing technology is time and cost. It requires expertise and time to segment important anatomical components individually before a print can be commenced. Printing time itself has been shortened in certain instances, but nevertheless, the 3D printing of a complex case can take up to a full day. The initial expense of buying a versatile printer and maintaining expert staff to run it is still expensive and may add on to an already escalating healthcare cost, resulting in being prohibitive to be used routinely for all patients. This current technique is therefore most useful for complex, elective procedures requiring detailed preoperative planning (Martelli et al. 2016; Ionita et al. 2014).

3D printing has progressed in leaps and bounds since the early days of laser-sintering resin models. We are now able to personalize models based on individual patients in an accurate and cost-effective way to help in the surgical process, surgical training, and patient understanding. The redult is that these collective technologies are very useful neurosurgical tools.