A perforator creates a bur hole in the model of a skull. (Photo Credit: © American Association of Neurosurgeons, 2013.)


Dec.12, 2013

Researchers from the University of Malaya in Malaysia, with collaboration from researchers from the University of Portsmouth and the University of Oxford in the United Kingdom, announced the creation of an ultra-realistic 3D-printed, two-part model of the skull for use in practicing drilling into bone and removing a tumour.

The model is composed of a variety of materials that simulate the various consistencies and densities of human tissues encountered during neurosurgery.

Neurosurgery is a difficult discipline to master. Trainees may spend as many as 10 years after graduation from medical school developing and honing their surgical skills before they can be designated as proficient in their specialty. However, it is often difficult to find suitable simulation models that offer accuracy and realism for neurosurgical training while keeping training costs down.

According to Vicknes Waran from the University of Malaya in Kuala Lumpur, Malaysia, the newest generation of multimaterial 3D printers can aid neurosurgical training by creating models that simulate different diseases in a variety of body tissues.

Researchers created a two-part model using a Stratasys Objet500 Connex multimaterial 3D printer. The base piece of the model (the “head”) consists of one material. It has human features (a “face”) and the natural contours of a human skull. This piece is used to train the novice in neuronavigation techniques and can be reused again and again. The second part of the model defines the region in which simulated surgery is performed. This piece contains several different materials, which separately simulate skin, bone, dura mater, tumor, and normal brain tissue. The second piece fits into a slot in the base piece; this multi-textured piece can only be used once and is discarded after the practice session. Fortunately, it is easy to reproduce a steady stream of new pieces.

To make the training session valuable, the trainee must be able to see, feel, and even hear different “tissue” responses to surgical instruments and techniques during simulation surgery. According to the researchers, the “skin” is designed to be pliable enough to be cut by a scalpel and repaired by sutures, yet sturdy enough to be held by a retractor; the “bone” has to be hard enough for the trainee to obtain experience using bone perforators and cutters; the “dura mater” must be thin and pliable—just like the real thing. The consistency and color of the “tumor” differ from those of the “brain” to simulate actual tissues. The researchers made the “tumor” softer than the “brain” and colored it orange, whereas they colored the brain light yellow.

To test the quality of the model produced by the printer and to make minor adjustments, the researchers from Malaysia were aided by other researchers from the UK. Three neurosurgeons and one expert in surgical simulations performed simulated surgery and assessed the model’s “tissue” components. All parts received ratings of “fair” or “good,” with most rated “good.”

The usefulness of the model in training neuronavigation techniques was also assessed. Since the two-part model was based on data from a real patient, it was no surprise that “neuroimaging” was rated “excellent” by the evaluating team. Two navigation systems were used, and in both cases “registration was accurate and planning possible.”

Waran and colleagues state that the reusable base piece of the model costs approximately US$2000 to fabricate and the disposable inset costs US$600. This makes these training models affordable.

“As 3D printer technology improves, these machines will provide the possibility for newer, more complex models to be created, allowing an improved training experience.” notes the researchers. According to Dr. Waran, “3D models of the future may allow the possibility to perform entire operations from start to finish, making for a realistic simulator” to be used in neurosurgical training.

Posted in 3D Printing Applications



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