Cranioplasty prosthesis manufacturing based on reverse engineering technology
Year: 2012
Type: article
Study design: Clinical Trial
Abstract: BACKGROUND: Most patients with large focal skull bone loss after craniectomy are referred for cranioplasty. Reverse engineering is a technology which creates a computer-aided design (CAD) model of a real structure. Rapid prototyping is a technology which produces physical objects from virtual CAD models. The aim of this study was to assess the clinical usefulness of these technologies in cranioplasty prosthesis manufacturing. MATERIAL/METHODS: CT was performed on 19 patients with focal skull bone loss after craniectomy, using a dedicated protocol. A material model of skull deficit was produced using computer numerical control (CNC) milling, and individually pre-operatively adjusted polypropylene-polyester prosthesis was prepared. In a control group of 20 patients a prosthesis was manually adjusted to each patient by a neurosurgeon during surgery, without using CT-based reverse engineering/rapid prototyping. In each case, the prosthesis was implanted into the patient. The mean operating times in both groups were compared. RESULTS: In the group of patients with reverse engineering/rapid prototyping-based cranioplasty, the mean operating time was shorter (120.3 min) compared to that in the control group (136.5 min). The neurosurgeons found the new technology particularly useful in more complicated bone deficits with different curvatures in various planes. CONCLUSIONS: Reverse engineering and rapid prototyping may reduce the time needed for cranioplasty neurosurgery and improve the prosthesis fitting. Such technologies may utilize data obtained by commonly used spiral CT scanners. The manufacturing of individually adjusted prostheses should be commonly used in patients planned for cranioplasty with synthetic material.
Source: Medical Science Monitor
DOI: 10.12659/msm.882186
Institutions: Jagiellonian University, Cracow University of Technology
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2016676397