Abstract
Achieving precise open reduction and fixation of acetabular fractures by using a plate osteosynthesis is a complex procedure. Increasing availability of affordable 3D printing devices and services now allow to actually print physical models of the patient's anatomy by segmenting the patient's CT image. The data processing and printing of the model however still take too much time and usually the resulting model is rigid and doesn't allow fracture reduction on the model itself.
Our proposed solution automatically detects relevant structures such as the fracture gaps and cortical bone while eliminating irrelevant structures such as debris and cancellous bone. This is done by approximating a sphere to the exterior surface of a classic segmented STL model. Stepwise, these approximated vertices are projected deeper into any structure such as the acetabular socket or fractures, following a specific set of rules. The resulting surface model finally is adapted precisely to the primary segmented model.
Creating an enhanced surface reconstruction model from the primary model took a median time of 42 sec. The whole workflow from DICOM to enhanced printable 3D file took a median time of 13:25 min. The median time and material needed for the prints without the process was 32:25:36 h and 241,04 g, with the process 09:41:33 h and 65,89 g, which is 70% faster. The price of material was very low with a median of 2,18€ per case. Moreover, fracture reduction becomes possible, allowing a dry-run of the procedure and allowing more precise plate placement.
Pre-contouring of osteosynthesis plates by using these 3D printouts was done for eleven patients prior to surgery. These printouts were validated to be accurate by three experiences surgeons and compared to classic segmented models regarding printing time, material cost and reduction ability. The pre-contouring of the plates was safely achievable. Our results show that improving the operative treatment with the help of enhanced 3D printed fracture models seems feasible and needs comparably little time and cost, thus making it a technique that can easily integrated into the clinical workflow.
