A creative solution for rapid and inexpensive model-making in product design processes

Rapid prototyping (RP) is an approach for automatically building a physical object through solid freeform fabrication. Nowadays, RP has become a vital aspect of most product development processes, due to the significant competitive advantages it offers compared to traditional manual model making. Even in academic environments, it is important to be able to quickly create accurate physical representations of concept solutions. Some of these can be used for simple visual validation, while others can be employed for ergonomic assessment by potential users or even for physical testing. However, the cost of traditional RP methods prevents their use in most academic environments on a regular basis, and even for very preliminary prototypes in many small companies. That results in delaying the first physical prototypes to later stages, or creating very rough mock-ups which are not as useful as they could be. In this paper we propose an approach for rapid and inexpensive model-making, which was developed in an academic context, and which can be employed for a variety of objects.

Voxel-based registration of simulated and real patient CBCT data for accurate dental implant pose estimation

The success of dental implant-supported prosthesis is directly linked to the accuracy obtained during implant’s pose estimation (position and orientation). Although traditional impression techniques and recent digital acquisition methods are acceptably accurate, a simultaneously fast, accurate and operator-independent methodology is still lacking. Hereto, an image-based framework is proposed to estimate the patient-specific implant’s pose using cone-beam computed tomography (CBCT) and prior knowledge of implanted model. The pose estimation is accomplished in a threestep approach: (1) a region-of-interest is extracted from the CBCT data using 2 operator-defined points at the implant’s main axis; (2) a simulated CBCT volume of the known implanted model is generated through Feldkamp-Davis-Kress reconstruction and coarsely aligned to the defined axis; and (3) a voxel-based rigid registration is performed to optimally align both patient and simulated CBCT data, extracting the implant’s pose from the optimal transformation. Three experiments were performed to evaluate the framework: (1) an in silico study using 48 implants distributed through 12 tridimensional synthetic mandibular models; (2) an in vitro study using an artificial mandible with 2 dental implants acquired with an i-CAT system; and (3) two clinical case studies. The results shown positional errors of 67±34μm and 108μm, and angular misfits of 0.15±0.08º and 1.4º, for experiment 1 and 2, respectively. Moreover, in experiment 3, visual assessment of clinical data results shown a coherent alignment of the reference implant. Overall, a novel image-based framework for implants’ pose estimation from CBCT data was proposed, showing accurate results in agreement with dental prosthesis modelling requirements.