Piezoresistive sensors for force mapping of hip-prostheses

The success of artificial prosthetic replacements depends on the fixation of the artificial prosthetic component after being implanted in the thighbone. The materials for fixation are subject to mechanical stresses, which originate permanent deformations, incipient cracks and even fatigue fractures. This work shows the possibility of monitoring the mechanical stress over time in prosthesis. In this way, highly sensitive silicon thin-film piezoresistive sensors were developed attached to prosthesis and their results compared with commercial strain gauge sensors. Mechanical stress-strain experiments were performed in compressive mode, during 10,000 cycles. Experimental data was acquired at mechanical vibration frequencies of 0.5 Hz, 1 Hz and 5 Hz, and sent to a computer by means of a wireless link. The results show that there is a decrease in sensitivity of the thin-film silicon piezoresistive sensors when they are attached to the prosthesis, but this decrease does not compromise its monitoring performance. The sensitivity, compared to that of commercial strain gauges, is much larger due to their higher gauge factors (-23.5), when compared to the GFs of commercial sensors (2).

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.