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.

The influence of matrix mediated hopping conductivity, filler concentration, aspect ratio and orientation on the electrical response of carbon nanotube/polymer nanocomposites

A model to simulate the conductivity of carbon nanotube/polymer nanocomposites is presented. The proposed model is based on hopping between the fillers. A parameter related to the influence of the matrix in the overall composite conductivity is defined. It is demonstrated that increasing the aspect ratio of the fillers will increase the conductivity. Finally, it is demonstrated that the alignment of the filler rods parallel to the measurement direction results in higher conductivity values, in agreement with results from recent experimental work.

Hand-held robotic device for laparoscopic surgery and training

Laparoscopic surgery (LS) has revolutionized traditional surgical techniques introducing minimally invasive procedures for diagnosis and local therapies. LSs have undeniable advantages, such as small patient incisions, reduced postoperative pain and faster recovery. On the other hand, restricted vision of the anatomical target, difficult handling of the surgical instruments, restricted mobility inside the human body, need of dexterity to hand-eye coordination and inadequate and non-ergonomic surgical instruments may restrict LS only to more specialized surgeons. To overcome the referred limitations, this work presents a new robotic surgical handheld system – the EndoRobot. The EndoRobot was designed to be used in clinical practice or even as a surgical simulator. It integrates an electromechanical system with 3 degrees of freedom. Each degree can be manipulated independently and combined with different levels of sensitivity allowing fast and slow movements. As other features, the EndoRobot has battery power or external power supply, enables the use of bipolar radiofrequency to prevent bleeding while cutting and allows plug-and-play of the laparoscopic forceps for rapid exchange. As a surgical simulator, the system was also instrumented to measure and transmit, in real time, its position and orientation for a training software able to monitor and assist the trainee’s surgical movements.