Artificial neural networks for automatic modelling of the pectus excavatum corrective prosthesis

Pectus excavatum is the most common deformity of the thorax and usually comprises Computed Tomography (CT) examination for pre-operative diagnosis. Aiming at the elimination of the high amounts of CT radiation exposure, this work presents a new methodology for the replacement of CT by a laser scanner (radiation-free) in the treatment of pectus excavatum using personally modeled prosthesis. The complete elimination of CT involves the determination of ribs external outline, at the maximum sternum depression point for prosthesis placement, based on chest wall skin surface information, acquired by a laser scanner. The developed solution resorts to artificial neural networks trained with data vectors from 165 patients. Scaled Conjugate Gradient, Levenberg-Marquardt, Resilient Back propagation and One Step Secant gradient learning algorithms were used. The training procedure was performed using the soft tissue thicknesses, determined using image processing techniques that automatically segment the skin and rib cage. The developed solution was then used to determine the ribs outline in data from 20 patient scanners. Tests revealed that ribs position can be estimated with an average error of about 6.82±5.7 mm for the left and right side of the patient. Such an error range is well below current prosthesis manual modeling (11.7±4.01 mm) even without CT imagiology, indicating a considerable step forward towards CT replacement by a 3D scanner for prosthesis personalization.

Modelling the Mechanical Behavior of Polymer-Based Nanocomposites

Molecular dynamics simulations were employed to analyze the mechanical properties of polymer-based nanocomposites with varying nanofiber network parameters. The study was focused on nanofiber aspect ratio, concentration and initial orientation. The reinforcing phase affects the behavior of the polymeric nanocomposite. Simulations have shown that the fiber concentration has a significant effect on the properties, with higher loadings resulting in higher stress levels and higher stiffness, matching the general behavior from experimental knowledge in this field. The results also indicate that, within the studied range, the observed effect of the aspect ratio and initial orientation is smaller than that of the concentration, and that these two parameters are interrelated.

Estimating Local Part Thickness in Midplane Meshes for Finite Element Analysis

Within the development of motor vehicles, crash safety (e.g. occupant protection, pedestrian protection, low speed damageability), is one of the most important attributes. In order to be able to fulfill the increased requirements in the framework of shorter cycle times and rising pressure to reduce costs, car manufacturers keep intensifying the use of virtual development tools such as those in the domain of Computer Aided Engineering (CAE). For crash simulations, the explicit finite element method (FEM) is applied. The accuracy of the simulation process is highly dependent on the accuracy of the simulation model, including the midplane mesh. One of the roughest approximations typically made is the actual part thickness which, in reality, can vary locally. However, almost always a constant thickness value is defined throughout the entire part due to complexity reasons. On the other hand, for precise fracture analysis within FEM, the correct thickness consideration is one key enabler. Thus, availability of per element thickness information, which does not exist explicitly in the FEM model, can significantly contribute to an improved crash simulation quality, especially regarding fracture prediction. Even though the thickness is not explicitly available from the FEM model, it can be inferred from the original CAD geometric model through geometric calculations. This paper proposes and compares two thickness estimation algorithms based on ray tracing and nearest neighbour 3D range searches. A systematic quantitative analysis of the accuracy of both algorithms is presented, as well as a thorough identification of particular geometric arrangements under which their accuracy can be compared. These results enable the identification of each technique’s weaknesses and hint towards a new, integrated, approach to the problem that linearly combines the estimates produced by each algorithm.

Accuracy Comparison of Implant Impression Techniques: A Systematic Review

Background: Several studies link the seamless fit of implant-supported prosthesis with the accuracy of the dental impression technique obtained during acquisition. In addition, factors such as implant angulation and coping shape contribute to implant misfit. Purpose: The aim of this study was to identify the most accurate impression technique and factors affecting the impression accuracy. Material and Methods: A systematic review of peer-reviewed literature was conducted analyzing articles published between 2009 and 2013. The following search terms were used: implant impression, impression accuracy, and implant misfit.A total of 417 articles were identified; 32 were selected for review. Results: All 32 selected studies refer to in vitro studies. Fourteen articles compare open and closed impression technique, 8 advocate the open technique, and 6 report similar results. Other 14 articles evaluate splinted and non-splinted techniques; all advocating the splinted technique. Polyether material usage was reported in nine; six studies tested vinyl polysiloxane and one study used irreversible hydrocolloid. Eight studies evaluated different copings designs. Intraoral optical devices were compared in four studies. Conclusions: The most accurate results were achieved with two configurations: (1) the optical intraoral system with powder and (2) the open technique with splinted squared transfer copings, using polyether as impression material.

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.