Accuracy of computer-aided design/computer-assisted manufacture (CAD/CAM) fabricated dental restorations: a comparative study

Introduction: Computer-Aided-Design (CAD) and Computer-Aided-Manufacture (CAM) has been developed to fabricate fixed dental restorations accurately, faster and improve cost effectiveness of manufacture when compared to the conventional method. Two main methods exist in dental CAD/CAM technology: the subtractive and additive methods. While fitting accuracy of both methods has been explored, no study yet has compared the fabricated restoration (CAM output) to its CAD in terms of accuracy. The aim of this present study was to compare the output of various dental CAM routes to a sole initial CAD and establish the accuracy of fabrication. The internal fit of the various CAM routes were also investigated. The null hypotheses tested were: 1) no significant differences observed between the CAM output to the CAD and 2) no significant differences observed between the various CAM routes. Methods: An aluminium master model of a standard premolar preparation was scanned with a contact dental scanner (Incise, Renishaw, UK). A single CAD was created on the scanned master model (InciseCAD software, V2.5.0.140, UK). Twenty copings were then fabricated by sending the single CAD to a multitude of CAM routes. The copings were grouped (n=5) as: Laser sintered CoCrMo (LS), 5-axis milled CoCrMo (MCoCrMo), 3-axis milled zirconia (ZAx3) and 4-axis milled zirconia (ZAx4). All copings were micro-CT scanned (Phoenix X-Ray, Nanotom-S, Germany, power: 155kV, current: 60µA, 3600 projections) to produce 3-Dimensional (3D) models. A novel methodology was created to superimpose the micro-CT scans with the CAD (GOM Inspect software, V7.5SR2, Germany) to indicate inaccuracies in manufacturing. The accuracy in terms of coping volume was explored. The distances from the surfaces of the micro-CT 3D models to the surfaces of the CAD model (CAD Deviation) were investigated after creating surface colour deviation maps. Localised digital sections of the deviations (Occlusal, Axial and Cervical) and selected focussed areas were then quantitatively measured using software (GOM Inspect software, Germany). A novel methodology was also explored to digitally align (Rhino software, V5, USA) the micro-CT scans with the master model to investigate internal fit. Fifty digital cross sections of the aligned scans were created. Point-to-point distances were measured at 5 levels at each cross section. The five levels were: Vertical Marginal Fit (VF), Absolute Marginal Fit (AM), Axio-margin Fit (AMF), Axial Fit (AF) and Occlusal Fit (OF). Results: The results of the volume measurement were summarised as: VM-CoCrMo (62.8mm3 ) > VZax3 (59.4mm3 ) > VCAD (57mm3 ) > VZax4 (56.1mm3 ) > VLS (52.5mm3 ) and were all significantly different (p presented as areas with different colour. No significant differences were observed at the internal aspect of the cervical aspect between all groups of copings. Significant differences (p< M-CoCrMo Internal Occlusal, Internal Axial and External Axial 2 ZAx3 > ZAx4 External Occlusal, External Cervical 3 ZAx3 < ZAx4 Internal Occlusal 4 M-CoCrMo > ZAx4 Internal Occlusal and Internal Axial The mean values of AMF and AF were significantly (p M-CoCrMo and CAD > ZAx4. Only VF of M-CoCrMo was comparable with the CAD Internal Fit. All VF and AM values were within the clinically acceptable fit (120µm). Conclusion: The investigated CAM methods reproduced the CAD accurately at the internal cervical aspect of the copings. However, localised deviations at axial and occlusal aspects of the copings may suggest the need for modifications in these areas prior to fitting and veneering with porcelain. The CAM groups evaluated also showed different levels of Internal Fit thus rejecting the null hypotheses. The novel non-destructive methodologies for CAD/CAM accuracy and internal fit testing presented in this thesis may be a useful evaluation tool for similar applications.

Uncertainty visualization in 3D scalar data

One problem in most three-dimensional (3D) scalar data visualization techniques is that they often overlook to depict uncertainty that comes with the 3D scalar data and thus fail to faithfully present the 3D scalar data and have risks which may mislead users’ interpretations, conclusions or even decisions. Therefore this thesis focuses on the study of uncertainty visualization in 3D scalar data and we seek to create better uncertainty visualization techniques, as well as to find out the advantages/disadvantages of those state-of-the-art uncertainty visualization techniques. To do this, we address three specific hypotheses: (1) the proposed Texture uncertainty visualization technique enables users to better identify scalar/error data, and provides reduced visual overload and more appropriate brightness than four state-of-the-art uncertainty visualization techniques, as demonstrated using a perceptual effectiveness user study. (2) The proposed Linked Views and Interactive Specification (LVIS) uncertainty visualization technique enables users to better search max/min scalar and error data than four state-of-the-art uncertainty visualization techniques, as demonstrated using a perceptual effectiveness user study. (3) The proposed Probabilistic Query uncertainty visualization technique, in comparison to traditional Direct Volume Rendering (DVR) methods, enables radiologists/physicians to better identify possible alternative renderings relevant to a diagnosis and the classification probabilities associated to the materials appeared on these renderings; this leads to improved decision support for diagnosis, as demonstrated in the domain of medical imaging. For each hypothesis, we test it by following/implementing a unified framework that consists of three main steps: the first main step is uncertainty data modeling, which clearly defines and generates certainty types of uncertainty associated to given 3D scalar data. The second main step is uncertainty visualization, which transforms the 3D scalar data and their associated uncertainty generated from the first main step into two-dimensional (2D) images for insight, interpretation or communication. The third main step is evaluation, which transforms the 2D images generated from the second main step into quantitative scores according to specific user tasks, and statistically analyzes the scores. As a result, the quality of each uncertainty visualization technique is determined.