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Background and objective: Due to the high prevalence of dental caries, fixed dental restorations are regularly required to restore compromised teeth or replace missing teeth while retaining function and aesthetic appearance. The fabrication of dental restorations, however, remains challenging due to the complexity of the human masticatory system as well as the unique morphology of each individual dentition. Adaptation and reworking are frequently required during the insertion of fixed dental prostheses (FDPs), which increase cost and treatment time. This article proposes a data-driven approach for the partial reconstruction of occlusal surfaces based on a data set that comprises 92 3D mesh files of full dental crown restorations. Methods: A Generative Adversarial Network (GAN) is considered for the given task in view of its ability to represent extensive data sets in an unsupervised manner with a wide variety of applications. Having demonstrated good capabilities in terms of image quality and training stability, StyleGAN-2 has been chosen as the main network for generating the occlusal surfaces. A 2D projection method is proposed in order to generate 2D representations of the provided 3D tooth data set for integration with the StyleGAN architecture. The reconstruction capabilities of the trained network are demonstrated by means of 4 common inlay types using a Bayesian Image Reconstruction method. This involves pre-processing the data in order to extract the necessary information of the tooth preparations required for the used method as well as the modification of the initial reconstruction loss. Results: The reconstruction process yields satisfactory visual and quantitative results for all preparations with a root mean square error (RMSE) ranging from 0.02 mm to 0.18 mm. When compared against a clinical procedure for CAD inlay fabrication, the group of dentists preferred the GAN-based restorations for 3 of the total 4 inlay geometries. Conclusions: This article shows the effectiveness of the StyleGAN architecture with a downstream optimization process for the reconstruction of 4 different inlay geometries. The independence of the reconstruction process and the initial training of the GAN enables the application of the method for arbitrary inlay geometries without time-consuming retraining of the GAN.
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PURPOSE: To compare detection rates of microaneurysms (MAs) on high-speed megahertz optical coherence tomography angiography (MHz-OCTA), fluorescein angiography (FA) and colour fundus photography (CF) in patients with diabetic retinopathy (DR). METHODS: For this exploratory cross-sectional study, MHz-OCTA data were acquired with a swept-source OCT prototype (A-scan rate: 1.7 MHz), and FA and CF imaging was performed using Optos® California. MA count was manually evaluated on en face MHz-OCTA/FA/CF images within an extended ETDRS grid. Detectability of MAs visible on FA images was evaluated on corresponding MHz-OCTA and CF images. MA distribution and leakage were correlated with detectability on OCTA and CF imaging. RESULTS: 47 eyes with severe DR (n = 12) and proliferative DR (n = 35) were included. MHz-OCTA and CF imaging detected on average 56% and 36% of MAs, respectively. MHz-OCTA detection rate was significantly higher than CF (p < 0.01). The combination of MHz-OCTA and CF leads to an increased detection rate of 70%. There was no statistically significant association between leakage and MA detectability on OCTA (p = 0.13). For CF, the odds of detecting leaking MAs were significantly lower than non-leaking MAs (p = 0.012). Using MHz-OCTA, detection of MAs outside the ETDRS grid was less likely than MAs located within the ETDRS grid (outer ring, p < 0.01; inner ring, p = 0.028). No statistically significant difference between rings was observed for CF measurements. CONCLUSIONS: More MAs were detected on MHz-OCTA than on CF imaging. Detection rate was lower for MAs located outside the macular region with MHz-OCTA and for leaking MAs with CF imaging. Combining both non-invasive modalities can improve MA detection.