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      Use of CBCT Guidance for Tooth Autotransplantation in Children

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          Abstract

          Tooth autotransplantation (TAT) offers a viable biological approach to tooth replacement in children and adolescents. The aim of this study was to evaluate the outcome of the cone-beam computed tomographic (CBCT)–guided TAT compared to the conventional TAT protocol and to assess the 3-dimensional (3D) patterns of healing after CBCT-guided TAT (secondary aim). This study included 100 autotransplanted teeth in 88 patients. Each experimental group consisted of 50 transplants in 44 patients (31 males and 19 females). The mean (SD) age at the time of surgery was 10.7 (1.1) y for the CBCT-guided group. This was 10.6 (1.3) y for the conventional group. The mean (SD) follow-up period was 4.5 (3.1) y (range, 1.1 to 10.4 y). Overall survival rate for the CBCT-guided TAT was 92% with a success rate of 86% compared to an 84% survival rate and a 78% success rate for the conventional group ( P > 0.005). The following measurements were extracted from the 3D analysis: root hard tissue volume (RV), root length (RL), apical foramen area (AFA), and mean and maximum dentin wall thickness (DWT). Overall, the mean (SD) percentage of tissue change was as follows: RV gain by 65.8% (34.6%), RL gain by 37.3% (31.5%), AFA reduction by 91.1% (14.9%), mean DWT increase by 107.9% (67.7%), and maximum DWT increase by 26.5% (40.1%). Principal component analysis (PCA) identified the mean DWT, RV, and maximum DWT as the parameters best describing the tissue change after TAT. Cluster analysis applied to the variables chosen by the PCA classified the CBCT group into 4 distinct clusters (C1 = 37.2%, C2 = 17.1%, C3 = 28.6%, C4 = 17.1%), revealing different patterns of tissue healing after TAT. The CBCT-guided approach increased the predictability of the treatment. The 3D analysis provided insights into the patterns of healing. CBCT-guided TAT could be adopted as an alternative for the conventional approach. (Clinical trial center and ethical board University Hospitals, KU Leuven: S55287; ClinicalTrials.gov Identifier: NCT02464202)

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          A new approach to the problem of multimodality medical image registration is proposed, using a basic concept from information theory, mutual information (MI), or relative entropy, as a new matching criterion. The method presented in this paper applies MI to measure the statistical dependence or information redundancy between the image intensities of corresponding voxels in both images, which is assumed to be maximal if the images are geometrically aligned. Maximization of MI is a very general and powerful criterion, because no assumptions are made regarding the nature of this dependence and no limiting constraints are imposed on the image content of the modalities involved. The accuracy of the MI criterion is validated for rigid body registration of computed tomography (CT), magnetic resonance (MR), and photon emission tomography (PET) images by comparison with the stereotactic registration solution, while robustness is evaluated with respect to implementation issues, such as interpolation and optimization, and image content, including partial overlap and image degradation. Our results demonstrate that subvoxel accuracy with respect to the stereotactic reference solution can be achieved completely automatically and without any prior segmentation, feature extraction, or other preprocessing steps which makes this method very well suited for clinical applications.
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            World traumatic dental injury prevalence and incidence, a meta-analysis-One billion living people have had traumatic dental injuries

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                Author and article information

                Contributors
                Journal
                Journal of Dental Research
                J Dent Res
                SAGE Publications
                0022-0345
                1544-0591
                April 2019
                February 20 2019
                April 2019
                : 98
                : 4
                : 406-413
                Affiliations
                [1 ]OMFS IMPATH Research Group, Faculty of Medicine, Department of Imaging and Pathology, KU Leuven and Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
                [2 ]Department of Oral Health Sciences, KU Leuven and Paediatric Dentistry and Special Dental Care, University Hospitals Leuven, Leuven, Belgium
                [3 ]Certified Freelance Statistician, Heverlee, Heverlee, Belgium
                [4 ]Biomedical Research Institute, Hasselt University, Diepenbeek, Belgium
                [5 ]Department of Oral Health Sciences, KU Leuven and Orthodontics and Dentistry, University Hospitals Leuven, Leuven, Belgium
                Article
                10.1177/0022034519828701
                30786806
                bc2fe851-6658-4f93-83a1-e077c6418e25
                © 2019

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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