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      Quantitative validation of a computer-aided maxillofacial planning system, focusing on soft tissue deformations

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          The aim of this study was to evaluate the accuracy of 3D soft tissue predictions generated by a computer-aided maxillofacial planning system in patients undergoing orthognathic surgery.

          Methods and Materials:

          Twenty patients with dentofacial dysmorphosis were treated with orthognathic surgery after a preoperative orthodontic treatment. Fourteen patients had an Angle Class II malocclusion; three patients had an Angle class III malocclusion, and three patients had an Angle Class I malocclusion. Skeletal asymmetry was observed in six patient. The surgeries were planned using the Maxilim software. Computer assisted surgical planning was transferred to the patient by digitally generated splints. The validation procedures were performed in the following steps: (1) Standardized registration of the pre- and postoperative Cone Beam CT volumes; (2) Automated adjustment of the bone-related planning to the actual operative bony displacement; (3) Simulation of soft tissue changes; (4) Calculation of the soft tissue differences between the predicted and the postoperative results by distance mapping.

          Statistical Analysis and Results:

          Eighty four percent of the mapped distances between the predicted and actual postoperative results measured between -2 mm and +2 mm. The mean absolute linear measurements between the predicted and actual postoperative surface was 1.18. Our study shows the overall prediction was dependent on neither the surgical procedures nor the dentofacial deformity type.


          Despite some shortcomings in the prediction of the final position of the lower lip and cheek area, this software promises a clinically acceptable soft tissue prediction for orthognathic surgical procedures.

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          Most cited references 11

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          Multimodality image registration by maximization of mutual information.

          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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            A cone-beam computed tomography triple scan procedure to obtain a three-dimensional augmented virtual skull model appropriate for orthognathic surgery planning.

            The aim of this study was to present a new approach to acquire a three-dimensional virtual skull model appropriate for orthognathic surgery planning without the use of plaster dental models and without deformation of the facial soft-tissue mask. A "triple" cone-beam computed tomography (CBCT) scan procedure with triple voxel-based rigid registration was evaluated and validated on 10 orthognathic patients. First, the patient was scanned vertically with a wax bite wafer in place (CBCT scan No1). Second, a limited dose scan of the patient with a Triple Tray AlgiNot impression in place was carried out (CBCT scan No2). Finally, a high-resolution scan of the Triple Tray AlgiNot impression was done (CBCT scan No3). Sequential and semiautomatic triple voxel-based rigid registration (RNo1-RNo3) was performed to augment the patient's skull model with accurate occlusal and intercuspidation data (Maxilim, version 2.1.1., Medicim NV, Mechelen, Belgium). All registrations were based on the Maximisation of Mutual Information registration algorithm. Because the accuracy and stability of the voxel-based registration (RNo1) between the Triple Tray AlgiNot impression scan and the limited low-dose patient scan were not known, this particular registration step needed to be validated. The accuracy of registration was measured on a synthetic skull and showed to be highly accurate. A volume overlap of 98.1% was found for registered impression scan No1. The mean distance between registered impression scan No1 and registered impression scan No2 was 0.08 +/- 0.03 mm (range, 0.04-0.11 mm). As far as the stability of registration was concerned, successful registration with a stable optimal position was obtained with a maximum variability of less than 0.1 mm. The results of this study showed that semiautomatic sequential triple voxel-based rigid registration of the triple CBCT scans augmented the 3-D virtual skull model with detailed occlusal and intercuspidation data in a highly accurate and robust way. The method is therefore appropriate and valid for 3-D virtual orthognathic surgery planning in the clinical routine.
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              Predicting soft tissue deformations for a maxillofacial surgery planning system: from computational strategies to a complete clinical validation.

              In the field of maxillofacial surgery, there is a huge demand from surgeons to be able to pre-operatively predict the new facial outlook after surgery. Besides the big interest for the surgeon during the planning, it is also an essential tool to improve the communication between the surgeon and his patient. In this work, we compare the usage of four different computational strategies to predict this new facial outlook. These four strategies are: a linear Finite Element Model (FEM), a non-linear Finite Element Model (NFEM), a Mass Spring Model (MSM) and a novel Mass Tensor Model (MTM). For true validation of these four models we acquired a data set of 10 patients who underwent maxillofacial surgery, including pre-operative and post-operative CT data. For all patient data we compared in a quantitative validation the predicted facial outlook, obtained with one of the four computational models, with post-operative image data. During this quantitative validation distance measurements between corresponding points of the predicted and the actual post-operative facial skin surface, are quantified and visualised in 3D. Our results show that the MTM and linear FEM predictions achieve the highest accuracy. For these models the average median distance measures only 0.60 mm and even the average 90% percentile stays below 1.5 mm. Furthermore, the MTM turned out to be the fastest model, with an average simulation time of only 10 s. Besides this quantitative validation, a qualitative validation study was carried out by eight maxillofacial surgeons, who scored the visualised predicted facial appearance by means of pre-defined statements. This study confirmed the positive results of the quantitative study, so we can conclude that fast and accurate predictions of the post-operative facial outcome are possible. Therefore, the usage of a maxillofacial soft tissue prediction system is relevant and suitable for daily clinical practice.

                Author and article information

                Department of Cranio-Maxillofacial Surgery, AZ MONICA, Harmoniestraat 68, B-2018 Antwerp, Belgium University Hospital Antwerp, Belgium
                [1 ]Medical Image Computing (Radiology - ESAT/PSI), Faculties of Medicine and Engineering University Hospital Gasthuisberg, Herestraat 49, B-3000 Leuven, Belgium
                [2 ]Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen, Medical Centre, Nijmegen, The Netherlands
                Author notes
                Address for correspondence: Prof. Nasser Nadjmi, Department of Cranio-Maxillofacial Surgery AZ MONICA, Harmoniestraat 68, 2018 Antwerp, Belgium. E-mail: nasser@
                Ann Maxillofac Surg
                Ann Maxillofac Surg
                Annals of Maxillofacial Surgery
                Medknow Publications & Media Pvt Ltd (India )
                Jul-Dec 2014
                : 4
                : 2
                : 171-175
                4293837 AMS-4-171 10.4103/2231-0746.147112
                Copyright: © Annals of Maxillofacial Surgery

                This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Original article - Comparative Study


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