Statistical face models for the rediction of soft-tissue deformations after orthognathic osteotomies

Design, development and clinical validation of computer-aided surgical simulation system for streamlined orthognathic surgical planning

PURPOSE

There are many proven problems associated with traditional surgical planning methods for orthognathic surgery. To address these problems, we developed a computer-aided surgical simulation (CASS) system, the AnatomicAligner, to plan orthognathic surgery following our streamlined clinical protocol.

METHODS

The system includes six modules: image segmentation and three-dimensional (3D) reconstruction, registration and reorientation of models to neutral head posture, 3D cephalometric analysis, virtual osteotomy, surgical simulation, and surgical splint generation. The accuracy of the system was validated in a stepwise fashion: first to evaluate the accuracy of AnatomicAligner using 30 sets of patient data, then to evaluate the fitting of splints generated by AnatomicAligner using 10 sets of patient data. The industrial gold standard system, Mimics, was used as the reference.

RESULT

When comparing the results of segmentation, virtual osteotomy and transformation achieved with AnatomicAligner to the ones achieved with Mimics, the absolute deviation between the two systems was clinically insignificant. The average surface deviation between the two models after 3D model reconstruction in AnatomicAligner and Mimics was 0.3 mm with a standard deviation (SD) of 0.03 mm. All the average surface deviations between the two models after virtual osteotomy and transformations were smaller than 0.01 mm with a SD of 0.01 mm. In addition, the fitting of splints generated by AnatomicAligner was at least as good as the ones generated by Mimics.

CONCLUSION

We successfully developed a CASS system, the AnatomicAligner, for planning orthognathic surgery following the streamlined planning protocol. The system has been proven accurate. AnatomicAligner will soon be available freely to the boarder clinical and research communities.

Improved Rubin-Bodner model for the prediction of soft tissue deformations

In craniomaxillofacial (CMF) surgery, a reliable way of simulating the soft tissue deformation resulted from skeletal reconstruction is vitally important for preventing the risks of facial distortion postoperatively. However, it is difficult to simulate the soft tissue behaviors affected by different types of CMF surgery. This study presents an integrated bio-mechanical and statistical learning model to improve accuracy and reliability of predictions on soft facial tissue behavior. The Rubin-Bodner (RB) model is initially used to describe the biomechanical behavior of the soft facial tissue. Subsequently, a finite element model (FEM) computers the stress of each node in soft facial tissue mesh data resulted from bone displacement. Next, the Generalized Regression Neural Network (GRNN) method is implemented to obtain the relationship between the facial soft tissue deformation and the stress distribution corresponding to different CMF surgical types and to improve evaluation of elastic parameters included in the RB model. Therefore, the soft facial tissue deformation can be predicted by biomechanical properties and statistical model. Leave-one-out cross-validation is used on eleven patients. As a result, the average prediction error of our model (0.7035mm) is lower than those resulting from other approaches. It also demonstrates that the more accurate bio-mechanical information the model has, the better prediction performance it could achieve.

Prediction of soft tissue deformations after CMF surgery with incremental kernel ridge regression

Facial soft tissue deformation following osteotomy is associated with the corresponding biomechanical characteristics of bone and soft tissues. However, none of the methods devised to predict soft tissue deformation after osteotomy incorporates population-based statistical data. The aim of this study is to establish a statistical model to describe the relationship between biomechanical characteristics and soft tissue deformation after osteotomy. We proposed an incremental kernel ridge regression (IKRR) model to accomplish this goal. The input of the model is the biomechanical information computed by the Finite Element Method (FEM). The output is the soft tissue deformation generated from the paired pre-operative and post-operative 3D images. The model is adjusted incrementally with each new patient's biomechanical information. Therefore, the IKRR model enables us to predict potential soft tissue deformations for new patient by using both biomechanical and statistical information. The integration of these two types of data is critically important for accurate simulations of soft-tissue changes after surgery. The proposed method was evaluated by leave-one-out cross-validation using data from 11 patients. The average prediction error of our model (0.9103mm) was lower than some state-of-the-art algorithms. This model is promising as a reliable way to prevent the risk of facial distortion after craniomaxillofacial surgery.

Soft-tissue changes associated with different surgical procedures for treating class III patients

OBJECTIVE

The aim of this study was to evaluate the effects of different surgical procedures on soft tissue and skeletal changes.

PATIENTS AND METHODS

Pre- and postoperative lateral cephalograms of 191 class III patients aged 16-58 years were retrospectively analyzed and divided into three groups according to the surgical therapy they had undergone. Group I had undergone maxillary advancement (n=51), group II a bimaxillary (n=102) procedure, and group III mandibular setback (n=38). Cephalometric assessments were made at the beginning of orthodontic treatment (T1) and at least 8 weeks after surgical correction of the class III malocclusion (T2). Nasolabial angle and the distance between the lower lip and the esthetic line were evaluated as soft-tissue parameters, while the skeletal parameters included the SNA and SNB angles, Wits value, gonial angle, and relative position of the maxilla to mandible. Mean values and standard deviations were calculated and any significant difference between soft and hard tissue in association with surgery methods was determined applying the Wilcoxon test using SAS statistical software.

RESULTS

Compared to mandible setback, the number of bimaxillary procedures and maxilla advancements has increased significantly in recent years (0.04). SNA values measured at T2 were an average of 84° in group I, 83° in group II, and 82° in group III, thus, revealing a tendency to return to the normal range. SNB values measured at T2 averaged 81° in group I, 80° in group II, and 81° in group III. We observed significant changes in the nasolabial angle according to the surgical approach taken (p=0.018). Changes in the distance between the lower lip and the esthetic line were only slightly significant (p=0.050). No significant differences were noted among the surgical approaches in terms of SNA and SNB angles.

CONCLUSION

The two groups that underwent advancement of the maxilla (groups I and II) revealed marked improvements in soft-tissue esthetics.

Incremental kernel ridge regression for the prediction of soft tissue deformations

This paper proposes a nonlinear regression model to predict soft tissue deformation after maxillofacial surgery. The feature which served as input in the model is extracted with finite element model (FEM). The output in the model is the facial deformation calculated from the preoperative and postoperative 3D data. After finding the relevance between feature and facial deformation by using the regression model, we establish a general relationship which can be applied to all the patients. As a new patient comes, we predict his/her facial deformation by combining the general relationship and the new patient's biomechanical properties. Thus, our model is biomechanical relevant and statistical relevant. Validation on eleven patients demonstrates the effectiveness and efficiency of our method.