Statistical Face Models for the Prediction of Soft-Tissue Deformations After Orthognathic Osteotomies

This paper describes a technique to approximately predict the facial morphology after standardized orthognathic ostoetomies. The technique only relies on the outer facial morphology represented as a set of surface points and does not require computed tomography (CT) images as input. Surface points may either be taken from 3D surface scans or from 3D positions palpated on the face using a tracking system. The method is based on a statistical model generated from a set of pre- and postoperative 3D surface scans of patients that underwent the same standardized surgery. The model contains both the variability of preoperative facial morphologies and the corresponding postoperative deformations. After fitting the preoperative part to 3D data from a new patient the preoperative face is approximated by the model and the prediction of the postoperative morphology can be extracted at the same time. We built a model based on a set of 15 patient data sets and tested the predictive power in leave-one-out tests for a set of relevant cephalometric landmarks. The average prediction error was found to be between 0.3 and 1.2 mm at all important facial landmarks in the relevant areas of upper and lower jaw. Thus the technique provides an easy and powerful way of prediction which avoids time, cost and radiation required by other prediction techniques such as those based on CT scans.