title: Improved Rubin-Bodner model for the prediction of soft tissue deformations.
abstract: 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.
authors.affiliation: Department of Radiology, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, USA.
authors.forename: Guangming
authors.initials: G
authors.lastname: Zhang
authors.affiliation: The Methodist Hospital Research Institute, Weil Cornell Medical College, Houston, TX 77030, USA.
authors.forename: James J
authors.initials: JJ
authors.lastname: Xia
authors.affiliation: Department of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Liebschner
authors.affiliation: The Methodist Hospital Research Institute, Weil Cornell Medical College, Houston, TX 77030, USA.
authors.forename: Xiaoyan
authors.initials: X
authors.lastname: Zhang
authors.affiliation: The Methodist Hospital Research Institute, Weil Cornell Medical College, Houston, TX 77030, USA.
authors.forename: Daeseung
authors.initials: D
authors.lastname: Kim
authors.affiliation: Department of Radiology, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, USA. Electronic address: xizhou@wakehealth.edu.
authors.forename: Xiaobo
authors.initials: X
authors.lastname: Zhou
mesh_terms: D001696:Biomechanical Phenomena; D005147:Facial Bones / Q000000981:diagnostic imaging; D005151:Facial Injuries / Q000000981:diagnostic imaging / Q000209:etiology* / Q000473:pathology*; D005260:Female; D020342:Finite Element Analysis; D006801:Humans; D015233:Models, Statistical; D012189:Retrospective Studies; D013314:Stress, Mechanical; D013515:Surgery, Oral; D014057:Tomography, X-Ray Computed
keywords: Craniomaxillofacial surgery; Generalized regression neural network; Rubin–Bodner model; Soft facial tissue; Stress distribution