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.

Spring assisted cranioplasty: A patient specific computational model

“…Recent developments in the field have addressed soft tissue displacement as an effect of rigid translation on underlying tissues using thin plate spline interpolation [15,16]; however the accuracy of such methods, which ignore tissue extensibility and compressibility and only ensures continuity of displacement, is still debated [17,18]. Latest attempts to include the physical behavior of soft and hard tissue through FE modelling of the skull have proven to provide better results [19][20][21][22] than previous approaches, with one study also focusing on sagittal SAC [23] with the aim of predicting the expansion force in a cohort of scaphocephalic patients treated with springs: the model was able to capture the on-table spring performance with expansion forces within 10% of the on-table measurements. The region of interest was however limited to the calvarial strip relative to the osteotomy border and, therefore, the model was unable to predict the final skull shape over time.…”

A Comparative Study of Analog Preoperative Planning Versus Virtual Preoperative Planning for Mandibular Reconstruction With Fibula Free Flap

“…[22][23][24] In this context, studies focusing on finite element analysis have contributed to the development of new technologies within the field of medicine, allowing for a great number of simulations such as the influence of mechanical forces and soft tissue predictions. 22,25 The finite element method brings the possibility of performing anatomical as well as alloplastic reconstruction with the possibility of better analyzing the biomechanical factors that contribute to the outcomes of the procedures. 22 The finite element method can be of use in reconstructive surgery since it allows for a more predictable treatment; helping to determine the best geometric characteristics of a certain device such as the custom reconstruction plates.…”

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

“…However, preparation and computation time for FEM is significant (from hours to days). Our laboratory has also solved this problem by using an eFace-template method to efficiently generate a patient-specific and anatomically detailed facial soft tissue FEM within minutes [61–63], which is also under rigorous clinical testing now. We hope to incorporate both features into our AnatomicAligner system in the near future.…”