Soft tissue prediction in orthognathic surgery: Improving accuracy by means of anatomical details

Ruggiero, Federica; Borghi, Alessandro; Bevini, Mirko; Badiali, Giovanni; Lunari, Ottavia; Dunaway, David; Marchetti, Claudio

Abstract

Three-dimensional virtual simulation of orthognathic surgery is now a well-established method in maxillo-facial surgery. The commercial software packages are still burdened by a consistent imprecision on soft tissue predictions. In this study, the authors produced an anatomically detailed patient specific numerical model for simulation of soft tissue changes in orthognathic surgery. Eight patients were prospectively enrolled. Each patient underwent CBCT and planar x-rays prior to surgery and in addition received an MRI scan. Postoperative soft-tissue change was simulated using Finite Element Modeling (FEM) relying on a patient-specific 3D models generated combining data from preoperative CBCT (hard tissue) scans and MRI scans (muscles and skin). An initial simulation was performed assuming that all the muscles and the other soft tissue had the same material properties (Homogeneous Model). This model was compared with the postoperative CBCT 3D simulation for validation purpose. Design of experiments (DoE) was used to assess the effect of the presence of the muscles considered and of their variation in stiffness. The effect of single muscles was evaluated in specific areas of the midface. The quantitative distance error between the homogeneous model and actual patient surfaces for the midface area was 0.55 mm, standard deviation 2.9 mm. In our experience, including muscles in the numerical simulation of orthognathic surgery, brought an improvement in the quality of the simulation obtained.

Citation

Ruggiero, F., Borghi, A., Bevini, M., Badiali, G., Lunari, O., Dunaway, D., & Marchetti, C. (2023). Soft tissue prediction in orthognathic surgery: Improving accuracy by means of anatomical details. PLoS ONE, 18(11), e0294640. https://doi.org/10.1371/journal.pone.0294640

• Journal Article Type: Article
• Acceptance Date: Nov 6, 2023
• Online Publication Date: Nov 27, 2023
• Publication Date: 2023
• Deposit Date: Dec 7, 2023
• Publicly Available Date: Dec 7, 2023
• Journal: PLOS ONE
• Electronic ISSN: 1932-6203
• Publisher: Public Library of Science
• Peer Reviewed: Peer Reviewed
• Volume: 18
• Issue: 11
• Pages: e0294640
• DOI: https://doi.org/10.1371/journal.pone.0294640
• Public URL: https://durham-repository.worktribe.com/output/1965759