An eFace-Template Method for Efficiently Generating Patient-Specific Anatomically-Detailed Facial Soft Tissue FE Models for Craniomaxillofacial Surgery Simulation

Abstract

Accurate surgical planning and prediction of craniomaxillofacial surgery outcome requires simulation of soft-tissue changes following osteotomy. This can only be accomplished on an anatomically-detailed facial soft tissue model. However, current anatomically-detailed facial soft tissue model generation is not appropriate for clinical applications due to the time intensive nature of manual segmentation and volumetric mesh generation. This paper presents a novel semi-automatic approach, named eFace-template method, for efficiently and accurately generating a patient-specific facial soft tissue model. Our novel approach is based on the volumetric deformation of an anatomically-detailed template to be fitted to the shape of each individual patient. The adaptation of the template is achieved by using a hybrid landmark-based morphing and dense surface fitting approach followed by a Thin-Plate Spline interpolation. This methodology was validated using 4 visible human datasets (regarded as gold standards) and 30 patient models. The results indicated that our approach can accurately preserve the internal anatomical correspondence (i.e., muscles) for finite element modeling (FEM). Additionally, our hybrid approach was able to achieve an optimal balance among the patient shape fitting accuracy, anatomical correspondence and mesh quality. Furthermore, the statistical analysis showed that our hybrid approach was superior to two previously published methods: mesh-matching and landmark-based transformation. Ultimately, our eFace-template method can be directly and effectively used clinically to simulate the facial soft tissue changes in the clinical application.

An eFTD-VP framework for efficiently generating patient-specific anatomically detailed facial soft tissue FE mesh for craniomaxillofacial surgery simulation

“…During both validations, parameter α in the surface registration Eq. (1) was set as 1, which produced a good balance between surface mesh quality and registration accuracy (Zhang et al 2016). It achieves registration error within 1 mm in average and produces triangular surface suitable for tetrahedral mesh generation using Tetgen (Si 2015).…”

“…3D CT soft tissue was replaced by the 3dMD soft tissue by registering the 3dMD photograph to the CT model. This was done to prevent any possible facial soft tissue strain during the CT scanning (Zhang et al 2016). All patient-specific models needed in the validations were successfully generated using our eFTD-VP approach, regardless of the class of their deformity.…”

“…In our previous study, the anatomically detailed eFace template has been generated from CT data and color gross anatomical cross-sectional images of the Chinese Visible Female (CVF) (Zhang et al 2016). The eFace template is a facial soft tissue model generated by forming a closed soft tissue surface (facial soft tissue envelope) that connects the skin (outer) surface and the mucosa (inner) surface adjacent to the skull.…”

“…First, landmark-based thin-plate splines (TPS) are applied to align the template skin and skull surfaces to the patient skin and skull surfaces. A previously validated group of easily identifiable anatomical landmarks (Zhang et al 2016) (45 on the skin, 26 on the maxilla, and 22 on the mandible) are manually digitized to guide the surface registration. TPS is used because it can generate smooth and non-rigid deformations simultaneously based on multiple landmarks (Chui and Rangarajan 2000).…”

“…One approach aims to construct an anatomically detailed FE mesh template that adapts to match each patient (Chabanas et al 2003; Bucki et al 2010; Lou et al 2012; Hung et al 2015; Zhang et al 2016). Although this approach is efficient, distorted elements are produced during superimposition and mesh quality is hard to be preserved after adaptation.…”