Soft-Tissue Simulation for Computational Planning of Orthognathic Surgery.
Patricia Alcañiz AladrénJesús PérezAlessandro GutiérrezHéctor BarreiroÁngel VillalobosDavid MirautCarlos IllanaJorge GuiñalesMiguel A OtaduyPublished in: Journal of personalized medicine (2021)
Simulation technologies offer interesting opportunities for computer planning of orthognathic surgery. However, the methods used to date require tedious set up of simulation meshes based on patient imaging data, and they rely on complex simulation models that require long computations. In this work, we propose a modeling and simulation methodology that addresses model set up and runtime simulation in a holistic manner. We pay special attention to modeling the coupling of rigid-bone and soft-tissue components of the facial model, such that the resulting model is computationally simple yet accurate. The proposed simulation methodology has been evaluated on a cohort of 10 patients of orthognathic surgery, comparing quantitatively simulation results to post-operative scans. The results suggest that the proposed simulation methods admit the use of coarse simulation meshes, with planning computation times of less than 10 seconds in most cases, and with clinically viable accuracy.
Keyphrases
- soft tissue
- virtual reality
- minimally invasive
- computed tomography
- high resolution
- magnetic resonance imaging
- end stage renal disease
- machine learning
- ejection fraction
- coronary artery disease
- acute coronary syndrome
- deep learning
- health insurance
- prognostic factors
- electronic health record
- artificial intelligence
- surgical site infection
- fluorescence imaging
- big data
- ionic liquid