In this paper we present an extensive quantitative validation on 3D facial soft tissue simulation for maxillofacial surgery planning. The study group contained 10 patients. In previous work we presented a new Mass Tensor Model to simulate the new facial appearance after maxillofacial surgery in a fast way. 10 patients were preoperatively CT-scanned and the surgical intervention was planned. 4 months after surgery, a post-operative control CT was acquired. In this study, the simulated facial outlook is compared with post-operative image data. After defining corresponding points between the predicted and actual post-operative facial skin surface, using a variant of the non-rigid TPS-RPM algorithm, distances between these correspondences are quantified and visualized in 3D. As shown, the average median distance measures only 0.60 mm and the average 90% percentile stays below 1.5 mm. We can conclude that our model clearly provides an accurate prediction of the real post-operative outcome and is therefore suitable for use in clinical practice.
Virtual surgery simulation plays an increasingly important role as a planning aid for the surgeon. A reliable simulation method to predict the surgical outcome of breast reconstruction and breast augmentation procedures does not yet exist. However, a method to pre-operatively assess the result of the procedure would be useful to ensure a symmetrical and naturally looking result, and could be a practical means of communication with the patient. In this paper, we present a basic framework to simulate a subglandular breast implantation. First, we propose a method to build a model of the patient's anatomy, based on a 3D picture of the skin surface in combination with thickness estimates of the soft tissue surrounding the breast. This approach is cheap, fast and the picture can be taken while the patient is standing upright, which makes it advantageous compared to conventional CTor MR-based methods. Second, a set of boundary conditions is defined to mimic the effect of the implant. Finally, we compute the new equilibrium geometry using the iterative FEM-based Mass Tensor Method, which is computationally more effcient than the traditional FEM approach since sufficient precision can be achieved with a limited number of iterations. We illustrate our approach with a preliminary validation study on 4 patients. We obtain promising results with a mean error between the simulated and the true post-operative breast geometry below 4 mm and maximal error below 10 mm, which is found to be sufficiently accurate for visual assessment in clinical practice.