Wouter Mollemans

Three-Dimensional Treatment Planning of Orthognathic Surgery in the Era of Virtual Imaging

PURPOSE The aim of this report was to present an integrated 3-dimensional (3D) virtual approach toward cone-beam computed tomography-based treatment planning of orthognathic surgery in the clinical routine. MATERIALS AND METHODS We have described the different stages of the workflow process for routine 3D virtual treatment planning of orthognathic surgery: 1) image acquisition for 3D virtual orthognathic surgery; 2) processing of acquired image data toward a 3D virtual augmented model of the patients head; 3) 3D virtual diagnosis of the patient; 4) 3D virtual treatment planning of orthognathic surgery; 5) 3D virtual treatment planning communication; 6) 3D splint manufacturing; 7) 3D virtual treatment planning transfer to the operating room; and 8) 3D virtual treatment outcome evaluation. CONCLUSIONS The potential benefits and actual limits of an integrated 3D virtual approach for the treatment of the patient with a maxillofacial deformity are discussed comprehensively from our experience using 3D virtual treatment planning clinically.

Predicting soft tissue deformations for a maxillofacial surgery planning system: from computational strategies to a complete clinical validation.

In the field of maxillofacial surgery, there is a huge demand from surgeons to be able to pre-operatively predict the new facial outlook after surgery. Besides the big interest for the surgeon during the planning, it is also an essential tool to improve the communication between the surgeon and his patient. In this work, we compare the usage of four different computational strategies to predict this new facial outlook. These four strategies are: a linear Finite Element Model (FEM), a non-linear Finite Element Model (NFEM), a Mass Spring Model (MSM) and a novel Mass Tensor Model (MTM). For true validation of these four models we acquired a data set of 10 patients who underwent maxillofacial surgery, including pre-operative and post-operative CT data. For all patient data we compared in a quantitative validation the predicted facial outlook, obtained with one of the four computational models, with post-operative image data. During this quantitative validation distance measurements between corresponding points of the predicted and the actual post-operative facial skin surface, are quantified and visualised in 3D. Our results show that the MTM and linear FEM predictions achieve the highest accuracy. For these models the average median distance measures only 0.60 mm and even the average 90% percentile stays below 1.5 mm. Furthermore, the MTM turned out to be the fastest model, with an average simulation time of only 10 s. Besides this quantitative validation, a qualitative validation study was carried out by eight maxillofacial surgeons, who scored the visualised predicted facial appearance by means of pre-defined statements. This study confirmed the positive results of the quantitative study, so we can conclude that fast and accurate predictions of the post-operative facial outcome are possible. Therefore, the usage of a maxillofacial soft tissue prediction system is relevant and suitable for daily clinical practice.

Semi-automated Ultrasound Facial Soft Tissue Depth Registration: Method and Validation

A mobile and fast, semi-automatic ultrasound (US) system was developed for facial soft tissue depth registration. The system consists of an A-Scan ultrasound device connected to a portable PC with interfacing and controlling software. For 52 cephalometric landmarks, the system was tested for repeatability and accuracy by evaluating intra-observer agreement and comparing ultrasound and CT-scan results on 12 subjects planned for craniofacial surgery, respectively. A paired t-test evaluating repeatability of the ultrasound measurements showed 5.7% (n = 3) of the landmarks being significantly different (p < 0.01). US and CT-scan results showed significant differences (p < 0.01) using a Wilcoxon signed rank test analysis for 11.5% (n = 6) of the landmarks. This is attributed to a difference in the volunteers head position between lying (CT) and sitting (US). Based on these tests, we conclude that the proposed registration system and measurement protocol allows relatively fast (52 landmarks/20 min), non-invasive, repeatable and accurate acquisition of facial soft tissue depth measurements.

Virtual occlusion in planning orthognathic surgical procedures.

Accurate preoperative planning is mandatory for orthognathic surgery. One of the most important aims of this planning process is obtaining good postoperative dental occlusion. Recently, 3D image-based planning systems have been introduced that enable a surgeon to define different osteotomy planes preoperatively and to assess the result of moving different bone fragments in a 3D virtual environment, even for soft tissue simulation of the face. Although the use of these systems is becoming more accepted in orthognathic surgery, few solutions have been proposed for determining optimal occlusion in the 3D planning process. In this study, a 3D virtual occlusion tool is presented that calculates a realistic interaction between upper and lower dentitions. It enables the surgeon to obtain an optimal and physically possible occlusion easily. A validation study, including 11 patient data sets, demonstrates that the differences between manually and virtually defined occlusions are small, therefore the presented system can be used in clinical practice.

Fast Soft Tissue Deformation with Tetrahedral Mass Spring Model for Maxillofacial Surgery Planning Systems

Maxillofacial surgery simulation and planning is an extremely challenging area of research combining medical imagery, computer graphics and mathematical modelling. In maxillofacial surgery abnormalities of the skeleton of the head are treat by skull remodelling. Since the human face plays a key role in interpersonal relationships, people are very sensitive to changes to their outlook. Therefore planning of the operation and reliable prediction of the facial changes are very important. Recently, the use of 3D image-based surgery planning systems is more and more accepted in this field. Although the bone-related planning concepts and methods are maturing, prediction of soft tissue deformation needs further fundamental research. In this paper we present a soft tissue simulator that uses a fast tetrahedral mass spring system to calculate soft tissue deformation due to bone displacement in a short time interval. Results of soft tissue simulation for patients who had a maxillofacial surgery are shown. Finally we truly validated the simulation results and compared our method with others.

Biomechanically based elastic breast registration using mass tensor simulation

We present a new approach for the registration of breast MR images, which are acquired at different time points for observation of lesion evolution. In this registration problem, it is of utmost importance to correct only for differences in patient positioning and to preserve other diagnostically important differences between both images, resulting from anatomical and pathological changes between both acquisitions. Classical free form deformation algorithms are therefore less suited, since they allow too large local volume changes and their deformation is not biomechanically based. Instead of adding constraints or penalties to these methods in order to restrict unwanted deformations, we developed a truly biomechanically based registration method where the position of skin and muscle surface are used as the only boundary conditions. Results of our registration method show an important improvement in correspondence between the reference and the deformed floating image, without introducing physically implausible deformations and within a short computational time.

Quantitative validation of a computer-aided maxillofacial planning system, focusing on soft tissue deformations

Aim: The aim of this study was to evaluate the accuracy of 3D soft tissue predictions generated by a computer-aided maxillofacial planning system in patients undergoing orthognathic surgery. Methods and Materials: Twenty patients with dentofacial dysmorphosis were treated with orthognathic surgery after a preoperative orthodontic treatment. Fourteen patients had an Angle Class II malocclusion; three patients had an Angle class III malocclusion, and three patients had an Angle Class I malocclusion. Skeletal asymmetry was observed in six patient. The surgeries were planned using the Maxilim software. Computer assisted surgical planning was transferred to the patient by digitally generated splints. The validation procedures were performed in the following steps: (1) Standardized registration of the pre- and postoperative Cone Beam CT volumes; (2) Automated adjustment of the bone-related planning to the actual operative bony displacement; (3) Simulation of soft tissue changes; (4) Calculation of the soft tissue differences between the predicted and the postoperative results by distance mapping. Statistical Analysis and Results: Eighty four percent of the mapped distances between the predicted and actual postoperative results measured between -2 mm and +2 mm. The mean absolute linear measurements between the predicted and actual postoperative surface was 1.18. Our study shows the overall prediction was dependent on neither the surgical procedures nor the dentofacial deformity type. Conclusion: Despite some shortcomings in the prediction of the final position of the lower lip and cheek area, this software promises a clinically acceptable soft tissue prediction for orthognathic surgical procedures.

Pre-operative simulation and post-operative validation of soft-tissue deformations for breast implantation planning

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 patients 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.

Automated Cephalometric Landmark Identification Using Shape and Local Appearance Models

In this paper a method is presented for the automated identification of cephalometric anatomical landmarks in craniofacial cone-beam CT images. This method makes use of statistical models, incorporating both local appearance and shape knowledge obtained from training data. Firstly, the local appearance model captures the local intensity pattern around each anatomical landmark in the image. Secondly, the shape model contains a local and a global component. The former improves the flexibility, whereas the latter improves the robustness of the algorithm. Using a leave-one-out approach to the training data, we assess the overall accuracy of the method. The mean and median error values for all landmarks are equal to 2.55mm and 1.72mm, respectively.

Image Segmentation Using Graph Representations and Local Appearance and Shape Models

A generic model-based segmentation algorithm is presented. Based on a set of training data, consisting of images with corresponding object segmentations, a local appearance and local shape model is build. The object is described by a set of landmarks. For each landmark a local appearance model is build. This model describes the local intensity values in the image around each landmark. The local shape model is constructed by considering the landmarks to be vertices in an undirected graph. The edges represent the relations between neighboring landmarks. By implying the markovianity property on the graph, every landmark is only directly dependent upon its neighboring landmarks, leading to a local shape model. The objective function to be minimized is obtained from a maximum a-posteriori approach. To minimize this objective function, the problem is discretized by considering a finite set of possible candidates for each landmark. In this way the segmentation problem is turned into a labeling problem. Mean field annealing is used to optimize this labeling problem. The algorithm is validated for the segmentation of teeth from cone beam computed tomography images and for automated cephalometric analysis.