The number of patients suffering from craniomaxillofacial (CMF) deformities and requiring surgical correction is escalating. CMF deformities may involve skeleton, overlying soft-tissues, or the both. Patients with CMF deformities often have psychological problems. The goal of CMF surgery is to reconstruct a normal facial appearance and function, and the outcome of the surgery is judged as such. The current problem is that we do not have a reliable way of simulating the soft-tissue-change following skeletal reconstruction. In treating patients with isolated skeletal defects, the current practice is to normalize the skeleton, hoping for optimal facial appearance. However, because the thickness and contour of the soft-tissue envelope varies from patient to patient, this approach is not reliable. The problem is even bigger in patients with composite defects. For example, in the scenario of a patient with a skeletal deformity and a mild soft-tissue defect, a surgeon would have to know, before surgery, how to overcorrect the skeleton to camouflage the soft-tissue defect. But, this information can only be attained by having an accurate planning system to simulate soft-tissue changes. In addition, from patient's perspective, the final facial appearance is the most apparent to them. Therefore, it is extremely important, for both doctors and patients, to accurately simulate soft-tissue-deformation. Simulation methods must be accurate and fast. Attaining both is difficult because these attributes are inversely related, the more accurate the model, the longer it takes to prepare and run. Among the most effective, they are empirical-based model, mass spring model, finite element model, and mass tensor model. Unfortunately they are either too inaccurate or too slow, and clinically unacceptable. Our hypothesis is that facial soft-tissue changes following virtual osteotomy can be accurately simulated by our innovative approach using an anatomically detailed modeling and mapping routine, along with statistical modeling technique. To test our hypothesis, we propose to develop an open source novel imaging informatics platform, eFace system, to accurately simulate soft-tissue-change following virtual osteotomies, and thus to significantly improve the outcomes of patients undergoing facial reconstruction. This approach can not only maintain the integrity of complex facial anatomy to accurately simulate the facial soft tissue deformation, but also significantly improve the computational efficiency in order to fit the requirement for clinical use This project presents an innovative approach to model the facial soft-tissue deformation. If successful, it will allow accurate simulation of soft-tissue changes after virtual osteotomy. Patients will also be able to foresee the postoperative face preoperatively (patient education) and regain their psychological confidence. Finally, eFace will have significant impact and applications in orthodontics, plastic surgery, general surgery, growth/aging prediction, and forensic science.
In the US and throughout the world, the number of patients requiring surgical correction for facial deformities, which involves skeleton, overlying soft tissu, or both, is escalating every year. 2. Currently, while surgeons are able to accurately plan bone reconstructive surgery, they have to use their visual imagination with clinical experience to mentally predict the facial soft tissue changes following the bone surgery because they do not have a reliable way of simulating the soft-tissue-change that is resulted from skeletal reconstruction. 3. We are proposing to develop an open source novel imaging informatics platform, eFace system, to accurately simulate soft-tissue-change following virtual skeletal surgery and thus to significantly improve the outcomes of patients undergoing facial reconstruction.
|Ren, Yi; Wang, Li; Gaol, Yaozong et al. (2014) Estimating anatomically-correct reference model for craniomaxillofacial deformity via sparse representation. Med Image Comput Comput Assist Interv 17:73-80|
|Wang, Li; Chen, Ken Chung; Gao, Yaozong et al. (2014) Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization. Med Phys 41:043503|