Our main clinical objective for this project is to provide personalized precision care to patients with craniomaxillofacial (CMF) deformities by significantly improving the surgical planning method. CMF deformities involve congenital and acquired deformities of the jaws and face. A large number of patients in the US and around the world suffer from CMF deformities. The basic principles of CMF surgery involve the restoration of deformed CMF structures back to normal anatomy and functions with osteotomy, autologous, bone grafts, or vascularized free flaps. The success of CMF surgery depends on not only the technical aspects of the operation, but also, to a large extent, the precise formulation of a surgical plan. However, CMF surgical planning is extremely challenging due to the complex nature of CMF anatomy and deformity. During a routine CMF surgical planning, a surgeon first acquires a three-dimensional (3D) model of the patient's skull. He then performs 3D cephalometric analysis to quantify the deformity. Finally, the surgery is simulated by virtually cutting the 3D model into multiple bony segments. The surgeon then tries his best to move and rotate each segment individually to a desired position within the normal range of cephalometric values (the current standard of care). This is problematic as ?normal? cephalometric values are the averageness of normal population, in which each value has a mean and a standard deviation. Due to the variation within the normal values, the surgeon must often guess what the exact value the patient's cephalometric measurement should be corrected to. In addition, cephalometry is a group of only linear and angular measurements, which certainly cannot represent the complex nature of human CMF structures. Therefore, surgical outcomes are often subjective and heavily dependent on the surgeons' experience and artistic talent. Because each human face is different, the average ?normal values? cannot represent the complex morphology of each individual face. To this end, we hypothesize that if a surgeon can foresee what the normal CMF shape of the patient should be, the surgical plan will be objective and personalized. Therefore, in this project, we propose developing and validating a new surgical planning method of using patient-specific and anatomically-correct reference models. The feasibility of our approach has already been proven by our preliminary studies. The results of this project will significantly improve the quality of patient care by developing personalized and precise surgical plans for CMF surgery objectively. The results will be especially beneficial to patients with jaw deformities, syndromic and non-syndromic craniofacial deformities, trauma, and CMF cancer. In the future, our approach can also be used to design and print 3D patient-specific resorbable bone implants with tissue engineering capability for bone regeneration.

Public Health Relevance

? The current standard of care of making treatment plan for patients with craniomaxillofacial (CMF) deformities is cephalometric analysis, including a group of linear and angular measurements, which cannot represent the complex morphology of human CMF facial structures. ? We propose to develop and validate an innovative shape-based surgical planning method of using patient-specific and anatomically-correct reference models, for replacing the current subjective cephalometric method. ? The results of this project will significantly improve the quality of patient care by developing personalized and precise surgical plans for CMF surgery objectively, which is especially beneficial to the patients with jaw deformities, syndromic and non-syndromic deformities, trauma, and CMF cancer.

Agency
National Institute of Health (NIH)
Institute
National Institute of Dental & Craniofacial Research (NIDCR)
Type
Research Project (R01)
Project #
1R01DE027251-01
Application #
9390559
Study Section
Bioengineering, Technology and Surgical Sciences Study Section (BTSS)
Program Officer
Fischer, Dena
Project Start
2017-07-01
Project End
2022-06-30
Budget Start
2017-07-01
Budget End
2018-06-30
Support Year
1
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Methodist Hospital Research Institute
Department
Type
DUNS #
185641052
City
Houston
State
TX
Country
United States
Zip Code
77030
Zhao, Miaoyun; Wang, Li; Chen, Jiawei et al. (2018) Craniomaxillofacial Bony Structures Segmentation from MRI with Deep-Supervision Adversarial Learning. Med Image Comput Comput Assist Interv 11073:720-727
Sutton, Peter H; Gateno, Jaime; English, Jeryl D et al. (2018) Both the Observer's Expertise and the Subject's Facial Symmetry Can Affect Anatomical Position of the Head. J Oral Maxillofac Surg :
GateƱo, J; Jones, T L; Shen, S G F et al. (2018) Fluctuating asymmetry of the normal facial skeleton. Int J Oral Maxillofac Surg 47:534-540
Zhang, Xiaoyan; Kim, Daeseung; Shen, Shunyao et al. (2018) An eFTD-VP framework for efficiently generating patient-specific anatomically detailed facial soft tissue FE mesh for craniomaxillofacial surgery simulation. Biomech Model Mechanobiol 17:387-402
Nie, Dong; Wang, Li; Trullo, Roger et al. (2017) Segmentation of Craniomaxillofacial Bony Structures from MRI with a 3D Deep-Learning Based Cascade Framework. Mach Learn Med Imaging 10541:266-273
Zhang, Jun; Liu, Mingxia; Wang, Li et al. (2017) Joint Craniomaxillofacial Bone Segmentation and Landmark Digitization by Context-Guided Fully Convolutional Networks. Med Image Comput Comput Assist Interv 10434:720-728
Li, B; Shen, S; Jiang, W et al. (2017) A new approach of splint-less orthognathic surgery using a personalized orthognathic surgical guide system: A preliminary study. Int J Oral Maxillofac Surg 46:1298-1305
Yuan, Peng; Mai, Huaming; Li, Jianfu et al. (2017) Design, development and clinical validation of computer-aided surgical simulation system for streamlined orthognathic surgical planning. Int J Comput Assist Radiol Surg 12:2129-2143