The ultimate goal of this project is to develop an open source novel imaging informatics platform, the AnatomicAligner, to improve the surgical planning method for craniomaxillofacial (CMF) surgery and subsequently to improve the treatment outcome of the patients with CMF deformities. CMF surgery involves the correction of congenital and acquired deformities of the skull and face. Due to the complex nature of the CMF skeleton, it requires extensive presurgical planning. Unfortunately, the traditional planning methods, e.g. prediction tracings and simulating surgery on stone models have remained unchanged over the last 50 years. Many unwanted surgical outcomes are the result of these deficient methods. To solve these problems, we have developed a Computer-Aided Surgical Simulation (CASS) system. Although it still needs significant improvements, the use of CASS has eliminated most of the limitations of the traditional methods. Unfortunately, it also creates a new problem that the digital establishment of dental occlusion becomes significantly more difficult. The dental articulation is an important step during the planning process to correct preexisting malocclusions or to surgically reestablish a new occlusion. The current gold standard is to utilize stone dental models and hand-articulate them on an articulator. Unfortunately, the same is not true in virtual world. These dental arches are 3D images. When the digital teeth are moved towards each other, they are not stopped by collision and continue to move through each other, which do not occur in real world. In order to completely solve these problems, it is critical to develop a new system that will integrate fully automated process of dental articulation and significantly improved our CASS technologies. Our hypotheses are that the occlusion can be digitally and automatically established in a computer planning system, and the computer-generated occlusion is as precise as the occlusion established by hand-articulating a set of stone models (the current gold standard). In order to prove our hypotheses, we are proposing three Specific Aims to develop and validate a novel imaging informatics platform, the AnatomicAligner, for CMF surgery. The system is innovative because for the first time, doctors will be able to efficiently and accurately plan the entire surgery in the computer, including automated establishment of dental occlusion. The new technical contributions include: 1) a robust 3D segmentation-based approach to achieve the initial digital dental model alignment;and 2) novel approaches for automated final digital articulation. The significance of this project is that the AnatomicAligner system will produce a paradigm shift in CMF planning. Surgeons will be able to completely abandon the problematic traditional methods for a more accurate, faster and cost effective method. The success of AnatomicAligner will lead to a new class of imaging informatics platform for CMF surgery. This platform can also be transformed to orthopedic surgery and other medical specialties. Once completed, the software (both source codes and executables) will be freely downloaded from internet by research community.

Public Health Relevance

In the surgical planning process of craniomaxillofacial surgeries, the articulation of dental models is an important step to correct preexisting malocclusions or to reestablish a new occlusion after it is disrupted by trauma, pathology or surgery. The traditional standard is to utilize stone dental models and articulate them by hand on an articulator. In order to solve the problems associated with the traditional planning methods and incorporate automated digital dental articulation for surgical planning, we are proposing to develop and validate an open source imaging informatics platform, the AnatomicAligner, for craniomaxillofacial surgery.

Agency
National Institute of Health (NIH)
Institute
National Institute of Dental & Craniofacial Research (NIDCR)
Type
Research Project (R01)
Project #
1R01DE022676-01
Application #
7948954
Study Section
Special Emphasis Panel (ZRG1-SBIB-Q (90))
Program Officer
Fischer, Dena
Project Start
2011-09-07
Project End
2014-08-31
Budget Start
2011-09-07
Budget End
2012-08-31
Support Year
1
Fiscal Year
2011
Total Cost
$388,750
Indirect Cost
Name
Methodist Hospital Research Institute
Department
Type
DUNS #
185641052
City
Houston
State
TX
Country
United States
Zip Code
77030
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
Yin, Q; Hung, S-C; Rathmell, W K et al. (2018) Integrative radiomics expression predicts molecular subtypes of primary clear cell renal cell carcinoma. Clin Radiol 73:782-791
Zhang, Yongqin; Shi, Feng; Cheng, Jian et al. (2018) Longitudinally Guided Super-Resolution of Neonatal Brain Magnetic Resonance Images. IEEE Trans Cybern :
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 :
Gateño, 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
Yin, Qingbo; Hung, Sheng-Che; Wang, Li et al. (2017) Associations between Tumor Vascularity, Vascular Endothelial Growth Factor Expression and PET/MRI Radiomic Signatures in Primary Clear-Cell-Renal-Cell-Carcinoma: Proof-of-Concept Study. Sci Rep 7:43356
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
Hughes, G N; Gateño, J; English, J D et al. (2017) There is variability in our perception of the standard head orientation. Int J Oral Maxillofac Surg 46:1512-1516
Wang, Jun; Wang, Qian; Peng, Jialin et al. (2017) Multi-task diagnosis for autism spectrum disorders using multi-modality features: A multi-center study. Hum Brain Mapp 38:3081-3097

Showing the most recent 10 out of 56 publications