The goals of the proposed research are 1) to investigate a novel framework to improve assessments of temporomandibular joint osteoarthritis; 2) to develop computational algorithms that extend the capabilities of 3D Slicer open-source software, in collaboration with the National Alliance for Medical Imaging Computing (NA-MIC), one of the NIH National Centers for Biomedical Computing; and 3) oversee the training and dissemination of these tools to the dental research community. The statistical modeling framework we previously developed and validated has allowed us to precisely localize and accurately quantify the extent of degenerative changes in the mandibular condyles. The novel quantification methodology included in this proposal will provide specific analytical tools for the detection, pathology characterization and treatment monitoring of diseases of arthritic origin. Building from this, the proposed mapping of the architecture of the osteoarthritic condyles using imaging criteria (such as condylar flattening, erosions and bone overgrowth) is an excellent model to facilitate detection of osteoarthritic changes, to monitor treatment outcomes, and to provide the foundation for the development of joint deterioration prevention strategies. This proposed research benefits from the combined efforts of a team of clinicians, and computer scientists and engineers who are active members of the NA-MIC algorithm and engineering cores. This research team brings special resources to enable the broader objective of developing an infrastructure for image analysis to be used in leading-edge dental clinical research and practice.
The inability to quantify bone damage is a severe bottleneck in the discovery of imaging diagnostic markers and therapeutic targets for Osteoarthritis. This proposal will alleviate such information bottleneck by precisely quantifying three-dimensional bony changes in the Temporomandibular joint (TMJ). The proposed mapping of arthritic TMJs and detection of changes that occur with progression of the disease will allow improved early diagnosis and better monitoring of treatment outcomes by generating and disseminating a publicly available set of tools based in 3DSlicer.
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