Detection of bone loss via subchondral bone analysis

Jean-Baptiste Vimort; Antonio Ruellas; Jack Prothero; J. S. Marron; Matthew McCormick; Lucia Cevidanes; Erika Benavides; Beatriz Paniagua

doi:10.1117/12.2293654

12 March 2018 Detection of bone loss via subchondral bone analysis

Jean-Baptiste Vimort, Antonio Ruellas, Jack Prothero, J. S. Marron, Matthew McCormick, Lucia Cevidanes, Erika Benavides, Beatriz Paniagua

Author Affiliations +

Proceedings Volume 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging; 105780Q (2018) https://doi.org/10.1117/12.2293654
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

To date, there is no single sign, symptom, or test that can clearly diagnose early stages of Temporomandibular Joint Osteoarthritis (TMJ OA). However, it has been observed that changes in the bone occur in early stages of this disease, involving structural changes both in the texture and morphometry of the bone marrow and the subchondral cortical plate. In this paper we present a tool to detect and highlight subtle variations in subchondral bone structure obtained from high resolution Cone Beam Computed Tomography (hr-CBCT) in order to help with detecting early TMJ OA. The proposed tool was developed in ITK and 3DSlicer and it has been disseminated as open-source software tools. We have validated both our texture analysis and morphometry analysis biomarkers for detection of TMJ OA comparing hr-CBCT to μCT. Our initial statistical results using the multidimensional features computed with our tool indicate that it is possible to classify areas of demonstrated loss of trabecular bone in both μCT and hr-CBCT. This paper describes the first steps to alleviate the current inability of radiological changes to diagnose TMJ OA before morphological changes are too advanced by quantifying subchondral bone biomarkers. This paper indicates that texture based and morphometry based biomarkers have the potential to identify OA patients at risk for further bone destruction.

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Jean-Baptiste Vimort, Antonio Ruellas, Jack Prothero, J. S. Marron, Matthew McCormick, Lucia Cevidanes, Erika Benavides, and Beatriz Paniagua "Detection of bone loss via subchondral bone analysis", Proc. SPIE 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 105780Q (12 March 2018); https://doi.org/10.1117/12.2293654

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