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Validation of CBCT for the Computation of Textural Biomarkers

1Departments of Psychiatry, Computer Science and Orthodontics., University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
2University of Michigan, School of Dentistry, MI, USA.
3Department of Statistics and Operational Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
4Texas A&M Health Science Center, Baylor College of Dentistry, TX, USA.
Publication Date:
SPIE 2015 Feb;
Temporomandibular joint, Osteoarthritis, subchondral bone, texture analysis
Appears in Collections:
U54 EB005149/EB/NIBIB NIH HHS/United States
R01 DE024450/DE/NIDCR NIH HHS/United States
Generated Citation:
Paniagua B., Ruellas A.C., Benavides E., Marron S., Woldford L., Cevidanes L.H. Validation of CBCT for the Computation of Textural Biomarkers. SPIE 2015 Feb;
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Osteoarthritis (OA) is associated with significant pain and 42.6% of patients with TMJ disorders present with evidence of TMJ OA. However, OA diagnosis and treatment remain controversial, since there are no clear symptoms of the disease. The subchondral bone in the TMJ is believed to play a major role in the progression of OA. We hypothesize that the textural imaging biomarkers computed in high resolution Conebeam CT (hr- CBCT) and μCT scans are comparable. The purpose of this study is to test the feasibility of computing textural imaging biomarkers in-vivo using hr-CBCT, compared to those computed in μCT scans as our Gold Standard. Specimens of condylar bones obtained from condylectomies were scanned using μCT and hr- CBCT. Nine different textural imaging biomarkers (four co-occurrence features and five run-length features) from each pair of μCT and hr-CBCT were computed and compared. Pearson correlation coefficients were computed to compare textural biomarkers values of μCT and hr-CBCT. Four of the nine computed textural biomarkers showed a strong positive correlation between biomarkers computed in μCT and hr-CBCT. Higher correlations in Energy and Contrast, and in GLN (grey-level non-uniformity) and RLN (run length nonuniformity) indicate quantitative texture features can be computed reliably in hr-CBCT, when compared with μCT. The textural imaging biomarkers computed in-vivo hr-CBCT have captured the structure, patterns, contrast between neighboring regions and uniformity of healthy and/or pathologic subchondral bone. The ability to quantify bone texture non-invasively now makes it possible to evaluate the progression of subchondral bone alterations, in TMJ OA.

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