Open AccessDetecting human articular cartilage degeneration in its early stage with polarization-sensitive optical coherence tomography
Author(s) -
Xin Zhou,
Felipe Eltit,
Jianshu Li,
Sina Maloufi,
Hanadi Alousaimi,
Qihao Liu,
Lin Huang,
Rizhi Wang,
Shuo Tang
Publication year - 2020
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.387242
Subject(s) - optical coherence tomography , osteoarthritis , cartilage , articular cartilage , materials science , grading (engineering) , birefringence , biomedical engineering , stage (stratigraphy) , medicine , optics , radiology , pathology , anatomy , biology , physics , ecology , alternative medicine , paleontology
Detecting articular cartilage (AC) degeneration in its early stage plays a critical role in the diagnosis and treatment of osteoarthritis (OA). Polarization-sensitive optical coherence tomography (PS-OCT) is sensitive to the alteration and disruption of collagen organization that happens during OA progression. This study proposes an effective OA evaluating method based on PS-OCT imaging. A slope-based analysis is applied on the phase retardation images to segment articular cartilage into three zones along the depth direction. The boundaries and birefringence coefficients (BRCs) of each zone are quantified. Two parameters, namely phase homogeneity index (PHI) and zonal distinguishability ( D z), are further developed to quantify the fluctuation within each zone and the zone-to-zone variation of the tissue birefringence properties. The PS-OCT based evaluating method then combines PHI and D z to provide a G PS score for the severity of OA. The proposed method is applied to human hip joint samples and the results are compared with the grading by histology images. The G PS score shows very strong statistical significance in differentiating different stages of OA. Compared to using the BRC of each zone or a single BRC for the entire depth, the G PS score shows great improvement in differentiating early-stage OA. The proposed method is shown to have great potential to be developed as a clinical tool for detecting OA.