Extracting Voxel‐Based Cartilage Relaxometry Features in Hip Osteoarthritis Subjects Using Principal Component Analysis

Background MRI‐based relaxation time measurements provide quantitative assessment of cartilage biochemistry. Identifying distinctive relaxometry features in hip osteoarthritis (OA) might provide important information on regional disease variability. Purpose First, to incorporate fully automatic voxel‐based relaxometry (VBR) with principal component analysis (PCA) to extract distinctive relaxometry features in subjects with radiographic hip OA and nondiseased controls. Second, to use the identified features to further distinguish subjects with cartilage compositional abnormalities. Study Type Cross‐sectional. Subjects Thirty‐three subjects with radiographic hip OA (20 males; age, 50.2 ± 13.3 years) and 55 controls participated (28 males; 41.3 ± 12.0 years). Sequence A 3.0T scanner using 3D SPGR, combined T1ρ/T2, and fast spin echo sequences. Assessment Pelvic radiographs, patients' self‐reported symptoms, physical function, and cartilage morphology were analyzed. Cartilage relaxation times were quantified using traditional regions of interest and VBR approaches. PCA was performed on VBR data to identify distinctive relaxometry features, and were subsequently used to identify a subgroup of subjects from the controls that exhibited compositional abnormalities. Statistical Tests Chi‐square and independent t‐tests were used to compare group characteristics. Logistic regression models were used to identify the possible principal components (PCs) that were able to predict OA vs. control classification. Results In T1ρ assessment, OA subjects demonstrated higher T1ρ values in the posterior hip region and deep cartilage layer when compared with controls (P = 0.012 and 0.001, respectively). In T2 assessment, OA subjects exhibited higher T2 values in the posterior hip region (P