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Extracting Voxel‐Based Cartilage Relaxometry Features in Hip Osteoarthritis Subjects Using Principal Component Analysis
Author(s) -
Liao TzuChieh,
Pedoia Valentina,
Neumann Jan,
Link Thomas M.,
Souza Richard B.,
Majumdar Sharmila
Publication year - 2020
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.26955
Subject(s) - relaxometry , medicine , cartilage , osteoarthritis , principal component analysis , radiography , voxel , magnetic resonance imaging , nuclear medicine , logistic regression , radiology , pathology , anatomy , artificial intelligence , computer science , spin echo , alternative medicine
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 T 1ρ /T 2 , 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 T 1ρ assessment, OA subjects demonstrated higher T 1ρ values in the posterior hip region and deep cartilage layer when compared with controls ( P = 0.012 and 0.001, respectively). In T 2 assessment, OA subjects exhibited higher T 2 values in the posterior hip region ( P < 0.001). Based on the PC score classification, 16 subjects without radiographic evidence of OA demonstrated relaxometry patterns similar to OA subjects, and exhibited worse physical function ( P = 0.003) and cartilage lesions ( P = 0.009–0.032) when compared with the remaining controls. Data Conclusion The study identified distinctive cartilage relaxometry features that were able to discriminate subjects with and without radiographic hip OA effectively. Level of Evidence: 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1708–1719.