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

Abstract

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 /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 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 T₂ assessment, OA subjects exhibited higher T₂ 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.

Keywords: T1ρ and T2; hip osteoarthritis; magnetic resonance imaging; principal component analysis; voxel-based relaxometry.

Figure 1.

Flowchart of subject enrollment. KL grade, Kellgren-Lawrence grading for radiographic osteoarthritis; OA: osteoarthritis.

Figure 2.

Hip joint subregions for the grading of Scoring Hip OA with MR imaging. (A) Acetabulum joint subregions seen from lateral aspect. Femur joint subregions seen from (B) medial aspect, (C) anterior aspect, and (D) posterior aspect. Reproduced with permission from Kumar et al., J Orthop Res, 2015.

Figure 3.

Illustration of the first 5 principal components (PCs) extracted from the Z-score T_1ρ mapping. Each PC is visualized at mean ± 3 mode variance. Mode 1: global elevation; mode 2: elevation in inferoposterior vs. central region (pink arrow); mode 3: elevation in acetabular (yellow arrow); mode 4: elevation in superficial vs. deep layer (white arrow); mode 5: elevation in inferoposterior and central regions (green arrow). * indicates PC mode to be significant predictor of the OA vs. control group classification.

Figure 4.

Illustration of the first 5 principal components (PCs) extracted from the Z-score T₂ mapping. Each PC is visualized at mean ± 3 mode variance. Mode 1: elevation in femoral vs. acetabular (pink arrow); mode 2: global elevation; mode 3: posterior vs. central region (yellow arrow); mode 4: acetabular vs. deep layer (white arrow); mode 5: anterior vs. posterior (green arrow). * indicates PC mode to be significant predictor of the OA vs. control group classification.

Figure 5.

Principal component plots based on the 3 modes that significantly predicted the radiographic OA vs. control group classification.