A semi‐automatic framework based upon quantitative analysis of MR‐images for classification of femur cartilage into asymptomatic, early OA, and advanced‐OA groups

Abstract

To develop a semi-automatic framework for quantitative analysis of biochemical properties and thickness of femur cartilage using magnetic resonance (MR) images and evaluate its potential for femur cartilage classification into asymptomatic (AS), early osteoarthritis (OA), and advanced OA groups. In this study, knee joint MRI data (fat suppressed-proton density-weighted and multi-echo T2-weighted images) of eight AS-volunteers (data acquired twice) and 34 OA patients including 20 early OA (16 Grade-I and 4 Grade-II), 14 advanced-OA (Grade-III) were acquired at 3.0T MR scanner. Modified Outerbridge classification criteria was performed for the clinical evaluation of data by an experienced radiologist. Cartilage segmentation, T2mapping, 2D-WearMap generation, and subregion analysis were performed semiautomatically using in-house developed algorithms. The intraclass correlation coefficient (ICC) and coefficient of variation (CV) were computed for testing the reproducibility of T2 values. One-way analysis of variance with Tukey-Kramer post hoc test was performed for evaluating the differences among the groups. The performance of individual T2 and thickness, as well as their combination using logistic regression, were evaluated with receiver operating characteristics (ROC) curve analysis. The interscan agreement based on the ICC index was 0.95 and the CV was 2.45 ± 1.33%. T2 mean of values greater than 75th percentile showed sensitivity and specificity of 94.1% and 81.3% (AUC = 0.93, cut-off value = 47.9 ms) in differentiating AS volunteers versus OA group, while sensitivity and specificity of 90.0% and 81.3% (AUC = 0.90, cut-off value = 47.9 ms) in differentiating AS volunteers versus early OA groups, respectively. In the differentiation of early OA versus advanced-OA group, ROC results of combination (T2 and thickness) showed the highest sensitivity and specificity of 85.7%, and 70.0% (AUC = 0.79, cut-off value = 0.39) compared with individual T2 and thickness features, respectively. A computeraided quantitative evaluation of femur cartilage degeneration showed promising results and can be used to assist clinicians in diagnosing OA.

Device for Assessing Knee Joint Dynamics During Magnetic Resonance Imaging

“…For TFBG evaluation, the tibiofemoral bone gap ROI's normal gap between each pixel of interface with bones was measured for each slice and then fed in a 2D-WearMap row. 37 Further, the average of TFBG at four regions on bone gap 2D-WearMap: 1) the whole lateral and medial compartment profile and 2) lateral and medial regions where femur cartilage makes contact with tibial cartilage were computed. Figure 4 shows the pipeline of data analysis.…”

“…The procedure of generating 2D-WearMap is described in the reported study. 37 Similarly, FCT2 and TCT2 of each region were evaluated from the 2D-WearMap of the cartilage's T 2 -value map. The T 2 -map or CartiGram was generated by fitting mono-exponential function 37 given as follows:…”

“…The cartilage‐thickness 2D‐WearMap was developed by feeding the values of tangential thickness at each pixel where cartilage interfaces with bone for each slice, as shown in the steps of Figure 4. The procedure of generating 2D‐WearMap is described in the reported study 37 …”

“…Similarly, FCT2 and TCT2 of each region were evaluated from the 2D‐WearMap of the cartilage's T 2 ‐value map. The T 2 ‐map or CartiGram was generated by fitting mono‐exponential function 37 given as follows: where S (TE) is the signal intensity of the T 2 ‐weighted image at echo time (TE); T 2 is the transverse relaxation time, S 0 is the initial signal intensity, and c is a constant are parameters to be estimated. The 2D‐WearMap of the cartilage's T 2 ‐value map was developed by averaging the T 2 ‐values across tangential cartilage thickness, as shown in the steps of Figure 4.…”