T2 Relaxation Time Varies Within the Load-Bearing Regions of Non-OA Femoral Cartilage

Purpose: Magnetic resonance imaging (MRI) is a crucial tool in assessing knee osteoarthritis (OA). Specifically, T2 relaxometry MRI analysis is a well-accepted, noninvasive method to characterize biochemical properties of cartilage, in particular the water content and collagen organization. While T2 relaxation time measures are frequently performed in OA research, there is no standard method to report these data. Most commonly a region of interest is defined in a particular MRI slice and the T2 relaxation times averaged over this region. This two-dimensional approach may lose important information as it quantifies T2 relaxation time in one particular location. However, there may be spatial variations in the cartilage biochemical properties assessed by T2 measurement, an observation supported by studies showing femoral cartilage thickness variations by subregions. Given that standard subregions exist in cartilage thickness literature, it is of interest to develop a three-dimensional method to analyze T2 relaxation times based on the same subregions. This study aimed to design such a method and test the hypothesis that there are variations in femoral cartilage T2 relaxation time among subregions in non-OA knees. Methods: After approval from the ethics committee, two MRI scans were performed for 13 healthy volunteers (5 female, BMI 23.8+/−3.4 (mean+/-SD) kg/m2, 31.2+/−4.6 years) using a 3T Siemens device: double echo steady state (DESS) morphological sequence (voxel size 0.6 × 0.6 × 0.6 mm) and multi-slice, multi-echo T2 sequence (voxel size 0.3 × 0.3 × 3 mm), from which T2 relaxation times were calculated. Cartilage and bone boundaries were semi-manually segmented on the DESS images using validated in-house software, yielding three-dimensional models of the femoral bone and cartilage. For each knee, these models were then imported into the T2 dataset using a registration process based on the shape of the femoral bone. Next, the load-bearing regions of the medial and lateral condyles, as well as three subregions of interest per condyle, were determined on the registered bone model, as commonly done in cartilage thickness analyses (Fig. 1). Finally, the voxels of the T2 dataset corresponding to the cartilage covering the load-bearing regions and subregions of the bone were identified, and the T2 relaxation time in these voxels averaged, resulting in one mean T2 value per region/subregion of interest. Repeated measures ANOVA and post-hoc paired t-tests were used to compare the average T2 values across regions/subregions using an alpha level set a priori at 5% with Bonferroni correction for multiple comparisons. Results: The average T2 relaxation times were not significantly different between the lateral and medial regions (47.0+/−7.3 vs 51.5 +/−8.3 ms, p=0.23). Repeated measures ANOVA indicated a significant difference in average T2 values between the six subregions (p<0.001). Post-hoc analysis showed that, in both the lateral and medial compartment, T2 relaxation time was significantly greater in the internal subregions versus both the central and external subregions (Fig. 1, all p<0.001 except Mi vs Me p=0.003). Conclusions: Study results indicate that T2 relaxation time of non-OA femoral cartilage is significantly longer in the internal subregions than in the two other subregions for both condyles, therefore highlighting the importance of subregional analyses. The higher T2 values in the internal subregions may be related to locally lower mechanical stresses, which could lead to a higher water content and a different organization of the collagen ultrastructure. Overall, this study presents a new three-dimensional method to analyze spatial variation of T2 relaxation time using widely accepted cartilage thickness-based regions/subregions of interest. In general, this subregional analysis of T2 relaxation time may enhance MRI measurement sensitivity to OA changes, as previously shown with cartilage thickness analyses. Ultimately, this methodology could be applied to utilize both cartilage morphological and biochemical composition data simultaneously to more thoroughly understand cartilage degradation and possibly improve early disease detection.