Abstract
BACKGROUND: This project aimed to investigate the improvement in the detection of osteoarthritis (OA) in cartilage by the interpolation of T2 images, in the situation when the native MRI resolution is insufficient to resolve the depth-dependent T2 characteristics in articular cartilage (AC). METHODS: Eighteen intact canine knee joints that were healthy or had mild (contralateral) or severe OA were T2-imaged in a 7T/20 cm MRI system at 200 µm/pixel resolution (macro-MRI). Two image analysis methods were used to interpolate the images to 100 µm/pixel, i.e., by Fourier-transforming the time-domain FID (Free Induction Decay) signal using the Varian NMR software and by interpolating the 2D T2 image using the ImageJ software. RESULTS: The T2 profiles from 30 individual ROI of each healthy [6], mild [6] and OA [6] cartilage at 200 µm and the interpolated 100 µm resolutions were subdivided into two equal-thickness regions and three-equal thickness regions based on clinical MRI protocols. A new method divided the T2 profiles into three-unequal thickness zones according to the T2 profiles at 17.6 µm/pixel from the same cartilage imaged in a 7 Tesla/9 cm µMRI system. Both interpolation methods improved the depth-dependent T2 images/profiles in macro-MRI. The unequal zone division in T2 had better OA sensitivity than the equal zone division. The three-equal zone division of T2 profiles had better OA sensitivity than the two-equal zone division. The statistical significant difference between the healthy and mild OA cartilage is detected (P=0.0018) only by the unequal zone division method at 100 µm resolution. CONCLUSIONS: Data interpolation improves the T2 sensitivity in MRI of cartilage OA. Unequal division of tissue thickness enables better early stage of OA detection than the equal division.