The influences of different spatial resolutions on the characteristics of T2 relaxation times in articular cartilage: A coarse-graining study of the microscopic magnetic resonance imaging data

Compressed sensing in quantitative determination of GAG concentration in cartilage by microscopic MRI

PURPOSE

To evaluate the potentials of compressed sensing (CS) in MRI quantification of glycosaminoglycan (GAG) concentration in articular cartilage at microscopic resolution.

METHODS

T1 -weighted 2D experiments of cartilage were fully sampled in k-space with five inversion times at 17.6 μm resolution. These fully sampled k-space data were re-processed, by undersampling at various 1D and 2D CS undersampling factors (UFs). The undersampled data were reconstructed individually into 2D images using nonlinear reconstruction, which were used to calculate 2D maps of T1 and GAG concentration. The values of T1 and GAG in cartilage were evaluated at different UFs (up to 16, which used 6.25% of the data). K-space sampling pattern and zonal variations were also investigated.

RESULTS

Using 2D variable density sampling pattern, the T1 images at UFs up to eight preserved major visual information and produced negligible artifacts. The GAG concentration remained accurate for different sub-tissue zones at various UFs. The variation of the mean GAG concentration through the whole tissue depth was 1.20%, compared to the fully sampled results. The maximum variation was 2.24% in the deep zone of cartilage. Using 1D variable density sampling pattern, the quantitative T1 mapping and GAG concentration at UFs up to 4 showed negligible variations.

CONCLUSION

This study demonstrates that CS could be beneficial in microscopic MRI (µMRI) studies of cartilage by acquiring less data, without losing significant accuracy in the quantification of GAG concentration. Magn Reson Med 79:3163-3171, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Image interpolation improves the zonal analysis of cartilage T2 relaxation in MRI

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.