Accelerating the 3D T_1ρ mapping of cartilage using a signal-compensated robust tensor principal component analysis...

Magnetic resonance (MR) quantitative T_1ρ imaging has been increasingly used to detect the early stages of osteoarthritis. The small volume and curved surface of articular cartilage necessitate imaging with high in-plane resolution and thin slices for accurate T_1ρ measurement. Compared with 2D T_1ρ mapping, 3D T_1ρ mapping is free from artifacts caused by slice cross-talk and has a thinner slice thickness and full volume coverage. However, this technique needs to acquire multiple T_1ρ-weighted images with different spin-lock times, which results in a very long scan duration. It is highly expected that the scan time can be reduced in 3D T_1ρ mapping without compromising the T_1ρ quantification accuracy and precision.

To accelerate the acquisition of 3D T_1ρ mapping without compromising the T_1ρ quantification accuracy and precision, a signal-compensated robust tensor principal component analysis method was proposed in this paper. The 3D T_1ρ-weighted images compensated at different spin-lock times were decomposed as a low-rank high-order tensor plus a sparse component. Poisson-disk random undersampling patterns were applied to k-space data in the phase- and partition-encoding directions in both retrospective and prospective experiments. Five volunteers were involved in this study. The fully sampled k-space data acquired from 3 volunteers were retrospectively undersampled at R=5.2, 7.7, and 9.7, respectively. Reference values were obtained from the fully sampled data. Prospectively undersampled data for R=5 and R=7 were acquired from 2 volunteers. Bland-Altman analyses were used to assess the agreement between the accelerated and reference T_1ρ measurements. The reconstruction performance was evaluated using the normalized root mean square error and the median of the normalized absolute deviation (MNAD) of the reconstructed T_1ρ-weighted images and the corresponding T_1ρ maps.

T_1ρ parameter maps were successfully estimated from T_1ρ-weighted images reconstructed using the proposed method for all accelerations. The accelerated T_1ρ measurements and reference values were in good agreement for R=5.2 (T_1ρ: 40.4±1.4 ms), R=7.7 (T_1ρ: 40.4±2.1 ms), and R=9.7 (T_1ρ: 40.9±2.2 ms) in the Bland-Altman analyses. The T_1ρ parameter maps reconstructed from the prospectively undersampled data also showed promising image quality using the proposed method.

The proposed method achieves the 3D T_1ρ mapping of in vivo knee cartilage in eight minutes using a signal-compensated robust tensor principal component analysis method in image reconstruction.