Accelerating the 3D T1ρ mapping of cartilage using a signal-compensated robust tensor principal component analysis model.

Abstract

Magnetic resonance (MR) quantitative T1ρ 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 T1ρ measurement. Compared with 2D T1ρ mapping, 3D T1ρ 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 T1ρ-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 T1ρ mapping without compromising the T1ρ quantification accuracy and precision. To accelerate the acquisition of 3D T1ρ mapping without compromising the T1ρ quantification accuracy and precision, a signal-compensated robust tensor principal component analysis method was proposed in this paper. The 3D T1ρ-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 T1ρ 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 T1ρ-weighted images and the corresponding T1ρ maps. T1ρ parameter maps were successfully estimated from T1ρ-weighted images reconstructed using the proposed method for all accelerations. The accelerated T1ρ measurements and reference values were in good agreement for R=5.2 (T1ρ: 40.4±1.4 ms), R=7.7 (T1ρ: 40.4±2.1 ms), and R=9.7 (T1ρ: 40.9±2.2 ms) in the Bland-Altman analyses. The T1ρ parameter maps reconstructed from the prospectively undersampled data also showed promising image quality using the proposed method. The proposed method achieves the 3D T1ρ mapping of in vivo knee cartilage in eight minutes using a signal-compensated robust tensor principal component analysis method in image reconstruction.