Purpose

Quantitative MRI provides valuable information on compositional changes in soft tissues. Among the techniques, numerous studies have suggested that T_1ρ imaging could be used to detect early cartilage degeneration in osteoarthritis.(1, 2) However, the long scan time of T_1ρ due to the necessity of acquiring multiple images remains as a challenge for clinical application. Accelerated T_1ρ imaging combining parallel imaging and compressed sensing have shown promising results.(3, 4) However, previous studies primarily applied retrospective downsampling and no studies yet have reported results from prospective downsampling. In this work, we applied a novel Kernel-based low-rank (KLR) compressed sensing reconstruction and demonstrated the feasibility of accelerated T_1ρ imaging with retrospective and prospective downsampling in the knee.

Methods

In the quantitative T_1ρ technique, the exponential decay model represents the underlying relationship between echo images. However, additional factors in actual data acquisition such as noise and motion effect further complicate this relationship. To solve this, we used the kernel-based low-rank (KLR) method to reconstruct the images. Specifically, temporal bases were learned from low-resolution images obtained from a fully sampled center k-space of each echo. These temporal bases can represent not only the exponential model but also the noise and motion effects. Then an optimization problem was formulated by adding temporal bases as constraint along with a data fidelity term, which can be solved by an iterative algorithm. The details of the KLR method can be found in (5).
Four volunteers were studied using a 3T MR scanner (Magnetom Prisma, Siemens Healthcare AG, Erlangen, Germany) with a 1Tx/15Rx knee coil (QED). The imaging protocol included 3D magnetization-prepared angle-modulated partitioned k-space spoiled gradient-echo snapshots (MAPSS) T_1ρ imaging (6, 7) and dual-echo steady-state (DESS) imaging sequences. For retrospective undersampling, two healthy volunteers were scanned with parallel imaging (GRAPPA) factor of 2, and reconstructed K-space data was used to simulate retrospective undersampling. Undersampling factors (denoted as RF) of 4, 6, and 8 were simulated. For prospective undersampling, the mask generated during retrospective undersampling simulation was implemented to the sequence program. Two healthy subjects were scanned with the implemented sequence with RF of 4, 6, and 8, respectively. For both retrospective and prospective cases, imaging with 2x GRAPPA was used as reference image. Detailed sequence parameters are listed at Table 1. For KLR compressed sensing reconstruction, the number of principal components was set to 6, and the soft threshold value of 10 was used.
For T_1ρ quantification, a two-parameter mono-exponential fitting was performed. Automatic segmentation was performed on the first echo of T_1ρ image to segment six compartments (medial/lateral femur [MFC/LFC], medial/lateral tibia [MT/LT], trochlea [TRO], and patellar [PAT]). For prospective undersampling, a deep learning automatic segmentation was used on the DESS image to segment four compartments (Femur [FC], medial/lateral tibia [MT/LT], and patellar [PAT]). The coefficient of variations (CVs) were calculated between the reference and different undersampled results.

Results

Table 2 summarized the quantitative results for the T_1ρ map generated from the compressed-sensing reconstruction of both retrospectively and prospectively undersampled acquisition. For retrospective undersampling, the average CVs with respect to the reference value increased with higher RFs, but the overall average was lower than 3% (Table 3). With prospective undersampling, the overall CV increased compared to the retrospectively undersampled reconstruction, but the average CVs stayed under 5%. Figure 1 shows two slices of T_1ρ maps from prospective undersampling.

Discussion

With our novel compressed sensing reconstruction, 8 echoes of T_1ρ weighted images could be collected within 4 minutes to generate a T_1ρ map within 5% CV of the reference T_1ρ map. The CVs were comparable to the scan-rescan repeatability of in vivo T_1ρ imaging reported in the literature.(2) 3D MAPSS T_1ρ imaging with RF cycling and variable flip angles has been validated to provide more accurate and reliable T_1ρ quantification by minimizing the filter effect in k-space and T₁ recovery contamination (6, 7). However, RF cycling requires a double scan time. The proposed acceleration method in the study will significantly reduce the acquisition time while keeping the quantification accuracy. In this study, the reconstruction based on the KLR algorithm showed promising results. No spatial regularization was applied within each echo images. We will investigate if combining spatial regularization and KLR will further improve quantification accuracy. There also remains a question regarding the discrepancy between retrospective simulation and actual prospectively undersampled outcome, which can be contributed by the different order of k-space data acquisition and different noise distribution between the simulation and actual acquisition. Different schemes of acquisition ordering will be implemented and evaluated in the future.

Conclusion

In this study, we have demonstrated the feasibility of highly accelerated T_1ρ imaging with high quantification accuracy by using prospective downsampling and novel compressed sensing reconstruction. To confirm the results warrants large scale studies. Such novel acceleration techniques will significantly facilitate the rapid clinical translation of quantitative MRI techniques.

Acknowledgements

No acknowledgement found.