Improved liver fat and quantification at 0.55 T using locally low-rank denoising.

Purpose: To improve liver proton density fat fraction (PDFF) and quantification at 0.55 T by systematically validating the acquisition parameter choices and investigating the performance of locally low-rank denoising methods.

Methods: A Monte Carlo simulation was conducted to design a protocol for PDFF and mapping at 0.55 T. Using this proposed protocol, we investigated the performance of robust locally low-rank (RLLR) and random matrix theory (RMT) denoising. In a reference phantom, we assessed quantification accuracy (concordance correlation coefficient [ ] vs. reference values) and precision (using SD) across scan repetitions. We performed in vivo liver scans (11 subjects) and used regions of interest to compare means and SDs of PDFF and measurements. Kruskal-Wallis and Wilcoxon signed-rank tests were performed (p < 0.05 considered significant).

Results: In the phantom, RLLR and RMT denoising improved accuracy in PDFF and with >0.992 and improved precision with >67% decrease in SD across 50 scan repetitions versus conventional reconstruction (i.e., no denoising). For in vivo liver scans, the mean PDFF and mean were not significantly different between the three methods (conventional reconstruction; RLLR and RMT denoising). Without denoising, the SDs of PDFF and were 8.80% and 14.17 s. RLLR denoising significantly reduced the values to 1.79% and 5.31 s (p < 0.001); RMT denoising significantly reduced the values to 2.00% and 4.81 s (p < 0.001).

Conclusion: We validated an acquisition protocol for improved PDFF and quantification at 0.55 T. Both RLLR and RMT denoising improved the accuracy and precision of PDFF and measurements.