Shih Shu-Fu, Tasdelen Bilal, Yagiz Ecrin, Zhang Zhaohuan, Zhong Xiaodong, Cui Sophia X, Nayak Krishna S, Wu Holden H
Department of Radiological Sciences, University of California Los Angeles, Los Angeles, California, USA.
Department of Bioengineering, University of California Los Angeles, Los Angeles, California, USA.
Magn Reson Med. 2025 Mar;93(3):1348-1364. doi: 10.1002/mrm.30324. Epub 2024 Oct 9.
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.
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).
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).
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.
通过系统验证采集参数选择并研究局部低秩去噪方法的性能，提高0.55 T场强下肝脏质子密度脂肪分数（PDFF）及其定量分析。
进行蒙特卡洛模拟以设计0.55 T场强下PDFF及其成像的方案。使用该提议方案，我们研究了稳健局部低秩（RLLR）和随机矩阵理论（RMT）去噪的性能。在参考体模中，我们评估了多次扫描重复时的定量准确性（一致性相关系数[ ]与参考值相比）和精密度（使用标准差）。我们对11名受试者进行了肝脏活体扫描，并使用感兴趣区域比较PDFF及其测量值的均值和标准差。进行了Kruskal-Wallis和Wilcoxon符号秩检验（p < 0.05认为具有显著性）。
在体模中，与传统重建（即无去噪）相比，RLLR和RMT去噪在50次扫描重复中提高了PDFF及其定量的准确性（ > 0.992），并提高了精密度，标准差降低了67%以上。对于肝脏活体扫描，三种方法（传统重建；RLLR和RMT去噪）之间的平均PDFF和平均 无显著差异。无去噪时，PDFF和 的标准差分别为8.80%和14.17 s。RLLR去噪显著将这些值降低至1.79%和5.31 s（p < 0.001）；RMT去噪显著将这些值降低至2.00%和4.81 s（p < 0.001）。
我们验证了一种用于改善0.55 T场强下PDFF及其定量分析的采集方案。RLLR和RMT去噪均提高了PDFF及其测量的准确性和精密度。
