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    • Free‐breathing Liver Fat and Quantification Using Motion‐corrected Averaging Based on a Nonlocal Means Algorithm

    Huiwen Luo,Ante Zhu,Curtis N. Wiens,Jitka Starekova,Ann Shimakawa,Scott B. Reeder,Kevin M. Johnson,Diego Hernando

    Magnetic Resonance In Medicine（2020）SCI 2区

    Univ Wisconsin | GE Healthcare

    Cited 4|Views42
    Abstract
    PurposeTo propose a motion‐robust chemical shift‐encoded (CSE) method with high signal‐to‐noise (SNR) for accurate quantification of liver proton density fat fraction (PDFF) and .MethodsA free‐breathing multi‐repetition 2D CSE acquisition with motion‐corrected averaging using nonlocal means (NLM) was proposed. PDFF and quantified with 2D CSE‐NLM were compared to two alternative 2D techniques: direct averaging and single acquisition (2D 1ave) in a digital phantom. Further, 2D NLM was compared in patients to 3D techniques (standard breath‐hold, free‐breathing and navigated), and the alternative 2D techniques. A reader study and quantitative analysis (Bland‐Altman, correlation analysis, paired Student’s t‐test) were performed to evaluate the image quality and assess PDFF and measurements in regions of interest.ResultsIn simulations, 2D NLM resulted in lower standard deviations (STDs) of PDFF (2.7%) and (8.2 ) compared to direct averaging (PDFF: 3.1%, : 13.6 ) and 2D 1ave (PDFF: 8.7%, : 33.2 ). In patients, 2D NLM resulted in fewer motion artifacts than 3D free‐breathing and 3D navigated, less signal loss than 2D direct averaging, and higher SNR than 2D 1ave. Quantitatively, the STDs of PDFF and of 2D NLM were comparable to those of 2D direct averaging (p>0.05). 2D NLM reduced bias, particularly in (−5.73 to −0.36 ) that arises in direct averaging (−3.96 to 11.22 ) in the presence of motion.Conclusions2D CSE‐NLM enables accurate mapping of PDFF and in the liver during free‐breathing.
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    liver,motion-corrected averaging,nonlocal means,proton density fat fraction,quantification,R-2(*)
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    【要点】：本文提出了一种基于非局部均值算法的运动校正平均的自由呼吸肝脏脂肪和R2量化方法，旨在提高信号噪声比，实现肝脏质子密度脂肪分数（PDFF）和R2的精确量化。
    

    【方法】：采用了一种自由呼吸的多重复制2D化学位移编码（CSE）采集方法，结合非局部均值算法的运动校正平均。
    

    【实验】：在数字phantom中，将2D CSE-NLM法得到的PDFF和R2与直接平均和单次采集（2D 1ave）两种2D技术进行比较；在患者身上，将2D NLM与3D技术（标准呼吸暂停、自由呼吸和导航）以及另一种2D技术进行比较。进行了读者研究及定量分析（Bland-Altman分析、相关性分析、配对t检验），以评估感兴趣区域内的图像质量，并测量PDFF和R2。
    

    结果显示，在模拟实验中，与直接平均（PDFF: 3.1%, R2*: 13.6 s-1）和2D 1ave（PDFF: 8.7%, R2*: 33.2 s-1）相比，2D NLM得到的PDFF（2.7%）和R2（8.2 s-1）的标准差更低。在患者身上，2D NLM比3D自由呼吸和3D导航产生的运动伪影更少，比2D直接平均导致的信号损失更小，比2D 1ave具有更高的信噪比。定量结果显示，2D NLM得到的PDFF和R2的标准差与2D直接平均相当（p>0.05），在运动存在的情况下，2D NLM降低了偏差，特别是R2的偏差（-5.73到-0.36 s-1），而直接平均的偏差为（-3.96到11.22 s-1）。结论是，2D CSE-NLM能够在自由呼吸期间准确映射肝脏的PDFF和R2。