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首页> 外文期刊>International journal of computational biology and drug design >Performance of chemical shift-based water-fat separation with self-calibrated fat spectrum is sensitive to echo times
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Performance of chemical shift-based water-fat separation with self-calibrated fat spectrum is sensitive to echo times

机译:具有自校准脂肪光谱的基于化学位移的水脂肪分离性能对回波时间敏感

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摘要

Chemical shift-based water-fat separation method utilises water-fat resonance frequency difference to decompose signals into water and fat partitions in magnetic resonance imaging (MRI) on a pixel-wise basis. It provides an effective way to measure fat fraction, or to suppress fat signal which might obscure underlying pathology. IDEAL (Iterative decomposition of water and fat with echo asymmetry and least-squares estimation) algorithm with multi-peak fat spectral modelling has been developed. Recent studies have discussed the performance of this algorithm assuming that the frequencies and relative amplitudes of fat peaks are constant among all subjects. However, the fat spectra vary in different tissues, thus a self-calibration method which estimates the fat spectrum directly from the data provides more accurate results. In this work, we analyse the performance of multi-peak IDEAL algorithm with self-calibrated fat spectrum by theoretical calculation, simulation, and experiments, and find optimal echo time increments which provide reliable water-fat separation.
机译:基于化学位移的水脂分离方法利用水脂共振频率差在磁共振成像(MRI)中以像素为基础将信号分解为水和脂肪分区。它提供了一种有效的方法来测量脂肪含量或抑制可能掩盖潜在病理的脂肪信号。开发了具有多峰脂肪光谱模型的IDEAL(具有回声不对称和最小二乘估计的水和脂肪迭代分解)算法。假设脂肪峰的频率和相对幅度在所有受试者中都是恒定的,最近的研究已经讨论了该算法的性能。但是,脂肪光谱在不同的组织中会发生变化,因此直接根据数据估算脂肪光谱的自校准方法可提供更准确的结果。在这项工作中,我们通过理论计算,仿真和实验分析了具有自校准脂肪光谱的多峰IDEAL算法的性能,并找到了可提供可靠的水脂肪分离的最佳回波时间增量。

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