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Improved Quantification of Nuclear Magnetic Resonance Relaxometry Data via Partial Least Squares Analysis

机译：通过局部最小二乘分析改善核磁共振宽松数据的量化

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摘要

Nuclear magnetic resonance relaxometry measurements are frequently used to quantify sample constituents. The standard approach for quantification involves converting the time-domain data to a distribution of characteristic times, either by fitting a fixed number of exponentials or performing an inverse Laplace transform, and then integrating the area under the peaks. We evaluated an alternative method to quantify relaxometry data. Partial least squares (PLS) analysis was applied directly to a variety of simulated time-domain relaxation data under diverse conditions to predict constituent content and results were compared to the standard analysis methods. For many situations, PLS analysis displayed superior performance for quantification than the standard analyses. The technique consistently produced better predictions at lower signal to noise. This robustness to noise makes it an appealing alternative for analysing data from applications that typically have low SNR, such as one-sided sensors, surface measurements, or well-logging. The method also enabled quantification of relaxation rates too close to be separated by an inverse Laplace transform. This capability may allow quantification to be performed using only one-dimensional relaxation data where multi-dimensional measurements were previously necessary to provide constituent separation. The method also enabled quantification of relaxometry data without the need for human interpretation or prior knowledge of what relaxation time is associated with a given constituent. These advantages make PLS analysis an appealing alternative for quantification of relaxometry data in many situations.
机译：核磁共振宽松测量通常用于量化样品成分。定量的标准方法涉及将时域数据转换为特征时间的分布，通过拟合固定数量的指数或执行逆拉普拉斯变换，然后整合峰下的区域。我们评估了一种替代方法来量化放宽数据。在各种条件下直接向各种模拟时域松弛数据直接应用部分最小二乘（PLS）分析，以预测成分含量，结果与标准分析方法进行比较。对于许多情况，PLS分析显示出比标准分析的量化卓越的性能。该技术在较低信号到噪声中始终如一地产生更好的预测。这种对噪声的稳健性使其成为一种吸引人的替代方案，用于分析通常具有低SNR的应用程序的数据，例如单侧传感器，表面测量或良好的记录。该方法还使得能够通过逆拉普拉斯变换来定量太靠近的弛豫速率太近。该能力可以仅使用仅使用一维放松数据来进行量化，其中先前需要多维测量以提供组成分离。该方法还能够使放置数据量化的量化，而无需人类解释或先验知识的放松时间与给定的组成部分相关联。这些优点使PLS分析了许多情况下的放宽数据的吸引力替代方案。

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