Smith Alex K, Ray Kevin J, Larkin James R, Craig Martin, Smith Seth A, Chappell Michael A
Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom.
Cancer Research UK and Medical Research Council Oxford Institute for Radiation Oncology, Department of Oncology, University of Oxford, Oxford, United Kingdom.
Magn Reson Med. 2020 Sep;84(3):1359-1375. doi: 10.1002/mrm.28212. Epub 2020 Feb 18.
Chemical exchange saturation transfer (CEST) is an MRI technique sensitive to the presence of low-concentration solute protons exchanging with water. However, magnetization transfer (MT) effects also arise when large semisolid molecules interact with water, which biases CEST parameter estimates if quantitative models do not account for macromolecular effects. This study establishes under what conditions this bias is significant and demonstrates how using an appropriate model provides more accurate quantitative CEST measurements.
CEST and MT data were acquired in phantoms containing bovine serum albumin and agarose. Several quantitative CEST and MT models were used with the phantom data to demonstrate how underfitting can influence estimates of the CEST effect. CEST and MT data were acquired in healthy volunteers, and a two-pool model was fit in vivo and in vitro, whereas removing increasing amounts of CEST data to show biases in the CEST analysis also corrupts MT parameter estimates.
When all significant CEST/MT effects were included, the derived parameter estimates for each CEST/MT pool significantly correlated (P < .05) with bovine serum albumin/agarose concentration; minimal or negative correlations were found with underfitted data. Additionally, a bootstrap analysis demonstrated that significant biases occur in MT parameter estimates (P < .001) when unmodeled CEST data are included in the analysis.
These results indicate that current practices of simultaneously fitting both CEST and MT effects in model-based analyses can lead to significant bias in all parameter estimates unless a sufficiently detailed model is utilized. Therefore, care must be taken when quantifying CEST and MT effects in vivo by properly modeling data to minimize these biases.
化学交换饱和转移(CEST)是一种对与水交换的低浓度溶质质子敏感的磁共振成像(MRI)技术。然而,当大分子与水相互作用时也会产生磁化传递(MT)效应,如果定量模型未考虑大分子效应,这会使CEST参数估计产生偏差。本研究确定了这种偏差在何种条件下显著,并展示了使用适当的模型如何提供更准确的定量CEST测量。
在含有牛血清白蛋白和琼脂糖的体模中采集CEST和MT数据。使用几种定量CEST和MT模型对体模数据进行分析,以证明拟合不足如何影响CEST效应的估计。在健康志愿者中采集CEST和MT数据,并在体内和体外拟合双池模型,同时去除越来越多的CEST数据以显示CEST分析中的偏差也会破坏MT参数估计。
当纳入所有显著的CEST/MT效应时,每个CEST/MT池的导出参数估计与牛血清白蛋白/琼脂糖浓度显著相关(P <.05);而对于拟合不足的数据,相关性最小或为负相关。此外,自抽样分析表明,当分析中包含未建模的CEST数据时,MT参数估计会出现显著偏差(P <.001)。
这些结果表明,在基于模型的分析中同时拟合CEST和MT效应的当前做法,除非使用足够详细的模型,否则可能导致所有参数估计出现显著偏差。因此,在体内量化CEST和MT效应时,必须谨慎通过正确地对数据进行建模以最小化这些偏差。