Roccia Elisa, Mikhno Arthur, Zanderigo Francesca, Angelini Elsa D, Ogden R Todd, Mann J John, Laine Andrew F
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:2243-6. doi: 10.1109/EMBC.2015.7318838.
Quantification of regional cerebral metabolic rate of glucose (rCMRglu) via positron emission tomography (PET) imaging requires measuring the arterial input function (AIF) via invasive arterial blood sampling. In this study we describe a non-invasive approach, the non-invasive simultaneous estimation (nSIME), for the estimation of rCMRglu that considers a pharmacokinetic input function model and constraints derived from machine learning applied to a fusion of individual medical health records and dynamic [(18)F]-FDG-PET brain images data. The results obtained with our data indicate potential for future clinical application of nSIME, with correlation measures of 0.87 for rCMRglu compared to quantification with full arterial blood sampling.
通过正电子发射断层扫描（PET）成像对局部脑葡萄糖代谢率（rCMRglu）进行定量分析需要通过侵入性动脉血采样来测量动脉输入函数（AIF）。在本研究中，我们描述了一种非侵入性方法，即非侵入性同步估计（nSIME），用于估计rCMRglu，该方法考虑了药代动力学输入函数模型以及应用于个体医疗健康记录与动态[（18）F]-FDG-PET脑图像数据融合的机器学习得出的约束条件。我们的数据所获得的结果表明nSIME在未来临床应用中的潜力，与全动脉血采样定量相比，rCMRglu的相关系数为0.87。
