MATHEMATICAL AND STATISTICAL ANALYSIS TECHNIQUES FOR IN VIVO IMAGING STUDIES

体内成像研究的数学和统计分析技术

• 批准号: 6290544
• 负责人: LOUIS SOKOLOFF
• 金额: --
• 依托单位: NATIONAL INSTITUTE OF MENTAL HEALTH
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/6290544
• 关键词: brain metabolism; chemical kinetics; deoxyglucose; glucose metabolism; mathematical model; model design /development; positron emission tomography; radiotracer; statistics /biometry; time resolved data

Changes in images of brain functional activity that are produced by disease or by activation of various pathways in the normal brain can only be unambiguously interpreted if the rates of the physiological and biochemical processes that underlie the imaging method are quantified. In imaging modalities that use radioactive tracers, e.g. positron emission tomography (PET), quantification is carried out by means of a mathematical model that describes the rates of the biochemical reactions in the metabolic pathway of the tracer and traced molecules. Selection of the best kinetic model is critical as the use of an inappropriate model can lead to substantial errors in quantification and possible misinterpretation of results. Once a model is selected, numerical procedures that are efficient, robust, and require minimal assumptions about the errors in the measurements are required to estimate accurately the parameters. Additionally, powerful statistical tests are needed so that the data can be examined for significant differences among experimental groups. The objective of this project is to develop better techniques for addressing these interrelated mathematical and statistical issues; advances in the current year were made in the following areas:(1) We have extended a previous study to identify compartmental systems that meet the conditions necessary for application of spectral methods. Spectral methods are used to determine the best kinetic model for a system under study and estimate the system parameters. They are particularly important for use in brain imaging studies with PET because the spatial resolution of the PET scanner is insufficient to obtain measurements in kinetically and structurally homogeneous tissue regions. The total number of components necessary to describe the data is, therefore, usually unknown. Current spectral methods do not apply to all linear compartmental systems, and it is essential to establish that all possible candidate compartmental systems that may be used for describing the data under analysis meet the spectral analytic conditions prior to application of the spectral technique.(2) We have initiated a study to examine the effects of the diffusion limitation of water on determinations of cerebral blood flow (CBF) with O-15 labeled water and PET. The kinetic model currently used for measurement of CBF does not take into account either the diffusion limitation of water or the kinetic heterogeneity of the tissues necessarily included in the field of view of each measurement due to the limited spatial resolution of the PET scanner. We have previously quantified the extent to which kinetic heterogeneity leads to an underestimation of CBF with the kinetic model currently in use, and developed an alternative kinetic model that takes into account the heterogeneity and avoids the CBF underestimation. We have now begun to quantify the extent of the underestimations of CBF due to the diffusion limitation of water and to explore the possibility of including corrections in the kinetic model.(3) Further progress was made in the development of a robust minimum variance adaptive (MVA) method for parameter estimation and statistical hypothesis testing. The MVA method selects an estimator for the parameter of interest or a test statistic that possesses the minimum possible uncertainty, i.e. the minimum possible variance. It is adaptive in the sense that the specific estimator or test statistic is not chosen prior to the data analysis. Instead, a large group of possible estimators or test statistics is considered, and the procedure adapts by choosing the single estimator or test statistic that is best for the data set under analysis. Unlike parametric methods, the MVA method requires no prior assumptions about the statistical probability distribution of the underlying population. Publications:Turkheimer F, Sokoloff L, Bertoldo A, Lucignani G, Reivich M, Jaggi JL, Schmidt K (1998) Estimation of component and parameter distributions in spectral analysis. J Cereb Blood Flow Metab 18:1211-1222.Schmidt K (1999) Which linear compartmental systems can be analyzed by spectral analysis of PET output data summed over all compartments? J Cereb Blood Flow Metab 19:560-569.Turkheimer F, Pettigrew K, Sokoloff L, Schmidt K. A minimum variance adaptive technique for parameter estimation and hypothesis testing. Communications in Statistics - Simulation and Computation, In press (accepted 5 May 1999).

由于疾病或正常大脑中各种通路的激活而产生的脑功能活动图像的变化，只有在量化成像方法基础上的生理和生化过程的速率时，才能明确地解释。在使用放射性示踪剂的成像方式中，例如正电子发射断层扫描（PET），量化是通过描述示踪剂和被追踪分子代谢途径中生化反应速率的数学模型来进行的。选择最佳的动力学模型是至关重要的，因为使用不适当的模型可能导致量化的重大错误和可能的结果误解。一旦选择了模型，就需要高效、稳健且对测量误差的假设最小的数值程序来准确地估计参数。此外，还需要强有力的统计检验，以便检验实验组之间的显著差异。这个项目的目标是发展更好的技术来处理这些相互关联的数学和统计问题；本年度在以下方面取得了进展：(1)我们扩展了先前的研究，以确定满足光谱方法应用所需条件的区隔系统。光谱方法用于确定所研究系统的最佳动力学模型和估计系统参数。它们对于使用PET进行脑成像研究尤其重要，因为PET扫描仪的空间分辨率不足以获得动力学和结构均匀的组织区域的测量。因此，描述数据所需的组件总数通常是未知的。目前的光谱方法并不适用于所有的线性隔室系统，在应用光谱技术之前，必须确定所有可能用于描述分析数据的候选隔室系统都符合光谱分析条件。(2)我们已经开始研究水的扩散限制对O-15标记水和PET测定脑血流量（CBF）的影响。目前用于测量脑血流的动力学模型既没有考虑水的扩散限制，也没有考虑由于PET扫描仪有限的空间分辨率而必然包括在每次测量视场中的组织的动力学非均质性。我们之前用目前使用的动力学模型量化了动力学非均质性导致CBF低估的程度，并开发了一种考虑非均质性并避免CBF低估的替代动力学模型。我们现在已经开始量化由于水的扩散限制而低估CBF的程度，并探索在动力学模型中包括修正的可能性。(3)鲁棒最小方差自适应（MVA）方法在参数估计和统计假设检验方面取得了进一步进展。MVA方法为感兴趣的参数或具有最小可能不确定性（即最小可能方差）的检验统计量选择一个估计量。它是自适应的，因为在数据分析之前不选择特定的估计量或检验统计量。取而代之的是，考虑一大批可能的估计量或测试统计量，并通过选择最适合分析数据集的单个估计量或测试统计量来进行调整。与参数方法不同，MVA方法不需要对潜在总体的统计概率分布进行先验假设。出版物：Turkheimer F， Sokoloff L, Bertoldo A, Lucignani G, Reivich M, Jaggi JL, Schmidt K（1998）光谱分析中成分和参数分布的估计。[J]脑血流杂志。Schmidt K（1999）哪些线性隔室系统可以通过对所有隔室的PET输出数据进行光谱分析来分析？[J] .脑血流杂志，19(6):556 -569。李建军，李建军，李建军，等。一种基于最小方差自适应的参数估计方法。统计通讯-模拟与计算，出版（1999年5月5日接受）。