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: 6432814
• 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/6432814
• 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) Work concluded on the development of a robust minimum variance adaptive (MVA) method for parameter estimation and statistical hypothesis testing. Rather than choosing a specific estimator or test statistic prior to the data analysis, the MVA method adapts to each data set by choosing from a large candidate group the single best (minimum variance) estimator or test statistic for the particular data. Unlike parametric methods, the MVA method requires no prior assumptions about the statistical probability distribution of the underlying population. (2) Examination of the effects of the diffusion limitation of water, and of kinetic heterogeneity of tissues necessarily included in field of view of PET measurements, on determinations of cerebral blood flow (CBF) with O-15 labeled water and PET continued. The kinetic model currently used for measurement of CBF does not take either effect into account. 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. Due to the high degree of nonlinearity of the model in its parameters, however, estimation of the parameters with standard nonlinear least squares algorithms lacks robustness and is computationally intensive. We have developed an alternative algorithm that is both efficient and robust. Preliminary results from simulation studies indicate that the algorithm provides accurate estimates of weighted average blood flow and gray matter blood flow in a mixed tissue. Publications: Turkheimer F, Pettigrew K, Sokoloff L, Schmidt K (1999) "A minimum variance adaptive technique for parameter estimation and hypothesis testing," Commun Statist - Simula 28(4): 931-956.Turkheimer F, Pettigrew K, Sokoloff L, Smith CB, Schmidt K (2000) "Selection of an adaptive test statistic for use with multiple comparison analyses of neuroimaging data," Neuroimage 12(2): 219-229.Schmidt KC (2000) "Identification of linear compartmental systems that can be analyzed by spectral analysis of the sum of all compartments", in Physiological Imaging of the Brain with PET. A Gjedde, SB Hansen, GM Knudsen, OB Paulson, Eds. Academic Press (In press).

只有对成像方法所依据的生理和生化过程的速率进行量化，才能明确地解释由疾病或正常大脑中各种通路的激活所产生的大脑功能活动图像的变化。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），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进行定量。 选择最佳动力学模型至关重要，因为使用不合适的模型可能会导致量化中出现重大错误，并可能导致结果的误解。一旦选择了模型，就需要高效、稳健且需要对测量误差进行最小假设的数值程序来准确估计参数。此外，还需要强大的统计测试，以便可以检查数据以发现实验组之间的显着差异。该项目的目标是开发更好的技术来解决这些相互关联的数学和统计问题；今年在以下方面取得了进展：（1）完成了用于参数估计和统计假设检验的鲁棒最小方差自适应（MVA）方法的开发工作。 MVA 方法不是在数据分析之前选择特定的估计量或检验统计量，而是通过从大型候选组中选择特定数据的单个最佳（最小方差）估计量或检验统计量来适应每个数据集。与参数方法不同，MVA 方法不需要对基础总体的统计概率分布进行事先假设。 (2) 继续检查水的扩散限制以及 PET 测量视野中必然包含的组织动力学异质性对用 O-15 标记的水和 PET 测定脑血流量 (CBF) 的影响。目前用于测量 CBF 的动力学模型没有考虑这两种效应。我们之前已经用当前使用的动力学模型量化了动力学异质性导致 CBF 低估的程度，并开发了一种考虑异质性并避免 CBF 低估的替代动力学模型。 然而，由于模型参数的高度非线性，使用标准非线性最小二乘算法估计参数缺乏鲁棒性并且计算量大。我们开发了一种既高效又稳健的替代算法。 模拟研究的初步结果表明，该算法可以准确估计混合组织中的加权平均血流量和灰质血流量。出版物：Turkheimer F、Pettigrew K、Sokoloff L、Schmidt K (1999)“参数估计和假设检验的最小方差自适应技术”，Commun Statist - Simula 28(4)：931-956。Turkheimer F、Pettigrew K、Sokoloff L、Smith CB、Schmidt K (2000)“自适应检验的选择 用于神经影像数据多重比较分析的统计量”，Neuroimage 12(2): 219-229。Schmidt KC (2000)“可通过所有区室总和的光谱分析来分析的线性区室系统的识别”，《PET 脑生理成像》。 A Gjedde、SB Hansen、GM Knudsen、OB Paulson，编辑。 学术出版社（正在出版）。