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Compartmental analysis of dynamic nuclear medicine data: regularization procedure and application to physiology

机译:动态核医学数据的隔间分析:正规化程序和生理学应用

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Compartmental models based on tracer mass balance are extensively used in clinical and pre-clinical nuclear medicine in order to obtain quantitative information on tracer metabolism in the biological tissue. This paper is the second of a series of two that deal with the problem of tracer coefficient estimation via compartmental modelling in an inverse problem framework. While the previous work was devoted to the discussion of identifiability issues for 2, 3 and n-dimension compartmental systems Delbary et?al. [Compartmental analysis of dynamic nuclear medicine data: models and identifiability. Inverse Probl. 2016], here we discuss the problem of numerically determining the tracer coefficients by means of a general regularized Multivariate Gauss–Newton scheme. In this paper, applications concerning cerebral, hepatic and renal functions are considered, involving experimental measurements on FDG–PET data on different set of murine models.
机译:基于示踪质量平衡的隔间模型广泛用于临床和前临床核医学,以获得关于生物组织中的示踪代谢的定量信息。 本文是两种系列中的第二种,这是通过在逆问题框架中通过隔间建模来解决示踪系数估计的问题。 虽然以前的工作讨论了2,3和N维级分区系统德比亚·et?al的可识别性问题。 [动态核医学数据分区分析:模型与可识别性。 逆probl。 2016]在这里,我们讨论了借助于一般正则化多变量Gauss-Newton方案确定示踪系数的问题。 在本文中,考虑了有关脑,肝和肾功能的应用,涉及对不同鼠模型的FDG-PET数据进行实验测量。

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