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Application of separable parameter space techniques to multi-tracer PET compartment modeling

机译:可分离参数空间技术在多示踪PET隔室建模中的应用

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摘要

Multi-tracer positron emission tomography (PET) can image two or more tracers in a single scan, characterizing multiple aspects of biological functions to provide new insights into many diseases. The technique uses dynamic imaging, resulting in time-activity curves that contain contributions from each tracer present. The process of separating and recovering separate images and/or imaging measures for each tracer requires the application of kinetic constraints, which are most commonly applied by fitting parallel compartment models for all tracers. Such multi-tracer compartment modeling presents challenging nonlinear fits in multiple dimensions. This work extends separable parameter space kinetic modeling techniques, previously developed for fitting single-tracer compartment models, to fitting multi-tracer compartment models. The multi-tracer compartment model solution equations were reformulated to maximally separate the linear and nonlinear aspects of the fitting problem, and separable least-squares techniques were applied to effectively reduce the dimensionality of the nonlinear fit. The benefits of the approach are then explored through a number of illustrative examples, including characterization of separable parameter space multi-tracer objective functions and demonstration of exhaustive search fits which guarantee the true global minimum to within arbitrary search precision. Iterative gradient-descent algorithms using Levenberg–Marquardt were also tested, demonstrating improved fitting speed and robustness as compared to corresponding fits using conventional model formulations. The proposed technique overcomes many of the challenges in fitting simultaneous multi-tracer PET compartment models.
机译:多示踪剂正电子发射断层扫描(PET)可以在一次扫描中对两个或更多个示踪剂成像,从而表征生物学功能的多个方面,从而提供对许多疾病的新见解。该技术使用动态成像,得到的时间-活动曲线包含来自每个示踪剂的贡献。为每个示踪剂分离和恢复单独的图像和/或成像措施的过程需要应用动力学约束,这通常是通过为所有示踪剂拟合平行隔室模型来应用的。这种多示踪剂隔室建模提出了具有挑战性的多维非线性拟合。这项工作将以前为拟合单示踪剂隔室模型而开发的可分离参数空间动力学建模技术扩展为拟合多示踪剂隔室模型。重新构造了多示踪车厢模型求解方程,以最大程度地拟合拟合问题的线性和非线性方面,并应用了可分离的最小二乘技术来有效降低非线性拟合的维数。然后,通过许多说明性示例来探讨该方法的好处,包括可分离参数空间多跟踪器目标函数的表征以及详尽的搜索拟合的演示,这些拟合将保证真正的全局最小值在任意搜索精度内。还测试了使用Levenberg-Marquardt的迭代梯度下降算法,与使用传统模型公式进行的拟合相比,证明了改进的拟合速度和鲁棒性。拟议的技术克服了同时安装多示踪PET隔室模型的许多挑战。

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