Department of Mathematics and Scientific Computing, University of Graz, Graz, Austria.
Radiological Sciences Laboratory, Stanford University, Stanford, CA, United States of America.
Phys Med Biol. 2024 Jul 30;69(16):165008. doi: 10.1088/1361-6560/ad539e.
In quantitative dynamic positron emission tomography (PET), time series of images, reflecting the tissue response to the arterial tracer supply, are reconstructed. This response is described by kinetic parameters, which are commonly determined on basis of the tracer concentration in tissue and the arterial input function. In clinical routine the latter is estimated by arterial blood sampling and analysis, which is a challenging process and thus, attempted to be derived directly from reconstructed PET images. However, a mathematical analysis about the necessity of measurements of the common arterial whole blood activity concentration, and the concentration of free non-metabolized tracer in the arterial plasma, for a successful kinetic parameter identification does not exist. Here we aim to address this problem mathematically.We consider the identification problem in simultaneous pharmacokinetic modeling of multiple regions of interests of dynamic PET data using the irreversible two-tissue compartment model analytically. In addition to this consideration, the situation of noisy measurements is addressed using Tikhonov regularization. Furthermore, numerical simulations with a regularization approach are carried out to illustrate the analytical results in a synthetic application example.We provide mathematical proofs showing that, under reasonable assumptions, all metabolic tissue parameters can be uniquely identified without requiring additional blood samples to measure the arterial input function. A connection to noisy measurement data is made via a consistency result, showing that exact reconstruction of the ground-truth tissue parameters is stably maintained in the vanishing noise limit. Furthermore, our numerical experiments suggest that an approximate reconstruction of kinetic parameters according to our analytic results is also possible in practice for moderate noise levels.The analytical result, which holds in the idealized, noiseless scenario, suggests that for irreversible tracers, fully quantitative dynamic PET imaging is in principle possible without costly arterial blood sampling and metabolite analysis.
在定量动态正电子发射断层扫描(PET)中,重建反映组织对动脉示踪剂供应反应的时间序列图像。该反应由动力学参数描述,这些参数通常基于组织中的示踪剂浓度和动脉输入函数来确定。在临床常规中,后者通过动脉采血和分析来估计,这是一个具有挑战性的过程,因此,尝试直接从重建的 PET 图像中得出。然而,关于确定动力学参数是否必须测量常见的动脉全血活性浓度和动脉血浆中未代谢的游离示踪剂浓度,目前还没有数学分析。在这里,我们旨在从数学上解决这个问题。
我们考虑使用不可逆两室模型在动态 PET 数据的多个感兴趣区域的同时药物代谢动力学建模中,从数学上分析识别问题。除了这种考虑之外,还使用 Tikhonov 正则化来解决噪声测量的情况。此外,通过正则化方法进行数值模拟,以在合成应用示例中说明分析结果。
我们提供数学证明,表明在合理的假设下,无需额外的血液样本测量动脉输入函数,就可以唯一地识别所有代谢组织参数。通过一致性结果将其与噪声测量数据联系起来,表明在噪声消失的极限下,对真实组织参数的精确重建可以稳定保持。此外,我们的数值实验表明,根据我们的分析结果进行的动力学参数的近似重建在实践中对于中等噪声水平也是可能的。
在理想化的无噪声情况下得出的分析结果表明,对于不可逆示踪剂,在原则上无需进行昂贵的动脉采血和代谢物分析,就可以进行完全定量的动态 PET 成像。