Department of Chemistry, Washington University, Saint Louis, MO, USA.
Department of Medical Physics, Aarhus University, Aarhus, Denmark.
Mol Imaging Biol. 2018 Feb;20(1):150-159. doi: 10.1007/s11307-017-1090-x.
This study aims to develop a constrained local arterial input function (cL-AIF) to improve quantitative analysis of dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) data by accounting for the contrast-agent bolus amplitude error in the voxel-specific AIF.
Bayesian probability theory-based parameter estimation and model selection were used to compare tracer kinetic modeling employing either the measured remote-AIF (R-AIF, i.e., the traditional approach) or an inferred cL-AIF against both in silico DCE-MRI data and clinical, cervical cancer DCE-MRI data.
When the data model included the cL-AIF, tracer kinetic parameters were correctly estimated from in silico data under contrast-to-noise conditions typical of clinical DCE-MRI experiments. Considering the clinical cervical cancer data, Bayesian model selection was performed for all tumor voxels of the 16 patients (35,602 voxels in total). Among those voxels, a tracer kinetic model that employed the voxel-specific cL-AIF was preferred (i.e., had a higher posterior probability) in 80 % of the voxels compared to the direct use of a single R-AIF. Maps of spatial variation in voxel-specific AIF bolus amplitude and arrival time for heterogeneous tissues, such as cervical cancer, are accessible with the cL-AIF approach.
The cL-AIF method, which estimates unique local-AIF amplitude and arrival time for each voxel within the tissue of interest, provides better modeling of DCE-MRI data than the use of a single, measured R-AIF. The Bayesian-based data analysis described herein affords estimates of uncertainties for each model parameter, via posterior probability density functions, and voxel-wise comparison across methods/models, via model selection in data modeling.
本研究旨在开发一种约束局部动脉输入函数（cL-AIF），通过考虑体素特异性 AIF 中的对比剂浓度波幅误差，改善动态对比增强（DCE）-磁共振成像（MRI）数据的定量分析。
采用贝叶斯概率理论的参数估计和模型选择，比较了使用测量的远程动脉输入函数（R-AIF，即传统方法）或推断的 cL-AIF 进行示踪剂动力学建模的方法，与仿真 DCE-MRI 数据和临床宫颈癌 DCE-MRI 数据。
当数据模型包含 cL-AIF 时，在典型临床 DCE-MRI 实验的对比噪声条件下，可从仿真数据中正确估计示踪剂动力学参数。考虑到临床宫颈癌数据，对 16 名患者的所有肿瘤体素（共 35,602 个体素）进行了贝叶斯模型选择。在这些体素中，与直接使用单个 R-AIF 相比，在 80%的体素中，使用特定体素 cL-AIF 的示踪剂动力学模型更受青睐（即后验概率更高）。对于诸如宫颈癌等异质组织，可以使用 cL-AIF 方法获得体素特异性 AIF 浓度波幅和到达时间的空间变化图。
cL-AIF 方法为感兴趣组织内的每个体素估计独特的局部 AIF 浓度波幅和到达时间，比使用单个测量的 R-AIF 更能对 DCE-MRI 数据进行建模。本文所述的基于贝叶斯的数据分析方法通过后验概率密度函数为每个模型参数提供了估计不确定性，并通过模型选择在数据建模中对方法/模型进行体素间比较。
