Development of a dynamic flow imaging phantom for dynamic contrast-enhanced CT
Development of a dynamic flow imaging phantom for dynamic contrast-enhanced CT
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DOI：
10.1118/1.3615058
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发表时间：
2011-08-01
期刊：
影响因子：
3.8
通讯作者：
Coolens, C.
中科院分区：
文献类型：
--
作者：
Driscoll, B.;Keller, H.;Coolens, C.
目的：随着大容量 CT 扫描仪的进步，能够以亚秒级图像频率和亚毫米精度对整个器官进行成像，利用血流和组织灌注建模的动态对比增强 CT (DCE-CT) 研究在临床中变得越来越普遍。然而，灌注 DCE-CT 的广泛实施有待对动态对比成像和灌注建模产生的定量参数进行基本验证。因此，这项工作的目标是设计和构建一种新型动态血流成像模型，能够产生典型的临床时间衰减曲线（TAC），目的是开发一个框架，用于在真实流动条件下量化和验证 DCE-CT 测量和动力学建模。方法：该模型基于简单的两室模型，并使用 3D 打印机打印。体模的初步分析涉及简单的流量测量，然后进行 DCE-CT 实验，以测试体模的范围和再现性。然后利用模型生成真实的输入 TAC。开发了模型预测模型，用于根据给定的五个实验（控制）参数集计算输入和输出 TAC：泵流量、注射泵流量、注射造影剂浓度和两个控制阀位置。然后反向应用预测模型以确定生成一组所需输入和输出 TAC 所需的控制参数。使用模型开发并执行了一个协议，以研究真实流动条件下的图像噪声、部分体积效应和 CT 数准确性结果：该模型及其周围的流动系统能够创建各种生理相关的 TAC，这些 TAC 可以在实验之间以最小的误差重现（对于所有研究的指标，sigma/mu < 5%）。动态流动模型能够使用基于阶跃函数或典型的临床动脉输入函数 (AIF) 输入来生成输入和输出 TAC。输入函数的拟合优度 (R(2)) 介于 0.95 和 0.98 之间，测得的 TAC 与所有比较指标的预测非常一致，而最大增强差异不超过 3.3%。预测的输出函数同样准确，产生 0.92 至 0.99 之间的 R(2) 值，最大增强幅度在 9.0% 以内。研究了 ROI 大小对动脉输入功能 (AIF) 的影响，以确定 ROI 大小的操作范围，该范围受小尺寸噪声和大尺寸部分体积效应的影响最小。可以确定东芝（ROI 半径范围为 1.5 至 3.2 毫米“低剂量”、1.4 至 3.0 毫米“高剂量”）和 GE 扫描仪（1.5 至 2.6 毫米“低剂量”、1.1 至 3.4 毫米“高剂量”）的测量灵敏度。模型的这种应用还提供了评估 AIF 误差对动力学模型参数预测的影响的能力。结论：动态流成像模型能够产生准确且可重复的结果，并且可以预测和量化。这产生了在真实流动条件下进行灌注 DCE-CT 验证的独特工具，该工具不仅可以用于比较不同的 CT 扫描仪和成像协议，而且还可以在考虑到 MRI 和 PET 兼容性的情况下提供跨多模态动态成像的基本事实。 (C) 2011 年美国医学物理学家协会。 [DOI：10.1118/1.3615058]
Purpose: Dynamic contrast enhanced CT (DCE-CT) studies with modeling of blood flow and tissue perfusion are becoming more prevalent in the clinic, with advances in wide volume CT scanners allowing the imaging of an entire organ with sub-second image frequency and submillimeter accuracy. Wide-spread implementation of perfusion DCE-CT, however, is pending fundamental validation of the quantitative parameters that result from dynamic contrast imaging and perfusion modeling. Therefore, the goal of this work was to design and construct a novel dynamic flow imaging phantom capable of producing typical clinical time-attenuation curves (TACs) with the purpose of developing a framework for the quantification and validation of DCE-CT measurements and kinetic modeling under realistic flow conditions.Methods: The phantom is based on a simple two-compartment model and was printed using a 3D printer. Initial analysis of the phantom involved simple flow measurements and progressed to DCE-CT experiments in order to test the phantoms range and reproducibility. The phantom was then utilized to generate realistic input TACs. A phantom prediction model was developed to compute the input and output TACs based on a given set of five experimental (control) parameters: pump flow rate, injection pump flow rate, injection contrast concentration, and both control valve positions. The prediction model is then inversely applied to determine the control parameters necessary to generate a set of desired input and output TACs. A protocol was developed and performed using the phantom to investigate image noise, partial volume effects and CT number accuracy under realistic flow conditionsResults: This phantom and its surrounding flow system are capable of creating a wide range of physiologically relevant TACs, which are reproducible with minimal error between experiments (sigma/mu < 5% for all metrics investigated). The dynamic flow phantom was capable of producing input and output TACs using either step function based or typical clinical arterial input function (AIF) inputs. The measured TACs were in excellent agreement with predictions across all comparison metrics with goodness of fit (R(2)) for the input function between 0.95 and 0.98, while the maximum enhancement differed by no more than 3.3%. The predicted output functions were similarly accurate producing R(2) values between 0.92 and 0.99 and maximum enhancement to within 9.0%. The effect of ROI size on the arterial input function (AIF) was investigated in order to determine an operating range of ROI sizes which were minimally affected by noise for small dimensions and partial volume effects for large dimensions. It was possible to establish the measurement sensitivity of both the Toshiba (ROI radius range from 1.5 to 3.2 mm "low dose", 1.4 to 3.0 mm "high dose") and GE scanner (1.5 to 2.6 mm "low dose", 1.1 to 3.4 mm "high dose"). This application of the phantom also provides the ability to evaluate the effect of the AIF error on kinetic model parameter predictions.Conclusions: The dynamic flow imaging phantom is capable of producing accurate and reproducible results which can be predicted and quantified. This results in a unique tool for perfusion DCE-CT validation under realistic flow conditions which can be applied not only to compare different CT scanners and imaging protocols but also to provide a ground truth across multimodality dynamic imaging given its MRI and PET compatibility. (C) 2011 American Association of Physicists in Medicine. [DOI: 10.1118/1.3615058]