Center of Functionally Integrative Neuroscience and MINDLab, Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark.
Inst. of Neuroradiology, Charité University Medicine Berlin, Berlin, Germany.
PLoS One. 2019 Jan 3;14(1):e0209891. doi: 10.1371/journal.pone.0209891. eCollection 2019.
In dynamic contrast enhanced (DCE) MRI, separation of signal contributions from perfusion and leakage requires robust estimation of parameters in a pharmacokinetic model. We present and quantify the performance of a method to compute tissue hemodynamic parameters from DCE data using established pharmacokinetic models.
We propose a Bayesian scheme to obtain perfusion metrics from DCE MRI data. Initial performance is assessed through digital phantoms of the extended Tofts model (ETM) and the two-compartment exchange model (2CXM), comparing the Bayesian scheme to the standard Levenberg-Marquardt (LM) algorithm. Digital phantoms are also invoked to identify limitations in the pharmacokinetic models related to measurement conditions. Using computed maps of the extra vascular volume (ve) from 19 glioma patients, we analyze differences in the number of un-physiological high-intensity ve values for both ETM and 2CXM, using a one-tailed paired t-test assuming un-equal variance.
The Bayesian parameter estimation scheme demonstrated superior performance over the LM technique in the digital phantom simulations. In addition, we identified limitations in parameter reliability in relation to scan duration for the 2CXM. DCE data for glioma and cervical cancer patients was analyzed with both algorithms and demonstrated improvement in image readability for the Bayesian method. The Bayesian method demonstrated significantly fewer non-physiological high-intensity ve values for the ETM (p<0.0001) and the 2CXM (p<0.0001).
We have demonstrated substantial improvement of the perceptive quality of pharmacokinetic parameters from advanced compartment models using the Bayesian parameter estimation scheme as compared to the LM technique.
在动态对比增强（DCE）MRI 中，分离灌注和渗漏的信号贡献需要对药代动力学模型中的参数进行稳健估计。我们提出并量化了一种从 DCE 数据计算组织血液动力学参数的方法，该方法使用已建立的药代动力学模型。
我们提出了一种贝叶斯方案，从 DCE MRI 数据中获得灌注指标。通过扩展 Tofts 模型（ETM）和双室交换模型（2CXM）的数字体模，评估初始性能，将贝叶斯方案与标准 Levenberg-Marquardt（LM）算法进行比较。数字体模还用于确定与测量条件相关的药代动力学模型的局限性。使用 19 例脑胶质瘤患者的血管外容积（ve）计算图，我们使用单侧配对 t 检验（假设方差不等）分析两种模型的 ETM 和 2CXM 的非生理高强度 ve 值的数量差异。
贝叶斯参数估计方案在数字体模模拟中表现优于 LM 技术。此外，我们还确定了 2CXM 与扫描持续时间相关的参数可靠性的局限性。使用两种算法分析脑胶质瘤和宫颈癌患者的 DCE 数据，结果表明贝叶斯方法可显著提高图像可读性。与 LM 技术相比，贝叶斯方法显著减少了 ETM（p<0.0001）和 2CXM（p<0.0001）的非生理高强度 ve 值。
与 LM 技术相比，贝叶斯参数估计方案可显著提高高级室模型药代动力学参数的感知质量。
