Zhu Wentao, Li Quanzheng, Bai Bing, Conti Peter S, Leahy Richard M
IEEE Trans Med Imaging. 2014 Apr;33(4):913-24. doi: 10.1109/TMI.2014.2298868.
We investigate using dual time-point PET data to perform Patlak modeling. This approach can be used for whole body dynamic PET studies in which we compute voxel-wise estimates of Patlak parameters using two frames of data for each bed position. Our approach directly uses list-mode arrival times for each event to estimate the Patlak parametric image. We use a penalized likelihood method in which the penalty function uses spatially variant weighting to ensure a count independent local impulse response. We evaluate performance of the method in comparison to fractional changes in SUV values (%DSUV) between the two frames using Cramer Rao analysis and Monte Carlo simulation. Receiver operating characteristic (ROC) curves are used to compare performance in differentiating tumors relative to background based on the dynamic data sets. Using area under the ROC curve as a performance metric, we show superior performance of Patlak relative to %DSUV over a range of dynamic data sets and parameters. These results suggest that Patlak analysis may be appropriate for analysis of dual time-point whole body PET data and could lead to superior detection of tumors relative to %DSUV metrics.
我们研究使用双时间点PET数据进行Patlak建模。这种方法可用于全身动态PET研究，即我们针对每个床位位置使用两帧数据来计算Patlak参数的体素级估计值。我们的方法直接使用每个事件的列表模式到达时间来估计Patlak参数图像。我们使用一种惩罚似然方法，其中惩罚函数使用空间变化加权来确保与计数无关的局部脉冲响应。我们使用Cramer Rao分析和蒙特卡罗模拟，与两帧之间SUV值的分数变化（%DSUV）相比较，来评估该方法的性能。使用接受者操作特征（ROC）曲线，基于动态数据集比较区分肿瘤与背景的性能。以ROC曲线下面积作为性能指标，我们展示了在一系列动态数据集和参数上，Patlak相对于%DSUV的卓越性能。这些结果表明，Patlak分析可能适用于双时间点全身PET数据的分析，并且相对于%DSUV指标可能会带来更好的肿瘤检测效果。
