Patlak image estimation from dual time-point list-mode PET data.
Patlak image estimation from dual time-point list-mode PET data.
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DOI：
10.1109/tmi.2014.2298868
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发表时间：
2014-04
影响因子：
10.6
通讯作者：
Leahy RM
中科院分区：
文献类型：
--
作者：
Zhu W;Li Q;Bai B;Conti PS;Leahy RM
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，其中我们使用每个床位置的两帧数据计算Patlak参数的体素估计。我们的方法直接使用列表模式的到达时间为每个事件估计Patlak参数图像。我们使用惩罚似然方法，其中的惩罚函数使用空间变化的加权，以确保计数独立的局部脉冲响应。我们使用Cramer Rao分析和Monte Carlo模拟，比较两帧之间SUV值（%DSUV）的分数变化，评估该方法的性能。使用受试者工作特征（ROC）曲线来比较基于动态数据集相对于背景区分肿瘤的性能。使用ROC曲线下面积作为性能指标，我们在一系列动态数据集和参数上显示了Patlak相对于%DSUV的上级性能。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，并且可能导致相对于%DSUV指标的上级肿瘤检测。
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.