Datasheet1_Single-voxel delay map from long-axial field-of-view PET scans.docx

ObjectiveWe present an algorithm to estimate the delay between a tissue time activity curve and a blood input curve at a single-voxel level tested on whole-body data from a long-axial field-of-view scanner with tracers of different noise characteristics.
MethodsWhole-body scans of 15 patients divided equally among three tracers: [15O]H2O, [18F]FDG and [64Cu]Cu-DOTATATE, were used in development and testing of the algorithm. Delay time were estimated by fitting the cumulatively summed input function and tissue time activity curve with special considerations for noise. To evaluate the performance of the algorithm, it was compared against two other algorithms also commonly applied in delay estimation, name cross-correlation and a one-tissue compartment model with incorporated delay. All algorithms were tested on both synthetic time activity curves produced with the one-tissue compartment model with increasing levels of noise and delays between the tissue activity curve and the blood input curve. Whole-body delay maps were also calculated for each of the three tracers with data acquired on a long-axial field-of-view scanner with high time resolution.
ResultsOur proposed model performs better for low signal-to-noise ratio time activity curves compared to both cross-correlation and the one-tissue compartment models for non-[15O]H2O tracers. Testing on synthetically produced time activity curves it displays only a small and even residual delay, while the one-tissue compartment model with included delay showed varying residual delays.
ConclusionThe algorithm is robust to noise and proves applicable on a range of tracers as tested on [15O]H2O, [18F]FDG and [64Cu]Cu-DOTATATE, and hence is a viable option offering the ability for delay correction across various organs and tracers in use with kinetic modeling.

研究目标：我们提出一种算法，可在单体素层面（single-voxel level）估算组织时间-活性曲线（tissue time activity curve）与血液输入曲线（blood input curve）之间的延迟，并基于长轴向视野扫描仪（long-axial field-of-view scanner）获取的全身数据进行测试，测试所用显像剂（tracer）具备不同的噪声特性。

研究方法：将15名患者平均分为三组，每组各对应一种显像剂，分别为[15O]H2O、[18F]FDG与[64Cu]Cu-DOTATATE，其全身扫描数据被用于算法的开发与测试。本算法通过拟合累积求和的输入函数与组织时间-活性曲线，并针对噪声问题进行特殊处理，以此估算延迟时间。为评估该算法的性能，我们将其与另外两种常用于延迟估算的算法进行对比：分别为互相关（cross-correlation）法，以及引入延迟项的单组织室模型（one-tissue compartment model）。所有算法均在两类合成组织时间-活性曲线上开展测试：一类是通过引入不同水平噪声与组织活性曲线-血液输入曲线延迟的单组织室模型生成的合成曲线；另一类则是在高时间分辨率的长轴向视野扫描仪上采集的三种显像剂的全身数据，据此计算得到全身延迟映射图。

研究结果：相较于互相关法与引入延迟项的单组织室模型，我们提出的模型在低信噪比（signal-to-noise ratio）的组织时间-活性曲线上表现更优，尤其适用于非[15O]H2O类显像剂。在合成组织时间-活性曲线的测试中，该模型仅存在微小且均匀的残余延迟；而引入延迟项的单组织室模型则表现出不同程度的残余延迟。

研究结论：本算法具备良好的抗噪声性能，且在[15O]H2O、[18F]FDG与[64Cu]Cu-DOTATATE三种显像剂的测试中展现出广泛的适用性，因此可作为一种可行方案，实现在动力学建模（kinetic modeling）应用中，对不同器官与显像剂的延迟进行校正。