PDF(Pubmed)
Abstract：
The accurate estimation of the tracer arterial blood concentration is crucial for reliable quantitative kinetic analysis in PET. In the current work, we demonstrate the automatic extraction of an image-derived input function (IDIF) from a CT AI-based aorta segmentation subsequently resliced to a dynamic PET series acquired on a Siemens Vision Quadra long-axial field of view scanner in 10 human subjects scanned with [15O]H2O. We demonstrate that the extracted IDIF is quantitative and in excellent agreement with a delay- and dispersion-corrected sampled arterial input function (AIF). Perfusion maps in the brain are calculated and compared from the IDIF and AIF, respectively, showed a high degree of correlation. The results demonstrate the possibility of defining a quantitatively correct IDIF compared with AIFs from the new-generation high-sensitivity and high-time-resolution long-axial field-of-view PET/CT scanners.
摘要：
准确估计示踪动脉血浓度对于PET中可靠的定量动力学分析至关重要。在目前的工作中,我们演示了从基于CTAI的主动脉分割中自动提取图像衍生输入函数（IDIF）的方法，随后将其重新划分为在SiemensVisionQuadra长轴视场扫描仪上采集的动态PET系列，并扫描了10位人类受试者[15O]H2O。我们证明提取的IDIF是定量的，并且与延迟和分散校正的采样动脉输入函数（AIF）非常吻合。从IDIF和AIF计算并比较大脑中的灌注图，分别,表现出高度的相关性。结果表明，与新一代高灵敏度和高时间分辨率的长轴视场PET/CT扫描仪的AIF相比，可以定义定量正确的IDIF。
