Rahman Md Ashequr, Laforest Richard, Jha Abhinav K
Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA.
Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA.
Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:646-650. doi: 10.1109/isbi45749.2020.9098333. Epub 2020 May 22.
Reliable attenuation and scatter compensation (ASC) is a pre-requisite for quantification and beneficial for visual interpretation tasks in SPECT. In this paper, we develop a reconstruction method that uses the entire SPECT emission data, i.e. data in both the photopeak and scatter windows, acquired in list-mode format and including the energy attribute of the detected photon, to perform ASC. We implemented a GPU-based version of this method using an ordered subsets expectation maximization (OSEM) algorithm. The method was objectively evaluated using realistic simulation studies on the task of estimating uptake in the striatal regions of the brain in a 2-D dopamine transporter (DaT)-scan SPECT study. We observed that inclusion of data from the scatter window and using list-mode data yielded improved quantification compared to using data only from the photopeak window or using binned data. These results motivate further development of list-mode-based ASC methods that include scatter-window data for SPECT.
可靠的衰减和散射补偿(ASC)是SPECT中进行定量分析的前提条件,并且有利于视觉解释任务。在本文中,我们开发了一种重建方法,该方法使用以列表模式格式采集的整个SPECT发射数据,即光电峰和散射窗口中的数据,并包括检测到的光子的能量属性,来执行ASC。我们使用有序子集期望最大化(OSEM)算法实现了该方法的基于GPU的版本。在二维多巴胺转运体(DaT)扫描SPECT研究中,通过对估计大脑纹状体区域摄取量的任务进行逼真的模拟研究,对该方法进行了客观评估。我们观察到,与仅使用光电峰窗口的数据或使用分箱数据相比,纳入散射窗口的数据并使用列表模式数据可实现更好的定量分析。这些结果推动了基于列表模式的ASC方法的进一步发展,这些方法包括用于SPECT的散射窗口数据。