Ghanbari Nasrin, Clarkson Eric, Kupinski Matthew, Li Xin
College of Optical Sciences, University of Arizona, Tucson, AZ, 85721 USA.
IEEE Trans Radiat Plasma Med Sci. 2017 Sep;1(5):435-443. doi: 10.1109/TRPMS.2017.2715041. Epub 2017 Jun 13.
A method for optimization of an adaptive Single Photon Emission Computed Tomography (SPECT) system is presented. Adaptive imaging systems can quickly change their hardware configuration in response to data being generated in order to improve image quality for a specific task. In this work we simulate an adaptive SPECT system and propose a method for finding the adaptation that maximizes the performance on a signal estimation task. To start with, a simulated object model containing a spherical signal is imaged with a scout configuration. A Markov-Chain Monte Carlo (MCMC) technique utilizes the scout data to generate an ensemble of possible objects consistent with the scout data. This object ensemble is imaged by numerous simulated hardware configurations and for each system estimates of signal activity, size and location are calculated via the Scanning Linear Estimator (SLE). A figure of merit, based on a Modified Dice Index (MDI), quantifies the performance of each imaging configuration and it allows for optimization of the adaptive SPECT. This figure of merit is calculated by multiplying two terms: the first term uses the definition of the Dice similarity index to determine the percent of overlap between the actual and the estimated spherical signal, the second term utilizes an exponential function that measures the squared error for the activity estimate. The MDI combines the error in estimates of activity, size, and location, in one convenient metric and it allows for simultaneous optimization of the SPECT system with respect to all the estimated signal parameters. The results of our optimizations indicate that the adaptive system performs better than a non-adaptive one in conditions where the diagnostic scan has a low photon count - on the order of thousand photons per projection. In a statistical study, we optimized the SPECT system for one hundred unique objects and demonstrated that the average MDI on an estimation task is 0.84 for the adaptive system and 0.65 for the non-adaptive system.
本文提出了一种优化自适应单光子发射计算机断层扫描（SPECT）系统的方法。自适应成像系统可以根据生成的数据快速改变其硬件配置，以提高特定任务的图像质量。在这项工作中，我们模拟了一个自适应SPECT系统，并提出了一种方法来找到能使信号估计任务性能最大化的自适应方式。首先，使用一种预扫描配置对包含球形信号的模拟对象模型进行成像。马尔可夫链蒙特卡罗（MCMC）技术利用预扫描数据生成与预扫描数据一致的一系列可能对象。通过众多模拟硬件配置对这个对象集进行成像，并针对每个系统通过扫描线性估计器（SLE）计算信号活性、大小和位置的估计值。基于修正骰子指数（MDI）的品质因数量化了每个成像配置的性能，并允许对自适应SPECT进行优化。这个品质因数通过将两个项相乘来计算：第一项使用骰子相似性指数的定义来确定实际球形信号与估计球形信号之间的重叠百分比，第二项利用一个指数函数来测量活性估计的平方误差。MDI在一个方便的度量中结合了活性、大小和位置估计中的误差，并允许针对所有估计的信号参数同时优化SPECT系统。我们的优化结果表明，在诊断扫描光子计数较低（每个投影约一千个光子）的情况下，自适应系统的性能优于非自适应系统。在一项统计研究中，我们针对一百个独特对象优化了SPECT系统，并证明在估计任务中，自适应系统的平均MDI为0.84，非自适应系统为0.65。
