Oda K, Toyama H, Uemura K, Ikoma Y, Kimura Y, Senda M
Positron Medical Center, Tokyo Metropolitan Institute of Gerontology, Japan.
Ann Nucl Med. 2001 Oct;15(5):417-23. doi: 10.1007/BF02988345.
An ordered subsets expectation maximization (OS-EM) algorithm is used for image reconstruction to suppress image noise and to make non-negative value images. We have applied OS-EM to a digital brain phantom and to human brain 18F-FDG PET kinetic studies to generate parametric images. A 45 min dynamic scan was performed starting injection of FDG with a 2D PET scanner. The images were reconstructed with OS-EM (6 iterations, 16 subsets) and with filtered backprojection (FBP), and K1, k2 and k3 images were created by the Marquardt non-linear least squares method based on the 3-parameter kinetic model. Although the OS-EM activity images correlated fairly well with those obtained by FBP, the pixel correlations were poor for the k2 and k3 parametric images, but the plots were scattered along the line of identity and the mean values for K1, k2 and k3 obtained by OS-EM were almost equal to those by FBP. The kinetic fitting error for OS-EM was no smaller than that for FBP. The results suggest that OS-EM is not necessarily superior to FBP for creating parametric images.
有序子集期望最大化（OS-EM）算法用于图像重建，以抑制图像噪声并生成非负图像。我们已将OS-EM应用于数字脑模型和人脑18F-FDG PET动力学研究，以生成参数图像。使用二维PET扫描仪，在注射FDG后进行了45分钟的动态扫描。图像用OS-EM（6次迭代，16个子集）和滤波反投影（FBP）重建，并基于三参数动力学模型通过Marquardt非线性最小二乘法创建K1、k2和k3图像。尽管OS-EM活性图像与FBP获得的图像相关性相当好，但k2和k3参数图像的像素相关性较差，但图沿恒等线分散，OS-EM获得的K1、k2和k参数的平均值几乎等于FBP获得的平均值。OS-EM的动力学拟合误差不小于FBP的误差。结果表明，在创建参数图像方面，OS-EM不一定优于FBP。
