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Effect of reconstruction algorithm on parametric images in PET dynamic study: comparison between FBP and OS-EM

机译:重建算法对宠物动态研究参数图像的影响:FBP与OS-EM之间的比较

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Ordered subset 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 phantom and to an 18{sup left}F-FDG PET kinetic study to generate parametric images. A 45min dynamic scan was performed starting injection of 86-223MBq of FDG using a 2D PET scanner. The images were reconstructed with OS-EM (6 iterations, 16 subsets) and with filtered backprojection (FEP), and K1, k2 and k3 images were created with Marquardt method. The activity images by OS-EM correlated fairly well with those by FBP. Although the mean values of K1, k2 and k3 for OS-EM were almost equal to those for FBP, the pixel correlation between OS-EM and FEP showed an offset from the line of identity in parametric images, possibly due to different noise characteristics. The kinetic fitting error for OS-EM was no smaller than that for FEP. The results suggest that OS-EM is not necessarily superior to FBP for creating parametric images.
机译:订购的子集期望最大化(OS-EM)算法用于图像重建以抑制图像噪声并制作非负值图像。我们已将OS-EM应用于数字幻影,并向18 {SUP左} F-FDG宠物动力学研究以生成参数图像。使用2D PET扫描仪开始注入45min动态扫描86-223MBQ的FDG。通过OS-EM(6个迭代,16个子集)重建图像,并使用Marquardt方法创建滤波反冲(FEP),K1,K2和K3图像。 OS-EM的活动图像与FBP的OS-EM相当好。尽管OS-EM的K1,K2和K3的平均值几乎等于FBP的那些,但是OS-EM和FEP之间的像素相关性从参数图像中的标识线偏移,可能是由于不同的噪声特性。 OS-EM的动力学拟合误差不小于FEP。结果表明OS-EM不一定优于创建参数图像的FBP。

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