Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, No.1, Sec. 1, Jen Ai Rd., Zhongzheng Dist., Taipei City 100, Taiwan.
Phys Med Biol. 2020 Nov 17;65(22):225006. doi: 10.1088/1361-6560/abb1d8.
Due to high levels of noise in pixel-wise time-activity curves, the indirect method that generates kinetic parametric images from dynamic positron emission tomography (PET) images often results in poor parametric image quality. We have demonstrated that the quality of parametric images can be improved by denoising dynamic PET images, using gradient-free curve-fitting and applying a kernel-based post-filtering to parametric images. However, many gradient-free curve-fitting methods are time-consuming. Moreover, some parameter estimates (e.g. k and k) have large variability. To provide high-quality PET parametric images with low computational cost, we propose a curve-fitting method that incorporates the kernel-based denoising method and the highly constrained backprojection technique into the Levenberg-Marquardt (LM) algorithm. We conducted a simulation study to evaluate the performance of the proposed curve-fitting method. Dynamic PET images were reconstructed using the expectation-maximization (EM) algorithm and were denoised before parameter estimation. Compared to the LM algorithm with and without the kernel-based post-filtering, the proposed method achieved superior performance, offering a decrease in both bias and coefficient of variation (CV) on all parametric images. Overall, the proposed method exhibited lower bias and slightly higher CV than the gradient-free pattern search method with the kernel-based post-filtering (PatS-K). Moreover, the computation time of the proposed method was about 18 times lower than that of the PatS-K method. Finally, we show that the proposed method can further improve the quality of parametric images when dynamic PET images are reconstructed using the kernel-based EM algorithm.
由于像素级时间-活性曲线中的噪声水平较高，从动态正电子发射断层扫描 (PET) 图像生成动力学参数图像的间接方法通常会导致参数图像质量较差。我们已经证明，通过对动态 PET 图像进行去噪、使用无梯度曲线拟合并对参数图像应用基于核的后滤波，可以改善参数图像的质量。然而，许多无梯度曲线拟合方法都很耗时。此外，一些参数估计值（例如 k 和 k）的变异性较大。为了以低计算成本提供高质量的 PET 参数图像，我们提出了一种曲线拟合方法，该方法将基于核的去噪方法和高度约束的反向投影技术纳入到 Levenberg-Marquardt (LM) 算法中。我们进行了一项模拟研究来评估所提出的曲线拟合方法的性能。使用期望最大化 (EM) 算法重建动态 PET 图像，并在参数估计之前对其进行去噪。与具有和不具有基于核的后滤波的 LM 算法相比，所提出的方法具有更好的性能，所有参数图像的偏差和变异系数 (CV) 都有所降低。总体而言，所提出的方法的偏差低于无梯度模式搜索方法与基于核的后滤波（PatS-K），CV 略高。此外，与 PatS-K 方法相比，所提出的方法的计算时间大约低 18 倍。最后，我们表明，当使用基于核的 EM 算法重建动态 PET 图像时，所提出的方法可以进一步提高参数图像的质量。
