Guangdong Provincial Key Laboratory of Medical Image Processing and School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, China.
Guangdong Provincial Key Laboratory of Medical Image Processing and School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, China.
Comput Biol Med. 2021 Dec;139:104713. doi: 10.1016/j.compbiomed.2021.104713. Epub 2021 Jul 31.
In dynamic positron emission tomography (PET) imaging, the reconstructed image of a single frame often exhibits high noise due to limited counting statistics of projection data. This study proposed a median nonlocal means (MNLM)-based kernel method for dynamic PET image reconstruction. The kernel matrix is derived from median nonlocal means of pre-reconstructed composite images. Then the PET image intensities in all voxels were modeled as a kernel matrix multiplied by coefficients and incorporated into the forward model of PET projection data. Then, the coefficients of each feature were estimated by the maximum likelihood method. Using simulated low-count dynamic data of Zubal head phantom, the quantitative performance of the proposed MNLM kernel method was investigated and compared with the maximum-likelihood method, conventional kernel method with and without median filter, and nonlocal means (NLM) kernel method. Simulation results showed that the MNLM kernel method achieved visual and quantitative accuracy improvements (in terms of the ensemble mean squared error, bias versus variance, and contrast versus noise performances). Especially for frame 2 with the lowest count level of a single frame, the MNLM kernel method achieves lower ensemble mean squared error (10.43%) than the NLM kernel method (13.68%), conventional kernel method with and without median filter (11.88% and 23.50%), and MLEM algorithm (24.77%). The study on real low-dose F-FDG rat data also showed that the MNLM kernel method outperformed other methods in visual and quantitative accuracy improvements (in terms of regional noise versus intensity mean performance).
在动态正电子发射断层扫描（PET）成像中，由于投影数据的有限计数统计量，单个帧的重建图像通常会呈现出较高的噪声。本研究提出了一种基于中值非局部均值（MNLM）的核方法，用于动态 PET 图像重建。核矩阵是从预重建的复合图像的中值非局部均值导出的。然后，将所有体素中的 PET 图像强度建模为核矩阵乘以系数，并将其纳入 PET 投影数据的正向模型。然后，通过最大似然法估计每个特征的系数。使用 Zubal 头部幻影的模拟低计数动态数据，研究了所提出的 MNLM 核方法的定量性能，并与最大似然法、具有和不具有中值滤波器的常规核方法以及非局部均值（NLM）核方法进行了比较。仿真结果表明，MNLM 核方法在视觉和定量准确性方面都有所提高（在总体均方误差、偏差与方差、对比度与噪声性能方面）。特别是对于单个帧中计数水平最低的第 2 帧，MNLM 核方法的总体均方误差（10.43%）低于 NLM 核方法（13.68%）、具有和不具有中值滤波器的常规核方法（11.88%和 23.50%）以及 MLEM 算法（24.77%）。对真实低剂量 F-FDG 大鼠数据的研究也表明，MNLM 核方法在视觉和定量准确性方面都优于其他方法（在区域噪声与强度均值性能方面）。
