University of Lille, Inserm U1171, CHU Lille, F-59000 Lille, France. Department of Nuclear Medicine, CHU Lille, F-59000 Lille, France. Department of Neuroradiology, CHU Lille, F-59000 Lille, France. Author to whom any correspondence should be addressed.
Phys Med Biol. 2019 Aug 28;64(17):175002. doi: 10.1088/1361-6560/ab35b4.
This study aims at assessing whether EANM harmonisation strategy combined with EQ·PET methodology could be successfully applied to harmonize brain 2-deoxy-2[F]fluoro-D-glucose ([F]FDG) positron emission tomography (PET) images. The NEMA NU 2 body phantom was prepared according to the EANM guidelines with an [F]FDG solution. Raw PET phantom data were reconstructed with three different reconstruction protocols frequently used in clinical PET brain imaging: ([Formula: see text]) Ordered subset expectation maximization (OSEM) 3D with time of flight (TOF), 2 iterations and 21 subsets; ([Formula: see text]) OSEM 3D with TOF, 6 iterations and 21 subsets; and ([Formula: see text]) OSEM 3D with TOF, point spread function (PSF), and 8 iterations and 21 subsets. EQ·PET filters were computed as the Gaussian smoothing that best independently aligned the recovery coefficients (RCs) of reconstructions [Formula: see text] and [Formula: see text] with the RCs of the reference reconstruction, [Formula: see text]. The performance of the EQ·PET filter to reduce variations in quantification due to differences in reconstruction was investigated using clinical PET brain images of 35 early-onset Alzheimer's disease (EOAD) patients. Qualitative assessments and multiple quantitative metrics on the cortical surface at different scale levels with or without partial volume effect correction were evaluated on the [F]FDG brain data before and after application of the EQ·PET filter. The EQ·PET methodology succeeded in finding the optimal smoothing that minimised root-mean-square error (RMSE) calculated using human brain [F]FDG-PET datasets of EOAD patients, providing harmonized comparisons in the neurological context. Performance was superior for TOF than for TOF + PSF reconstructions. Results showed the capability of the EQ·PET methodology to minimize reconstruction-induced variabilities between brain [F]FDG-PET images. However, moderate variabilities remained after harmonizing PSF reconstructions with standard non-PSF OSEM reconstructions, suggesting that precautions should be taken when using PSF modelling.
本研究旨在评估 EANM 协调策略结合 EQ·PET 方法是否可成功应用于协调脑 2-脱氧-2[F]氟-D-葡萄糖([F]FDG)正电子发射断层扫描(PET)图像。根据 EANM 指南，使用[F]FDG 溶液制备 NEMA NU 2 体模。使用临床 PET 脑成像中常用的三种不同重建方案对原始 PET 体模数据进行重建：([Formula: see text])带飞行时间(TOF)的有序子集期望最大化(OSEM)3D，2 次迭代和 21 个子集；([Formula: see text])带 TOF 的 OSEM 3D，6 次迭代和 21 个子集；以及([Formula: see text])带 TOF、点扩散函数(PSF)的 OSEM 3D，8 次迭代和 21 个子集。EQ·PET 滤波器被计算为最佳的高斯平滑，可分别将重建[Formula: see text]和[Formula: see text]的恢复系数(RC)与参考重建[Formula: see text]的 RC 对齐。使用 35 例早发性阿尔茨海默病(EOAD)患者的临床 PET 脑图像研究了 EQ·PET 滤波器减少由于重建差异导致的定量变化的性能。在应用 EQ·PET 滤波器前后，在皮质表面的不同尺度水平上，使用或不使用部分容积效应校正，对[F]FDG 脑数据进行了定性评估和多种定量指标评估。EQ·PET 方法成功地找到了最小化均方根误差(RMSE)的最佳平滑，该误差是使用 EOAD 患者的人脑[F]FDG-PET 数据集计算得出的，在神经学背景下提供了协调的比较。与 TOF + PSF 重建相比，TOF 的性能更优。结果表明，EQ·PET 方法能够最小化脑[F]FDG-PET 图像之间由于重建引起的变异性。然而，在用标准非 PSF OSEM 重建协调 PSF 重建后，仍然存在中度变异性，这表明在使用 PSF 建模时应谨慎。
