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Abstract：
BACKGROUND: Image harmonization has been proposed to minimize heterogeneity in brain PET scans acquired in multi-center studies. However, standard validated methods and software tools are lacking. Here, we assessed the performance of a framework for the harmonization of brain PET scans in a multi-center European clinical trial.
METHODS: Hoffman 3D brain phantoms were acquired in 28 PET systems and reconstructed using site-specific settings. Full Width at Half Maximum (FWHM) of the Effective Image Resolution (EIR) and harmonization kernels were estimated for each scan. The target EIR was selected as the coarsest EIR in the imaging network. Using \"Hoffman 3D brain Analysis tool,\" indicators of image quality were calculated before and after the harmonization: The Coefficient of Variance (COV%), Gray Matter Recovery Coefficient (GMRC), Contrast, Cold-Spot RC, and left-to-right GMRC ratio. A COV% ≤ 15% and Contrast ≥ 2.2 were set as acceptance criteria. The procedure was repeated to achieve a 6-mm target EIR in a subset of scans. The method\'s robustness against typical dose-calibrator-based errors was assessed.
RESULTS: The EIR across systems ranged from 3.3 to 8.1 mm, and an EIR of 8 mm was selected as the target resolution. After harmonization, all scans met acceptable image quality criteria, while only 13 (39.4%) did before. The harmonization procedure resulted in lower inter-system variability indicators: Mean ± SD COV% (from 16.97 ± 6.03 to 7.86 ± 1.47%), GMRC Inter-Quartile Range (0.040-0.012), and Contrast SD (0.14-0.05). Similar results were obtained with a 6-mm FWHM target EIR. Errors of ± 10% in the DRO activity resulted in differences below 1 mm in the estimated EIR.
CONCLUSIONS: Harmonizing the EIR of brain PET scans significantly reduced image quality variability while minimally affecting quantitative accuracy. This method can be used prospectively for harmonizing scans to target sharper resolutions and is robust against dose-calibrator errors. Comparable image quality is attainable in brain PET multi-center studies while maintaining quantitative accuracy.
METHODS: Hoffman 3D brain phantoms were acquired in 28 PET systems and reconstructed using site-specific settings. Full Width at Half Maximum (FWHM) of the Effective Image Resolution (EIR) and harmonization kernels were estimated for each scan. The target EIR was selected as the coarsest EIR in the imaging network. Using \"Hoffman 3D brain Analysis tool,\" indicators of image quality were calculated before and after the harmonization: The Coefficient of Variance (COV%), Gray Matter Recovery Coefficient (GMRC), Contrast, Cold-Spot RC, and left-to-right GMRC ratio. A COV% ≤ 15% and Contrast ≥ 2.2 were set as acceptance criteria. The procedure was repeated to achieve a 6-mm target EIR in a subset of scans. The method\'s robustness against typical dose-calibrator-based errors was assessed.
RESULTS: The EIR across systems ranged from 3.3 to 8.1 mm, and an EIR of 8 mm was selected as the target resolution. After harmonization, all scans met acceptable image quality criteria, while only 13 (39.4%) did before. The harmonization procedure resulted in lower inter-system variability indicators: Mean ± SD COV% (from 16.97 ± 6.03 to 7.86 ± 1.47%), GMRC Inter-Quartile Range (0.040-0.012), and Contrast SD (0.14-0.05). Similar results were obtained with a 6-mm FWHM target EIR. Errors of ± 10% in the DRO activity resulted in differences below 1 mm in the estimated EIR.
CONCLUSIONS: Harmonizing the EIR of brain PET scans significantly reduced image quality variability while minimally affecting quantitative accuracy. This method can be used prospectively for harmonizing scans to target sharper resolutions and is robust against dose-calibrator errors. Comparable image quality is attainable in brain PET multi-center studies while maintaining quantitative accuracy.
摘要：
背景：已提出图像协调，以最大程度地减少在多中心研究中获得的脑PET扫描中的异质性。然而,缺乏标准的验证方法和软件工具。这里,我们在一项多中心的欧洲临床试验中评估了脑PET扫描统一框架的性能.
方法：在28个PET系统中获取Hoffman3D脑模型，并使用特定部位的设置进行重建。对于每次扫描,估计有效图像分辨率(EIR)的半峰全宽(FWHM)和协调核。选择目标EIR作为成像网络中最粗的EIR。使用\"霍夫曼3D大脑分析工具，“在协调前后计算图像质量指标：变异系数（COV%），灰质恢复系数（GMRC），对比,冷点RC,和左右GMRC比率。设定COV%≤15%和对比度≥2.2作为验收标准。重复该程序以在扫描的子集中实现6mm目标EIR。评估了该方法对典型剂量校准器误差的鲁棒性。
结果：整个系统的EIR范围为3.3至8.1毫米，并且选择8mm的EIR作为目标分辨率。统一后，所有扫描均符合可接受的图像质量标准，而之前只有13人（39.4%）这样做。协调程序导致较低的系统间变异性指标：平均值±SDCOV%（从16.97±6.03到7.86±1.47%），GMRC四分位数间范围（0.040-0.012），和对比度SD（0.14-0.05）。用6-mmFWHM靶EIR获得类似的结果。DRO活性中±10%的误差导致估计的EIR中低于1mm的差异。
结论：协调脑PET扫描的EIR显著降低了图像质量变异性，同时对定量准确性的影响最小。该方法可以前瞻性地用于协调扫描以达到更清晰的分辨率，并且对剂量校准器误差具有鲁棒性。在脑PET多中心研究中可获得相当的图像质量,同时保持定量准确性。
方法：在28个PET系统中获取Hoffman3D脑模型，并使用特定部位的设置进行重建。对于每次扫描,估计有效图像分辨率(EIR)的半峰全宽(FWHM)和协调核。选择目标EIR作为成像网络中最粗的EIR。使用\"霍夫曼3D大脑分析工具，“在协调前后计算图像质量指标：变异系数（COV%），灰质恢复系数（GMRC），对比,冷点RC,和左右GMRC比率。设定COV%≤15%和对比度≥2.2作为验收标准。重复该程序以在扫描的子集中实现6mm目标EIR。评估了该方法对典型剂量校准器误差的鲁棒性。
结果：整个系统的EIR范围为3.3至8.1毫米，并且选择8mm的EIR作为目标分辨率。统一后，所有扫描均符合可接受的图像质量标准，而之前只有13人（39.4%）这样做。协调程序导致较低的系统间变异性指标：平均值±SDCOV%（从16.97±6.03到7.86±1.47%），GMRC四分位数间范围（0.040-0.012），和对比度SD（0.14-0.05）。用6-mmFWHM靶EIR获得类似的结果。DRO活性中±10%的误差导致估计的EIR中低于1mm的差异。
结论：协调脑PET扫描的EIR显著降低了图像质量变异性，同时对定量准确性的影响最小。该方法可以前瞻性地用于协调扫描以达到更清晰的分辨率，并且对剂量校准器误差具有鲁棒性。在脑PET多中心研究中可获得相当的图像质量,同时保持定量准确性。
