In PET, corrections for photon scatter and attenuation are essential for visual and quantitative consistency. MR attenuation correction (MRAC) is generally conducted by image segmentation and assignment of discrete attenuation coefficients, which offer limited accuracy compared with CT attenuation correction. Potential inaccuracies in MRAC may affect scatter correction, because the attenuation image (μ-map) is used in single scatter simulation (SSS) to calculate the scatter estimate. We assessed the impact of MRAC to scatter correction using 2 scatter-correction techniques and 3 μ-maps for MRAC. Methods: The tail-fitted SSS (TF-SSS) and a Monte Carlo–based single scatter simulation (MC-SSS) algorithm implementations on the Philips Ingenuity TF PET/MR were used with 1 CT-based and 2 MR-based μ-maps. Data from 7 subjects were used in the clinical evaluation, and a phantom study using an anatomic brain phantom was conducted. Scatter-correction sinograms were evaluated for each scatter correction method and μ-map. Absolute image quantification was investigated with the phantom data. Quantitative assessment of PET images was performed by volume-of-interest and ratio image analysis. Results: MRAC did not result in large differences in scatter algorithm performance, especially with TF-SSS. Scatter sinograms and scatter fractions did not reveal large differences regardless of the μ-map used. TF-SSS showed slightly higher absolute quantification. The differences in volume-of-interest analysis between TF-SSS and MC-SSS were 3% at maximum in the phantom and 4% in the patient study. Both algorithms showed excellent correlation with each other with no visual differences between PET images. MC-SSS showed a slight dependency on the μ-map used, with a difference of 2% on average and 4% at maximum when a μ-map without bone was used. Conclusion: The effect of different MR-based μ-maps on the performance of scatter correction was minimal in non–time-of-flight ¹⁸F-FDG PET/MR brain imaging. The SSS algorithm was not affected significantly by MRAC. The performance of the MC-SSS algorithm is comparable but not superior to TF-SSS, warranting further investigations of algorithm optimization and performance with different radiotracers and time-of-flight imaging.

在PET中，光子散射和衰减的校正对于视觉和定量一致性至关重要。MR衰减校正（MRAC）通常通过图像分割和离散衰减系数的分配进行，与CT衰减校正相比，其准确性有限。MRAC中的潜在误差可能会影响散射校正，因为在单个散射仿真（SSS）中使用了衰减图像（μ-map）来计算散射估计。我们使用2种散射校正技术和3个MRAC的μ-map评估了MRAC对散射校正的影响。方法：飞利浦Ingenuity TF PET / MR的尾部拟合SSS（TF-SSS）和基于蒙特卡洛的单散射仿真（MC-SSS）算法实现与1个基于CT的图和2个基于MR的μ图一起使用。来自7位受试者的数据用于临床评估，并使用解剖学的大脑体模进行了体模研究。针对每种散射校正方法和μ-map评估了散射校正正弦图。用幻象数据研究了绝对图像量化。通过感兴趣的体积和比率图像分析对PET图像进行定量评估。结果：MRAC不会导致散点算法性能产生较大差异，尤其是使用TF-SSS时。散射正弦图和散射分数没有显示出很大的差异，无论使用的是μ-map。TF-SSS的绝对定量略高。TF-SSS和MC-SSS的兴趣量分析差异在模型中最大为3％，在患者研究中最大为4％。两种算法都显示出极好的相关性，PET图像之间没有视觉差异。MC-SSS对所使用的μ贴图显示出轻微的依赖性，当使用不带骨骼的μ贴图时，平均差异为2％，最大差异为4％。结论：在非飞行时间^18中，不同的基于MR的μ-map对散射校正性能的影响最小。F-FDG PET / MR脑成像。SRAC算法不受MRAC的显着影响。MC-SSS算法的性能可与TF-SSS媲美，但并不优于TF-SSS，因此需要进一步研究算法优化和使用不同放射性示踪剂和飞行时间成像的性能。