目标:今天,正电子发射断层成像/磁共振(PET/MR)硬件组件的衰减校正(AC)通过使用PET/CT混合成像的既定方法来执行。如以前的研究所示,从计算机断层扫描(CT)到PET衰减系数的既定数学转换可以,然而,当应用于PET/MR中硬件组件的AC时,会导致PET定量结果不正确。这项研究的目的是系统地研究PET/MR混合成像中经常使用的各种材料和电子元件的衰减特性。这项研究,因此,旨在改善PET/MR中的硬件组件衰减校正。
方法:总的来说,收集了38个不同的材料样本;使用模块化体模进行CT,PET,和所有样品的PET/MR扫描。以140kVp的管电压采集计算机断层扫描以确定亨氏单位(HU)。用511keV进行PET透射扫描以确定所有材料的线性衰减系数(LAC)。绘制衰减系数以获得HU到LAC的相关图,然后将其与文学中的两种既定转换进行比较。在体模验证实验之后,创建不同材料的硬件衰减图并将其应用于PET数据重建。从这些测量中,计算PET差异图以验证和比较所有三种转换方法。
结果:对于每种材料,可以确定HU和相应的LAC,并得出双线性HU到LAC的转换图。相应的方程为y=1.64*10-5×HU+1000+8.3*10-2。虽然在体模实验中,两种确定的转化导致平均定量PET偏差为4.69%±0.27%和-2.84%±0.72%,当应用本研究产生的新转化时,PET差异测量显示PET定量中只有0.5%的偏差。
结论:通过对38种不同材料的系统测量,已经得出了将CT转换为PET衰减系数的优化方法。与既定的方法相反,新的转换还考虑了高衰减材料,从而改善PET/MR混合成像中硬件组件的衰减校正。
OBJECTIVE: Today, attenuation correction (AC) of positron emission tomography/magnetic resonance (PET/MR) hardware components is performed by using an established method from PET/CT hybrid imaging. As shown in previous studies, the established mathematical conversion from computed tomography (CT) to PET attenuation coefficients may, however, lead to incorrect results in PET quantification when applied to AC of hardware components in PET/MR. The purpose of this study is to systematically investigate the attenuating properties of various materials and electronic components frequently used in the context of PET/MR hybrid imaging. The study, thus, aims at improving hardware component attenuation correction in PET/MR.
METHODS: Overall, 38 different material samples were collected; a modular phantom was used to for CT, PET, and PET/MR scanning of all samples. Computed tomography-scans were acquired with a tube voltage of 140 kVp to determine Hounsfield Units (HU). PET transmission scans were performed with 511 keV to determine linear attenuation coefficients (LAC) of all materials. The attenuation coefficients were plotted to obtain a HU to LAC correlation graph, which was then compared to two established conversions from literature. Hardware attenuation maps of the different materials were created and applied to PET data reconstruction following a phantom validation experiment. From these measurements, PET difference maps were calculated to validate and compare all three conversion methods.
RESULTS: For each material, the HU and corresponding LAC could be determined and a bi-linear HU to LAC conversion graph was derived. The corresponding equation was y = 1.64 ∗ 10 - 5 × HU + 1000 + 8.3 ∗ 10 - 2 . While the two established conversions lead to a mean quantification PET bias of 4.69% ± 0.27% and -2.84% ± 0.72% in a phantom experiment, PET difference measurements revealed only 0.5 % bias in PET quantification when applying the new conversion resulting from this study.
CONCLUSIONS: An optimized method for the conversion of CT to PET attenuation coefficients has been derived by systematic measurement of 38 different materials. In contrast to established methods, the new conversion also considers highly attenuating materials, thus improving attenuation correction of hardware components in PET/MR hybrid imaging.