Centre for Medical Image Computing; Department of Medical Physics and Biomedical Engineering, University College London Gower Street WC1E 6BT, London, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, UK. Electronic address: http://www.nmi.cs.ucl.ac.uk.
Division of Neuroscience & Experimental Psychology, University of Manchester, UK.
Neuroimage. 2021 May 15;232:117821. doi: 10.1016/j.neuroimage.2021.117821. Epub 2021 Feb 12.
Accurate regional brain quantitative PET measurements, particularly when using partial volume correction, rely on robust image registration between PET and MR images. We argue here that the precision, and hence the uncertainty, of MR-PET image registration is mainly driven by the registration implementation and the quality of PET images due to their lower resolution and higher noise compared to the structural MR images. We propose a dedicated uncertainty analysis for quantifying the precision of MR-PET registration, centred around the bootstrap resampling of PET list-mode events to generate multiple PET image realisations with different noise (count) levels. The effects of PET image reconstruction parameters, such as the use of attenuation and scatter corrections and different number of iterations, on the precision and accuracy of MR-PET registration were investigated. In addition, the performance of four software packages with their default settings for rigid inter-modality image registration were considered: NiftyReg, Vinci, FSL and SPM. Four distinct PET image distributions made of two early time frames (similar to cortical FDG) and two late frames using two amyloid PET dynamic acquisitions of one amyloid positive and one amyloid negative participants were investigated. For the investigated four PET frames, the biggest impact on the uncertainty was observed between registration software packages (up to 10-fold difference in precision) followed by the reconstruction parameters. On average, the lowest uncertainty for different PET frames and brain regions was observed with SPM and two iterations of fully quantitative image reconstruction. The observed uncertainty for the varying PET count-level (from 5% to 60%) was slightly lower than for the reconstruction parameters. We also observed that the registration uncertainty in quantitative PET analysis depends on amyloid status of the considered PET frames, with increased uncertainty (up to three times) when using post-reconstruction partial volume correction. This analysis is applicable for PET data obtained from either PET/MR or PET/CT scanners.
准确的区域性脑定量 PET 测量，特别是在使用部分容积校正时，依赖于 PET 和 MR 图像之间的稳健图像配准。我们在这里认为，由于与结构 MR 图像相比，PET 图像的分辨率较低且噪声较高，因此 MR-PET 图像配准的精度（即不确定性）主要取决于配准实现和 PET 图像的质量。我们提出了一种专门的不确定性分析方法，用于量化 MR-PET 配准的精度，该方法围绕着对 PET 列表模式事件进行引导重采样，以生成具有不同噪声（计数）水平的多个 PET 图像实现。研究了 PET 图像重建参数（如使用衰减和散射校正以及不同迭代次数）对 MR-PET 配准精度和准确性的影响。此外，还考虑了四个具有默认刚性模态间图像配准设置的软件包的性能：NiftyReg、Vinci、FSL 和 SPM。使用两种淀粉样蛋白 PET 动态采集的两个早期时间帧（类似于皮质 FDG）和两个晚期帧，对由两种不同的 PET 图像分布进行了研究，这两种分布使用了一个淀粉样蛋白阳性和一个淀粉样蛋白阴性参与者的采集。对于所研究的四个 PET 帧，观察到注册软件包之间的不确定性影响最大（精度差异高达 10 倍），其次是重建参数。平均而言，对于不同的 PET 帧和脑区，SPM 和完全定量图像重建的两次迭代观察到的不确定性最低。对于变化的 PET 计数水平（从 5%到 60%）观察到的不确定性略低于重建参数。我们还观察到，在定量 PET 分析中，注册不确定性取决于所考虑的 PET 帧的淀粉样蛋白状态，使用后重建部分容积校正时，不确定性增加（高达三倍）。该分析适用于从 PET/MR 或 PET/CT 扫描仪获得的 PET 数据。
