Uncertainty analysis of MR-PET image registration for precision neuro-PET imaging.
Uncertainty analysis of MR-PET image registration for precision neuro-PET imaging.
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DOI：
10.1016/j.neuroimage.2021.117821
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发表时间：
2021-05-15
期刊：
影响因子：
5.7
通讯作者：
Barkhof F
中科院分区：
文献类型：
--
作者：
Markiewicz PJ;Matthews JC;Ashburner J;Cash DM;Thomas DL;De Vita E;Barnes A;Cardoso MJ;Modat M;Brown R;Thielemans K;da Costa-Luis C;Lopes Alves I;Gispert JD;Schmidt ME;Marsden P;Hammers A;Ourselin S;Barkhof F
MR-PET配准不确定性分析的新方法和软件。配准软件对MR-PET配准精度的影响最大，其次是重建参数（即，迭代、平滑）和PET计数水平。PVC可以显著改善PET信号，但由于它依赖于精确的MR-PET配准，它也增加了PET信号的可变性，因此在使用时应小心。准确的局部脑定量PET测量，特别是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，MR-PET图像配准的精度，因此，不确定性，主要是由配准实施和PET图像的质量，由于其较低的分辨率和较高的噪声相比，结构MR图像。我们提出了一个专门的不确定性分析，用于量化MR-PET配准的精度，围绕PET列表模式事件的引导重新定位，以生成具有不同噪声（计数）水平的多个PET图像实现。PET图像重建参数的影响，如衰减和散射校正的使用和不同的迭代次数，对MR-PET配准的精度和准确性进行了研究。此外，还考虑了四个软件包的性能及其刚性模态间图像配准的默认设置：NiftyReg、芬奇、FSL和SPM。研究了四种不同的PET图像分布，包括两个早期时间帧（类似于皮质FDG）和两个晚期帧，使用两个淀粉样蛋白PET动态采集一个淀粉样蛋白阳性和一个淀粉样蛋白阴性参与者。对于研究的四个PET帧，在配准软件包之间观察到对不确定性的最大影响（精度差异高达10倍），其次是重建参数。平均而言，SPM和两次完全定量图像重建迭代观察到不同PET帧和脑区域的最低不确定性。观察到的不同PET计数水平（从5%到60%）的不确定性略低于重建参数。我们还观察到，定量PET分析中的配准不确定性取决于所考虑的PET帧的淀粉样蛋白状态，当使用重建后部分体积校正时，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。
Novel methodology and software for MR-PET registration uncertainty analysis. Registration software had the biggest effect on MR-PET registration precision, followed by reconstruction parameters (i.e., iterations, smoothing) and PET count level. PVC can significantly improve the PET signal, but since it relies on precise MR-PET registration, it also increases PET signal variability and hence care should be taken when using it. 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.
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影响因子：
3.5
作者：
BARRETT, HH;WILSON, DW;TSUI, BMW
通讯作者：
TSUI, BMW
影响因子：
3.5
作者：
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作者：
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通讯作者：
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作者：
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通讯作者：
Reilhac, Anthonin