Regional SUV quantification in hybrid PET/MR, a comparison of two atlas-based automatic brain segmentation methods
Regional SUV quantification in hybrid PET/MR, a comparison of two atlas-based automatic brain segmentation methods
复制标题
混合 PET/MR 中的区域 SUV 量化，两种基于图谱的自动大脑分割方法的比较
DOI：
10.1186/s13550-020-00648-8
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发表时间：
2020-06-08
期刊：
影响因子：
3.2
通讯作者：
Lan, Xiaoli
中科院分区：
文献类型：
--
作者：
Ruan, Weiwei;Sun, Xun;Lan, Xiaoli
背景脑正电子发射断层扫描（PET）的定量分析依赖于结构分割，这可能是耗时和操作员依赖于手动执行时。以前的自动分割通常将受试者的图像注册到一个图集模板（在这里定义为RSIAT）上进行组分析，这会改变个体的图像，并可能影响区域PET分割。相比之下，我们可以将图谱模板与受试者的图像（RATSI）配准，这会创建一个单独的图谱模板，并且对于PET分割可能更准确。我们分割了20例帕金森病（PD）和8例多系统萎缩（MSA）患者的两个代表性脑区，这些患者在混合正电子发射断层扫描/磁共振成像（PET/MR）中进行。采用Dice系数（DC）和Hausdorff距离（HD）评价分割精度，并以手动分割为参考，比较两种自动分割方法的标准化摄取值（SUV）。结果与RSIAT相比，PD患者RATSI的DC增大，HD减小（P < 0.05），而单因素方差分析（ANOVA）结果显示，两种自动分割方法的SUVmean和SUVmax无显著性差异。此外，RATSI用于比较PD和MSA患者脑代谢模式的区域差异。MSA组小脑节段灰质的SUV均值显著低于PD组（P < 0.05），这与以前的报道一致。结论RATSI算法对尾状核和壳核的自动分割精度较高，可用于PET/MR混合成像中的区域PET分析。
Background Quantitative analysis of brain positron-emission tomography (PET) depends on structural segmentation, which can be time-consuming and operator-dependent when performed manually. Previous automatic segmentation usually registered subjects' images onto an atlas template (defined as RSIAT here) for group analysis, which changed the individuals' images and probably affected regional PET segmentation. In contrast, we could register atlas template to subjects' images (RATSI), which created an individual atlas template and may be more accurate for PET segmentation. We segmented two representative brain areas in twenty Parkinson disease (PD) and eight multiple system atrophy (MSA) patients performed in hybrid positron-emission tomography/magnetic resonance imaging (PET/MR). The segmentation accuracy was evaluated using the Dice coefficient (DC) and Hausdorff distance (HD), and the standardized uptake value (SUV) measurements of these two automatic segmentation methods were compared, using manual segmentation as a reference. Results The DC of RATSI increased, and the HD decreased significantly (P < 0.05) compared with the RSIAT in PD, while the results of one-way analysis of variance (ANOVA) found no significant differences in the SUVmean and SUVmax among the two automatic and the manual segmentation methods. Further, RATSI was used to compare regional differences in cerebral metabolism pattern between PD and MSA patients. The SUVmean in the segmented cerebellar gray matter for the MSA group was significantly lower compared with the PD group (P < 0.05), which is consistent with previous reports. Conclusion The RATSI was more accurate for the caudate nucleus and putamen automatic segmentation and can be used for regional PET analysis in hybrid PET/MR.