Shan Yi, Yan Shao-Zhen, Wang Zhe, Cui Bi-Xiao, Yang Hong-Wei, Yuan Jian-Min, Yin Ya-Yan, Shi Feng, Lu Jie
Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, #45 Changchunjie, Xicheng District, Beijing, 100053, China.
Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, 100053, China.
EJNMMI Res. 2023 Sep 5;13(1):79. doi: 10.1186/s13550-023-01028-8.
Accurate analysis of quantitative PET data plays a crucial role in studying small, specific brain structures. The integration of PET and MRI through an integrated PET/MR system presents an opportunity to leverage the benefits of precisely aligned structural MRI and molecular PET images in both spatial and temporal dimensions. However, in many clinical workflows, PET studies are often performed without the aid of individually matched structural MRI scans, primarily for the sake of convenience in the data collection and brain segmentation possesses. Currently, two commonly employed segmentation strategies for brain PET analysis are distinguished: methods with or without MRI registration and methods employing either atlas-based or individual-based algorithms. Moreover, the development of artificial intelligence (AI)-assisted methods for predicting brain segmentation holds promise but requires further validation of their efficiency and accuracy for clinical applications. This study aims to compare and evaluate the correlations, consistencies, and differences among the above-mentioned brain segmentation strategies in quantification of brain metabolism in F-FDG PET/MR analysis.
Strong correlations were observed among all methods (r = 0.932 to 0.999, P < 0.001). The variances attributable to subject and brain region were higher than those caused by segmentation methods (P < 0.001). However, intraclass correlation coefficient (ICC)s between methods with or without MRI registration ranged from 0.924 to 0.975, while ICCs between methods with atlas- or individual-based algorithms ranged from 0.741 to 0.879. Brain regions exhibiting significant standardized uptake values (SUV) differences due to segmentation methods were the basal ganglia nuclei (maximum to 11.50 ± 4.67%), and various cerebral cortexes in temporal and occipital regions (maximum to 18.03 ± 5.52%). The AI-based method demonstrated high correlation (r = 0.998 and 0.999, P < 0.001) and ICC (0.998 and 0.997) with FreeSurfer, substantially reducing the time from 8.13 h to 57 s on per subject.
Different segmentation methods may have impact on the calculation of brain metabolism in basal ganglia nuclei and specific cerebral cortexes. The AI-based approach offers improved efficiency and is recommended for its enhanced performance.
正电子发射断层扫描（PET）定量数据的准确分析在研究小型特定脑结构中起着关键作用。通过集成式PET/MR系统将PET与MRI相结合，为在空间和时间维度上利用精确对齐的结构MRI和分子PET图像的优势提供了契机。然而，在许多临床工作流程中，PET研究通常在没有单独匹配的结构MRI扫描辅助的情况下进行，主要是为了数据收集的便利性以及脑部分割的需求。目前，脑PET分析中常用的两种分割策略有所不同：有或没有MRI配准的方法，以及采用基于图谱或基于个体的算法的方法。此外，用于预测脑部分割的人工智能（AI）辅助方法的开发具有前景，但需要进一步验证其在临床应用中的效率和准确性。本研究旨在比较和评估上述脑部分割策略在F-FDG PET/MR分析中脑代谢定量方面的相关性、一致性和差异。
所有方法之间均观察到强相关性（r = 0.932至0.999，P < 0.001）。受试者和脑区导致的方差高于分割方法引起的方差（P < 0.001）。然而，有或没有MRI配准的方法之间的组内相关系数（ICC）范围为0.924至0.975，而基于图谱或基于个体的算法的方法之间的ICC范围为0.741至0.879。由于分割方法而表现出显著标准化摄取值（SUV）差异的脑区是基底神经节核（最大差异达11.50±4.67%），以及颞叶和枕叶的各个脑皮质区域（最大差异达18.03±5.52%）。基于AI的方法与FreeSurfer显示出高度相关性（r = 0.998和0.999，P < 0.001）和ICC（0.998和0.997），将每个受试者的时间从8.13小时大幅缩短至57秒。
不同的分割方法可能会对基底神经节核和特定脑皮质区域的脑代谢计算产生影响。基于AI的方法提高了效率，因其性能提升而被推荐。
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