Bourgeat Pierrick, Villemagne Victor L, Dore Vincent, Brown Belinda, Macaulay S Lance, Martins Ralph, Masters Colin L, Ames David, Ellis Kathryn, Rowe Christopher C, Salvado Olivier, Fripp Jurgen
Preventative Health Flagship, CSIRO Digital Productivity and Services Flagship, Herston, Queensland, Australia.
The Mental Health Research Institute, University of Melbourne, Parkville, Victoria, Australia; Department of Nuclear Medicine and Centre for PET, Austin Health, Heidelberg, Victoria, Australia.
Neurobiol Aging. 2015 Jan;36 Suppl 1:S159-66. doi: 10.1016/j.neurobiolaging.2014.04.033. Epub 2014 Aug 27.
(11)C-Pittsburgh compound B (PiB) is a positron emission tomography (PET) tracer designed to bind to amyloid-β (Aβ) plaques, one of the hallmarks of Alzheimer's disease (AD). The potential of PiB as an early marker of AD led to the increasing use of PiB in clinical research studies and development of several F-18-labeled Aβ radiotracers. Automatic quantification of PiB images requires an accurate parcellation of the brain's gray matter (GM). Typically, this relies on a coregistered magnetic resonance imaging (MRI) to extract the cerebellar GM, compute the standardized uptake value ratio (SUVR), and provide parcellation and segmentation for quantification of regional and global SUVR. However, not all subjects can undergo MRI, in which case, an MR-less method is desirable. In this study, we assess 3 PET-only quantification methods: a mean atlas, an adaptive atlas, and a multi-atlas approaches on a database of 237 subjects having been imaged with both PiB PET and MRI. The PET-only methods were compared against MR-based SUVR quantification and evaluated in terms of correlation, average error, and performance in classifying subjects with low and high Aβ deposition. The mean atlas method suffered from a significant bias between the estimated neocortical SUVR and the PiB status, resulting in an overall error of 5.6% (R(2) = 0.98), compared with the adaptive and multi-atlas approaches that had errors of 3.06% and 2.74%, respectively (R(2) = 0.98), and no significant bias. In classifying PiB-negative from PiB-positive subjects, the mean atlas had 10 misclassified subjects compared with 0 for the adaptive and 1 for the multi-atlas approach. Overall, the adaptive and the multi-atlas approaches performed similarly well against the MR-based quantification and would be a suitable replacements for PiB quantification when no MRI is available.
（11）碳-匹兹堡化合物B（PiB）是一种正电子发射断层扫描（PET）示踪剂，旨在与淀粉样β蛋白（Aβ）斑块结合，Aβ斑块是阿尔茨海默病（AD）的标志性特征之一。PiB作为AD早期标志物的潜力导致其在临床研究中的使用日益增加，并促使了几种F-18标记的Aβ放射性示踪剂的研发。PiB图像的自动定量需要对大脑灰质（GM）进行准确的分割。通常，这依赖于配准的磁共振成像（MRI）来提取小脑GM、计算标准化摄取值比率（SUVR），并提供分割以对区域和整体SUVR进行定量。然而，并非所有受试者都能接受MRI检查，在这种情况下，就需要一种无需MRI的方法。在本研究中，我们在一个包含237名同时接受了PiB PET和MRI成像的受试者的数据库上，评估了3种仅基于PET的定量方法：平均图谱法、自适应图谱法和多图谱法。将仅基于PET的方法与基于MR的SUVR定量进行比较，并在相关性、平均误差以及对低Aβ沉积和高Aβ沉积受试者进行分类的性能方面进行评估。平均图谱法在估计的新皮质SUVR和PiB状态之间存在显著偏差，总体误差为5.6%（R² = 0.98），而自适应图谱法和多图谱法的误差分别为3.06%和2.74%（R² = 0.98），且无显著偏差。在区分PiB阴性和PiB阳性受试者时，平均图谱法有10名受试者误分类，而自适应图谱法为0名，多图谱法为1名。总体而言，自适应图谱法和多图谱法在与基于MR的定量比较中表现同样出色，并且在没有MRI可用时将是PiB定量的合适替代方法。
