Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland.
Department of Radiology, Nippon Medical School, Tokyo, Japan.
PLoS One. 2020 Jun 3;15(6):e0233886. doi: 10.1371/journal.pone.0233886. eCollection 2020.
The purpose of this study was to assess the impact of vendor-provided atlas-based MRAC on FDG PET/MR for the evaluation of Alzheimer's disease (AD) by using simulated images.
We recruited 47 patients, from two institutions, who underwent PET/CT and PET/MR (GE SIGNA) examination for oncological staging. From the PET raw data acquired on PET/MR, two FDG-PET series were generated, using vendor-provided MRAC (atlas-based) and CTAC. The following simulation steps were performed in MNI space: After spatial normalization and smoothing of the PET datasets, we calculated the error map for each patient, PETMRAC/PETCTAC. We multiplied each of these 47 error maps with each of the 203 Alzheimer's Disease Neuroimaging Initiative (ADNI) cases after the identical normalization and smoothing. This resulted in 203*47 = 9541 datasets. To evaluate the probability of AD in each resulting image, a cumulative t-value was calculated automatically using commercially-available software (PMOD PALZ) which has been used in multiple large cohort studies. The diagnostic accuracy for the discrimination of AD and predicting progression from mild cognitive impairment (MCI) to AD were evaluated in simulated images compared with ADNI original images.
The accuracy and specificity for the discrimination of AD-patients from normal controls were not substantially impaired, but sensitivity was slightly impaired in 5 out of 47 datasets (original vs. error; 83.2% [CI 75.0%-89.0%], 83.3% [CI 74.2%-89.8%] and 83.1% [CI 75.6%-88.3%] vs. 82.7% [range 80.4-85.0%], 78.5% [range 72.9-83.3%,] and 86.1% [range 81.4-89.8%]). The accuracy, sensitivity and specificity for predicting progression from MCI to AD during 2-year follow-up was not impaired (original vs. error; 62.5% [CI 53.3%-69.3%], 78.8% [CI 65.4%-88.6%] and 54.0% [CI 47.0%-69.1%] vs. 64.8% [range 61.5-66.7%], 75.7% [range 66.7-81.8%,] and 59.0% [range 50.8-63.5%]). The worst 3 error maps show a tendency towards underestimation of PET scores.
FDG-PET/MR based on atlas-based MR attenuation correction showed similar diagnostic accuracy to the CT-based method for the diagnosis of AD and the prediction of progression of MCI to AD using commercially-available software, although with a minor reduction in sensitivity.
本研究旨在使用模拟图像评估基于供应商提供的图谱的 MRAC 对 FDG PET/MR 评估阿尔茨海默病(AD)的影响。
我们招募了 47 名患者,他们来自两个机构,进行了 PET/CT 和 PET/MR(GE SIGNA)检查以进行肿瘤分期。从在 PET/MR 上获得的 PET 原始数据中,使用供应商提供的 MRAC(基于图谱)和 CTAC 生成了两个 FDG-PET 系列。在 MNI 空间中执行了以下模拟步骤:在对 PET 数据集进行空间归一化和平滑处理后,我们为每个患者计算了误差图,即 PETMRAC/PETCTAC。我们将这 47 个误差图中的每一个与经过相同归一化和平滑处理的 203 个阿尔茨海默病神经影像学倡议(ADNI)病例中的每一个相乘。这产生了 203*47=9541 个数据集。为了评估每个结果图像中 AD 的概率,使用市售软件(PMOD PALZ)自动计算累积 t 值,该软件已在多个大型队列研究中使用。与 ADNI 原始图像相比,在模拟图像中评估了基于图谱的 MRAC 对 AD 和预测从轻度认知障碍(MCI)到 AD 的进展的区分准确性。
在区分 AD 患者与正常对照方面,准确性和特异性没有明显受损,但在 5 个数据集(原始与误差;83.2%[75.0%-89.0%]、83.3%[74.2%-89.8%]和 83.1%[75.6%-88.3%]与 82.7%[范围 80.4%-85.0%]、78.5%[范围 72.9%-83.3%]和 86.1%[范围 81.4%-89.8%])中,敏感性略有受损。在 2 年随访期间预测 MCI 向 AD 的进展的准确性、敏感性和特异性不受影响(原始与误差;62.5%[53.3%-69.3%]、78.8%[65.4%-88.6%]和 54.0%[47.0%-69.1%]与 64.8%[范围 61.5%-66.7%]、75.7%[范围 66.7%-81.8%]和 59.0%[范围 50.8%-63.5%])。最差的 3 个误差图显示出低估 PET 评分的趋势。
基于图谱的 FDG-PET/MR 在使用市售软件进行 AD 诊断和预测 MCI 向 AD 进展方面,与基于 CT 的方法具有相似的诊断准确性,尽管敏感性略有下降。