Ultra-Low-Dose 18F-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs
Ultra-Low-Dose 18F-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs
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DOI：
10.1148/radiol.2018180940
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发表时间：
2019-03-01
期刊：
影响因子：
19.7
通讯作者：
Zaharchuk, Greg
中科院分区：
文献类型：
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作者：
Chen, Kevin T.;Gong, Enhao;Zaharchuk, Greg
目的：通过使用深度学习方法，在不牺牲诊断质量的情况下降低淀粉样蛋白PET/MRI的放射性示踪剂要求。材料和方法：来自39名患者的40个数据集（平均年龄+/-标准差[SD]，67岁+/- 8），包括16例男性患者和23例女性患者（平均年龄分别为66岁± 6岁和68岁± 9岁），同时接受淀粉样蛋白（氟18 [F-18]-florbetaben）PET/MRI检查，并对2016年3月至2017年10月进行回顾性分析。随机选择原始列表模式PET数据的百分之一，以模拟低剂量（1%）采集。卷积神经网络采用低剂量PET和多个MR图像（PET加MR模型）或仅采用低剂量PET（仅PET）作为输入来预测全剂量PET图像。评价合成图像的质量，同时Bland-Altman图评估图像类型之间的区域标准摄取值比（SUVR）的一致性。两名阅片员对图像质量进行5分制评分（5 =优秀），并确定淀粉样蛋白状态（阳性或阴性）。进行统计分析，以评估图像质量指标和读者的协议的差异，并确定置信区间（CI）为阅读result.Results：合成图像（特别是从PET-加-MR模型）的所有质量指标相比，低剂量图像显示出显着的改善。所有PET + MR图像的评分均为3分或更高，评分大于3分的图像比例与全剂量图像相似（-10%差异[80个读数中的8个]，95% CI：-15%，-5%）。淀粉样蛋白状态的准确性很高（80个读数中的71个[89%]），与全剂量图像的阅片员内重现性相似（80个读数中的73个[91%]）。PET-plus-MR模型也有最小的平均值和方差SUVR差异，全剂量images.Conclusion：同时采集的MRI和超低剂量PET数据可用于合成全剂量淀粉样蛋白PET图像。(C)RSNA，2018
Purpose: To reduce radiotracer requirements for amyloid PET/MRI without sacrificing diagnostic quality by using deep learning methods.Materials and Methods: Forty data sets from 39 patients (mean age +/- standard deviation [SD], 67 years +/- 8), including 16 male patients and 23 female patients (mean age, 66 years +/- 6 and 68 years +/- 9, respectively), who underwent simultaneous amyloid (fluorine 18 [F-18]-florbetaben) PET/MRI examinations were acquired from March 2016 through October 2017 and retrospectively analyzed. One hundredth of the raw list-mode PET data were randomly chosen to simulate a low-dose (1%) acquisition. Convolutional neural networks were implemented with low-dose PET and multiple MR images (PET-plus-MR model) or with low-dose PET alone (PET-only) as inputs to predict full-dose PET images. Quality of the synthesized images was evaluated while Bland-Altman plots assessed the agreement of regional standard uptake value ratios (SUVRs) between image types. Two readers scored image quality on a five-point scale (5 = excellent) and determined amyloid status (positive or negative). Statistical analyses were carried out to assess the difference of image quality metrics and reader agreement and to determine confidence intervals (CIs) for reading results.Results: The synthesized images (especially from the PET-plus-MR model) showed marked improvement on all quality metrics compared with the low-dose image. All PET-plus-MR images scored 3 or higher, with proportions of images rated greater than 3 similar to those for the full-dose images (-10% difference [eight of 80 readings], 95% CI: -15%, -5%). Accuracy for amyloid status was high (71 of 80 readings [89%]) and similar to intrareader reproducibility of full-dose images (73 of 80 [91%]). The PET-plus-MR model also had the smallest mean and variance for SUVR difference to full-dose images.Conclusion: Simultaneously acquired MRI and ultra-low-dose PET data can be used to synthesize full-dose-like amyloid PET images. (C) RSNA, 2018