Independent brain 18F-FDG PET attenuation correction using a deep learning approach with Generative Adversarial Networks
Independent brain 18F-FDG PET attenuation correction using a deep learning approach with Generative Adversarial Networks
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DOI：
10.1967/s002449911053
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发表时间：
2019-09-01
影响因子：
1.5
通讯作者：
Gatidis, Sergios
中科院分区：
文献类型：
--
作者：
Armanious, Karim;Kuestner, Thomas;Gatidis, Sergios
目的：当没有传输数据或计算机断层摄影（CT）数据可用时，例如在独立的PET扫描仪或PET/磁共振成像（MRI）中，正电子发射断层摄影（PET）数据的衰减校正（AC）构成挑战。在这些情况下，外部成像数据或形态成像数据通常用于生成衰减图。然而，新引入的机器学习方法可以允许从非衰减校正的PET数据（PETNAC）直接估计衰减图。因此，我们的目的是建立和评估一种方法的独立AC的脑氟-18-氟脱氧葡萄糖（F-18-FDG）PET图像仅基于PETNAC使用生成对抗网络（GAN）。研究对象和方法：在对PETNAC的配对训练数据集和来自50名患者的相应头部CT图像进行深度学习GAN框架训练后，从40名验证患者的PETNAC生成伪CT图像，其中20名用于技术验证，20名来自CNS疾病患者的患者用于临床验证。伪CT用于这些验证数据集的后续AC，从而产生独立衰减校正的PET数据。结果如下：目视检查显示，与所有确认数据集中采集的CT图像相比，生成的伪CT图像具有高度相似性，个体解剖细节存在微小差异。定量分析显示，在与参考PET图像相关的独立衰减校正的PET数据中，所有脑区的标准化摄取值（SUV）的最小低估低于5%。颜色编码的误差图显示没有区域偏倚，平均误差仅为+/-0%左右。使用独立衰减校正的PET数据，与参考PET图像相比，在20例神经系统疾病患者中未观察到基于图像的诊断差异。结论：使用所提出的易于实现的深度学习框架，脑F-18-FDG PET的独立AC是可行的，具有高精度。有必要在临床队列中进行进一步评价，以评估该方法的临床性能。
Objective: Attenuation correction (AC) of positron emission tomography (PET) data poses a challenge when no transmission data or computed tomography (CT) data are available, e.g. in stand alone PET scanners or PET/magnetic resonance imaging (MRI). In these cases, external imaging data or morphological imaging data are normally used for the generation of attenuation maps. Newly introduced machine learning methods however may allow for direct estimation of attenuation maps from non attenuation-corrected PET data (PETNAC). Our purpose was thus to establish and evaluate a method for independent AC of brain fluorine-18-fluorodeoxyglucose (F-18-FDG) PET images only based on PETNAC using Generative Adversarial Networks (GAN). Subjects and Methods: After training of the deep learning GAN framework on a paired training dataset of PETNAC and the corresponding CT images of the head from 50 patients, pseudo-CT images were generated from PETNAC of 40 validation patients, of which 20 were used for technical validation and 20 stemming from patients with CNS disorders were used for clinical validation. Pseudo-CT was used for subsequent AC of these validation data sets resulting in independently attenuation-corrected PET data. Results: Visual inspection revealed a high degree of resemblance of generated pseudo-CT images compared to the acquired CT images in all validation data sets, with minor differences in individual anatomical details. Quantitative analyses revealed minimal underestimation below 5% of standardized uptake value (SUV) in all brain regions in independently attenuation-corrected PET data corn-pared to the reference PET images. Color-coded error maps showed no regional bias and only minimal average errors around +/- 0%. Using independently attenuation-corrected PET data, no differences in image-based diagnoses were observed in 20 patients with neurological disorders compared to the reference PET images. Conclusion: Independent AC of brain F-18-FDG PET is feasible with high accuracy using the proposed, easy to implement deep learning framework. Further evaluation in clinical cohorts will be necessary to assess the clinical performance of this method.