Radiology and Psychiatry Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain.
Molecular Imaging Unit, Centro de Investigaciones Médico-Sanitarias, General Foundation of the University of Málaga, Málaga, Spain.
Med Phys. 2021 May;48(5):2482-2493. doi: 10.1002/mp.14838. Epub 2021 Mar 30.
SimPET (www.sim-pet.org) is a free cloud-based platform for the generation of realistic brain positron emission tomography (PET) data. In this work, we introduce the key features of the platform. In addition, we validate the platform by performing a comparison between simulated healthy brain FDG-PET images and real healthy subject data for three commercial scanners (GE Advance NXi, GE Discovery ST, and Siemens Biograph mCT).
The platform provides a graphical user interface to a set of automatic scripts taking care of the code execution for the phantom generation, simulation (SimSET), and tomographic image reconstruction (STIR). We characterize the performance using activity and attenuation maps derived from PET/CT and MRI data of 25 healthy subjects acquired with a GE Discovery ST. We then use the created maps to generate synthetic data for the GE Discovery ST, the GE Advance NXi, and the Siemens Biograph mCT. The validation was carried out by evaluating Bland-Altman differences between real and simulated images for each scanner. In addition, SPM voxel-wise comparison was performed to highlight regional differences. Examples for amyloid PET and for the generation of ground-truth pathological patients are included.
The platform can be efficiently used for generating realistic simulated FDG-PET images in a reasonable amount of time. The validation showed small differences between SimPET and acquired FDG-PET images, with errors below 10% for 98.09% (GE Discovery ST), 95.09% (GE Advance NXi), and 91.35% (Siemens Biograph mCT) of the voxels. Nevertheless, our SPM analysis showed significant regional differences between the simulated images and real healthy patients, and thus, the use of the platform for converting control subject databases between different scanners requires further investigation.
The presented platform can potentially allow scientists in clinical and research settings to perform MC simulation experiments without the need for high-end hardware or advanced computing knowledge and in a reasonable amount of time.
SimPET（www.sim-pet.org）是一个免费的基于云的平台，用于生成逼真的脑正电子发射断层扫描（PET）数据。在这项工作中，我们介绍了该平台的关键特性。此外，我们通过将模拟的健康大脑 FDG-PET 图像与来自三个商业扫描仪（GE Advance NXi、GE Discovery ST 和 Siemens Biograph mCT）的真实健康受试者数据进行比较来验证该平台。
该平台为一组自动脚本提供了一个图形用户界面，负责执行幻影生成、模拟（SimSET）和断层图像重建（STIR）的代码执行。我们使用从 25 名健康受试者的 PET/CT 和 MRI 数据中获得的活动和衰减图来描述性能，这些数据是使用 GE Discovery ST 采集的。然后，我们使用创建的图谱为 GE Discovery ST、GE Advance NXi 和 Siemens Biograph mCT 生成合成数据。验证是通过评估每个扫描仪的真实和模拟图像之间的 Bland-Altman 差异来进行的。此外，还进行了 SPM 体素级比较，以突出区域差异。包括淀粉样 PET 和生成真实病理患者的示例。
该平台可用于在合理的时间内高效生成逼真的模拟 FDG-PET 图像。验证结果表明，SimPET 与获得的 FDG-PET 图像之间存在较小差异，对于 98.09%（GE Discovery ST）、95.09%（GE Advance NXi）和 91.35%（Siemens Biograph mCT）的体素，误差低于 10%。然而，我们的 SPM 分析表明，模拟图像与真实健康患者之间存在显著的区域差异，因此，在不同扫描仪之间转换对照受试者数据库时，需要进一步研究该平台的使用。
所提出的平台可以潜在地使临床和研究环境中的科学家无需高端硬件或先进的计算知识，在合理的时间内进行 MC 模拟实验。
