SimPET—An open online platform for the Monte Carlo simulation of realistic brain PET data. Validation for 18 F‐FDG scans - Paredes‐Pacheco, José; López‐González, Francisco Javier; Silva‐Rodríguez, Jesús; Efthimiou, Nikos; Niñerola‐Baizán, Aida; Ruibal, Álvaro; Roé‐Vellvé, Núria; Aguiar, Pablo (Medical physics (Lancaster), 2021)
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.