Synthetic 18F-FDG PET Image Generation Using a Combination of Biomathematical Modeling and Machine Learning.

ABSTRACT: No previous works have attempted to combine generative adversarial network (GAN) architectures and the biomathematical modeling of positron emission tomography (PET) radiotracer uptake in tumors to generate extra training samples. Here, we developed a novel computational model to produce synthetic 18F-fluorodeoxyglucose (18F-FDG) PET images of solid tumors in different stages of progression and angiogenesis. First, a comprehensive biomathematical model is employed for creating tumor-induced angiogenesis, intravascular and extravascular fluid flow, as well as modeling of the transport phenomena and reaction processes of 18F-FDG in a tumor microenvironment. Then, a deep convolutional GAN (DCGAN) model is employed for producing synthetic PET images using 170 input images of 18F-FDG uptake in

SUBMITTER: Abazari MA 

PROVIDER: S-EPMC9179454 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature