Creating Artificial Images for Radiology Applications Using Generative Adversarial Networks (GANs) – A Systematic Review

Rationale and Objectives Generative adversarial networks (GANs) are deep learning models aimed at generating fake realistic looking images. These novel models made a great impact on the computer vision field. Our study aims to review the literature on GANs applications in radiology. Materials and Methods This systematic review followed the PRISMA guidelines. Electronic datasets were searched for studies describing applications of GANs in radiology. We included studies published up-to September 2019. Results Data were extracted from 33 studies published between 2017 and 2019. Eighteen studies focused on CT images generation, ten on MRI, three on PET/MRI and PET/CT, one on ultrasound and one on X-ray. Applications in radiology included image reconstruction and denoising for dose and scan time reduction (fourteen studies), data augmentation (six studies), transfer between modalities (eight studies) and image segmentation (five studies). All studies reported that generated images improved the performance of the developed algorithms. Conclusion GANs are increasingly studied for various radiology applications. They enable the creation of new data, which can be used to improve clinical care, education and research. Generative adversarial networks (GANs) are deep learning models aimed at generating fake realistic looking images. These novel models made a great impact on the computer vision field. Our study aims to review the literature on GANs applications in radiology. This systematic review followed the PRISMA guidelines. Electronic datasets were searched for studies describing applications of GANs in radiology. We included studies published up-to September 2019. Data were extracted from 33 studies published between 2017 and 2019. Eighteen studies focused on CT images generation, ten on MRI, three on PET/MRI and PET/CT, one on ultrasound and one on X-ray. Applications in radiology included image reconstruction and denoising for dose and scan time reduction (fourteen studies), data augmentation (six studies), transfer between modalities (eight studies) and image segmentation (five studies). All studies reported that generated images improved the performance of the developed algorithms. GANs are increasingly studied for various radiology applications. They enable the creation of new data, which can be used to improve clinical care, education and research.