ArticleInsights into imaging2022
The role of generative adversarial networks in brain MRI: a scoping review.
Article in Insights into imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Generating Brain MRI with StyleGAN2-ADA: The Effect of the Training Set Size on the Quality of Synthetic Images.Journal of imaging informatics in medicine · 2026Article
- Longitudinal Forecasting of Retinal Structure and Function Using a Multimodal StyleGAN-Based Architecture.Bioengineering (Basel, Switzerland) · 2026Article
- Traumatic brain injury recovery prediction by harmonizing real brain CT and synthetic brain MRI: a pilot study.Brain communications · 2026Article
- WASABI: A Metric for Evaluating Morphometric Plausibility of Synthetic Brain MRIs.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- Laplacian filter attention with style transfer GAN for brain tumor MRI imputation.Scientific reports · 2025Article
- High-resolution conditional MR image synthesis through the PACGAN framework.Scientific reports · 2025Article
- Generative Artificial Intelligence Use in Healthcare: Opportunities for Clinical Excellence and Administrative Efficiency.Journal of medical systems · 2025Article
- Parameter-optimized generative adversarial network framework for synthetic MRI generation: fine-tuning critical variables for enhanced image fidelity.Frontiers in medicine · 2025Article
- Synthetic data in medical imaging within the EHDS: a path forward for ethics, regulation, and standards.Frontiers in digital health · 2025Review
- Comprehensive Review: Machine and Deep Learning in Brain Stroke Diagnosis.Sensors (Basel, Switzerland) · 2024Review
- Deep learning for accelerated and robust MRI reconstruction.Magma (New York, N.Y.) · 2024Review
- Evaluating synthetic neuroimaging data augmentation for automatic brain tumour segmentation with a deep fully-convolutional network.IBRO neuroscience reports · 2024Article
- Uncover This Tech Term: Generative Adversarial Networks.Korean journal of radiology · 2024Article
- Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment.NPJ precision oncology · 2024Review
- Augmenting Blood Test and Periodontal Examination Data with Generative Adversarial Networks for Enhanced Dementia Risk Prediction.Advances in experimental medicine and biology · 2024Article
- Enhancing dementia risk screening with GAN-synthesized periodontal examination and general blood test data.Frontiers in neurology · 2024Article
- A scoping review of magnetic resonance angiography and perfusion image synthesis.Frontiers in dementia · 2024Article
- Article
- Artificial Intelligence-Based Methods for Integrating Local and Global Features for Brain Cancer Imaging: Scoping Review.JMIR medical informatics · 2023Article
- Improving diagnosis and prognosis of lung cancer using vision transformers: a scoping review.BMC medical imaging · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The performance of artificial intelligence (AI) for brain MRI can improve if enough data are made available. Generative adversarial networks (GANs) showed a lot of potential to generate synthetic MRI data that can capture the distribution of real MRI. Besides, GANs are also popular for segmentation, noise removal, and super-resolution of brain MRI images. This scoping review aims to explore how GANs methods are being used on brain MRI data, as reported in the literature. The review describes the different applications of GANs for brain MRI, presents the most commonly used GANs architectures, and summarizes the publicly available brain MRI datasets for advancing the research and development of GANs-based approaches. This review followed the guidelines of PRISMA-ScR to perform the study search and selection. The search was conducted on five popular scientific databases. The screening and selection of studies were performed by two independent reviewers, followed by validation by a third reviewer. Finally, the data were synthesized using a narrative approach. This review included 139 studies out of 789 search results. The most common use case of GANs was the synthesis of brain MRI images for data augmentation. GANs were also used to segment brain tumors and translate healthy images to diseased images or CT to MRI and vice versa. The included studies showed that GANs could enhance the performance of AI methods used on brain MRI imaging data. However, more efforts are needed to transform the GANs-based methods in clinical applications.
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