Recent Advances in Generative Models for Synthetic Brain MRI Image Generation

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Computer Vision

Background:

• Limited training data hinders the application of artificial intelligence (AI) in Magnetic Resonance Imaging (MRI) analysis.
• Realistic synthetic MRI images are crucial for developing and validating AI algorithms.
• Generative models offer a promising approach to synthesize high-fidelity medical images.

Purpose of the Study:

• To review recent advancements in AI-based generative models for synthetic brain MRI image generation.
• To identify and discuss popular generative models used in this field.
• To provide an overview of the current state-of-the-art in synthetic MRI data synthesis.

Main Methods:

• A comprehensive literature search was conducted on studies published within the last three years.
• Relevant articles were identified and analyzed through a narrative review.
• Key AI generative models, including Generative Adversarial Networks (GANs), diffusion models, Variational Autoencoders (VAEs), and transformers, were examined.

Main Results:

• Generative models, particularly GANs and diffusion models, show significant progress in generating realistic synthetic brain MRI images.
• Transformers are emerging as powerful tools for sequence modeling in image synthesis.
• The reviewed studies highlight the potential of these models to augment limited real-world MRI datasets.

Conclusions:

• AI-driven generative models are effective in producing synthetic brain MRI images, mitigating data scarcity issues.
• Continued research into advanced generative architectures will further enhance the quality and utility of synthetic MRI data.
• Synthetic MRI data holds great promise for improving AI-powered medical image analysis and diagnostics.