Generative Adversarial Networks in Brain Imaging: A Narrative Review

Area of Science:

• Radiology
• Artificial Intelligence
• Deep Learning

Background:

• Artificial intelligence (AI) is rapidly advancing clinical tasks in radiology, including disease detection, segmentation, and prediction.
• Generative Adversarial Networks (GANs), a deep learning approach, are emerging as a key AI application in medical imaging.
• Brain radiology was an early adopter of GANs, highlighting their potential in neuroradiology.

Purpose of the Study:

• To introduce Generative Adversarial Networks (GANs) in the context of brain imaging.
• To discuss the clinical potential and future applications of GANs in neuroradiology.
• To outline the challenges and pitfalls radiologists should consider when using GANs.

Main Methods:

• This is a narrative review.
• The review synthesizes current literature on GANs in brain radiology.
• Key applications and future directions are discussed.

Main Results:

• GANs offer novel capabilities in neuroradiology, including image-to-image and cross-modality synthesis.
• Applications span image reconstruction, segmentation, synthesis, data augmentation, and disease progression modeling.
• GANs facilitate advanced techniques like brain decoding.

Conclusions:

• GANs represent a transformative technology for brain imaging and radiology.
• Their application in neuroradiology opens new avenues for diagnosis and research.
• Radiologists must be aware of the potential benefits and limitations of GANs.