Deep Generative Adversarial Networks: Applications in Musculoskeletal Imaging

Area of Science:

• Radiology
• Medical Imaging
• Deep Learning

Background:

• Deep learning enhances diagnostic potential in musculoskeletal radiology.
• Generative adversarial networks (GANs) are deep neural networks capable of generating realistic images.
• GANs offer potential for faster imaging across multiple modalities and contrasts.

Purpose of the Study:

• To review key Generative adversarial network (GAN) architectures and their technical background.
• To highlight current research trends and challenges in applying GANs to musculoskeletal imaging.
• To provide an overview of issues impacting clinical applicability and evaluation.

Main Methods:

• Review of Generative adversarial network (GAN) architectures.
• Analysis of key research trends including image reconstruction, synthesis, enhancement, and segmentation.
• Discussion of clinical applicability challenges and evaluation metrics.

Main Results:

• Key GAN architectures and technical aspects are introduced.
• Research trends focus on high-resolution MRI reconstruction, cross-modality synthesis, image enhancement, and segmentation.
• Clinical validation remains a challenge, with limitations in conventional performance metrics and expert evaluation.

Conclusions:

• Generative adversarial networks (GANs) show significant potential to improve musculoskeletal imaging.
• Further clinical validation is necessary to fully realize the benefits of GANs in radiology.
• GANs could enhance diagnostic capabilities and efficiency in musculoskeletal radiology for adults and pediatrics.