Parameter-optimized generative adversarial network framework for synthetic MRI generation: fine-tuning critical variables for enhanced image fidelity

Key Points

• Synthetic MRI quality improves with optimized parameters, showing significant advancements in image fidelity.
• The parameter-optimized generative adversarial network achieved a balance between reconstruction accuracy and perceptual realism.
• Observational analysis demonstrates the framework's potential for privacy-preserving dataset augmentation.
• This approach may support robust applications in medical imaging, highlighting a need for further validation.

Abstract