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

Introduction: The availability of large-scale medical imaging datasets is often constrained by privacy regulations, high acquisition costs, and ethical concerns. Synthetic medical image generation using generative adversarial networks (GANs) offers a promising solution to overcome these limitations. This study investigates the effectiveness of a Parameter-Optimized Generative Adversarial Network (POP-GAN) and compares its performance with state-of-the-art architectures, including StyleGAN2, multi-stream GAN (mustGAN), and Conditional GAN (cGAN), for realistic MRI image synthesis. Methods: The proposed framework integrates progressive growing strategies with optimized hyperparameters, including a batch size of 256, learning rate of 1 × 10 Results: POP-GAN demonstrated a 27% reduction in MSE compared with the baseline model (from 6.58 × 10 Discussion: The results demonstrate that parameter optimization and progressive training substantially enhance synthetic MRI quality. POP-GAN provides a balanced trade-off between reconstruction accuracy, perceptual realism, and clinical relevance, supporting its potential for privacy-preserving dataset augmentation and robust medical imaging research.