Parameter-optimized generative adversarial network framework for synthetic MRI generation: fine-tuning critical variables for enhanced image fidelity
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.
Authors
- Parvathaneni Naga Srinivasu (ORCID: https://orcid.org/0000-0001-9247-9132)
- Naveenkumar Anbalagan
- Muhammad Fazal Ijaz (ORCID: https://orcid.org/0000-0002-9381-5801)
- J. Isabelle Choi (ORCID: https://orcid.org/0009-0005-2534-5886)
- Anto Lourdu Xavier Raj Arockia Selvarathinam
Institutions
- Gachon University (KR)
- Torrens University Australia (AU)
- Amrita Vishwa Vidyapeetham (IN)
- Grand Valley State University (US)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2026-02-03
- DOI
- https://doi.org/10.3389/fmed.2025.1731370
- Primary Topic
- Generative Adversarial Networks and Image Synthesis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Research Foundation
- National Research Foundation of Korea
- Ministry of Science and ICT, South Korea