Intelligent Design of Magnetic Resonance Imaging Metamaterials With an Improved Conditional Generative Adversarial Network

Metamaterials, with their unique electromagnetic properties, offer a promising solution for enhancing the signal‐to‐noise ratio (SNR) in Magnetic Resonance Imaging (MRI). However, their customized design tailored to patient‐specific anatomical features remains a significant challenge. Strict geometric dimensional constraints lead to extreme sparsity within the parameter space, causing traditional inverse design methods to fail in generating valid structures that satisfy physical constraints due to interpolation errors. To address the bottleneck of high simulation costs in customized design, this paper proposes a generative data augmentation strategy. By leveraging an improved CWGAN‐GP to accurately fit high‐Q resonances and bridge data gaps, combined with an inverse network, we successfully achieve the precise design of metamaterials satisfying strict geometric constraints at zero post‐training simulation cost, establishing a novel, general, and efficient paradigm for personalized design. The results indicate that this “Generate‐Augment‐Inverse” closed‐loop strategy not only resolves the issue of interpolation failure under sparse sampling but also provides a generic data‐driven pathway for the efficient and low‐cost customization of personalized medical devices.