# A Unified Hyper-GAN Model for Unpaired Multi-contrast MR Image Translation Source: [A Unified Hyper-GAN Model for Unpaired Multi-contrast MR Image Translation]() Collection index: [llms.txt]() Data updated: 2026-01-04T05:07:20.992Z Authors: Heran Yang, Jian Sun, Liwei Yang, Zongben Xu Published: 2021-07-26T03:49:21.000Z ## Abstract Cross-contrast image translation is an important task for completing missing contrasts in clinical diagnosis. However, most existing methods learn separate translator for each pair of contrasts, which is inefficient due to many possible contrast pairs in real scenarios. In this work, we propose a unified Hyper-GAN model for effectively and efficiently translating between different contrast pairs. Hyper-GAN consists of a pair of hyper-encoder and hyper-decoder to first map from the source contrast to a common feature space, and then further map to the target contrast image. To facilitate the translation between different contrast pairs, contrast-modulators are designed to tune the hyper-encoder and hyper-decoder adaptive to different contrasts. We also design a common space loss to enforce that multi-contrast images of a subject share a common feature space, implicitly modeling the shared underlying anatomical structures. Experiments on two datasets of IXI and BraTS 2019 show that our Hyper-GAN achieves state-of-the-art results in both accuracy and efficiency, e.g., improving more than 1.47 and 1.09 dB in PSNR on two datasets with less than half the amount of parameters. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Cross-contrast MR image translation on IXI (test)](): Cross-contrast MR image translation; IXI (test); MAE: 0.0133. - [Cross-contrast MR image translation on BraTS 2019 (test)](): Cross-contrast MR image translation; BraTS 2019 (test); MAE: 0.0067. ## Original sources and implementation [Original paper]() [PDF]()