A Unified Hyper-GAN Model for Unpaired Multi-contrast MR Image Translation
This addresses the problem of completing missing contrasts for clinical diagnosis by providing a more efficient and accurate method, though it is incremental as it builds on existing GAN-based translation approaches.
The paper tackles the inefficiency of learning separate translators for each contrast pair in multi-contrast MR image translation by proposing a unified Hyper-GAN model, which achieves state-of-the-art results with improvements of over 1.47 and 1.09 dB in PSNR on two datasets while using less than half the parameters.
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.