OMGAN: One-to-Many Generative Adversarial Network for Diagnosing Orbital Lymphoproliferative Disorders in Incomplete Multi-Parametric MRI

Multi-parametric magnetic resonance imaging (mpMRI) is widely used in the diagnosis of orbital lymphoproliferative disorders (OLPDs) due to its non-invasive nature. However, in clinical practice, contrast-enhanced T1-weighted (T1C) images are often unavailable due to contraindications to gadolinium-based contrast agents, meanwhile T2-weighted (T2w) images may also be omitted for time-sensitive diagnoses, making it a challenge to generate these images from T1-weighted (T1 w) image alone for multimodal differential diagnosis. Generative adversarial network (GAN)-based models partially address the issue of missing modalities in medical image analysis; however, they often suffer from unstable generation of missing images and lack integration with subsequent diagnostic tasks. To this end, we propose a One-to-Many Generative Adversarial Network (OMGAN) for diagnosing OLPDs in incomplete mpMRI, consisting of a cross-modal generator and a self-representation module, enabling multimodal diagnosis using pre-contrast images alone within a single model. Specifically, we first design an image-modality fusion module that incorporates trigonometric function coding and mixup augmentation to effectively guide the generation from T1 w to T2w and T1 C within one model. Then, we construct a cross-modal generator with a semantic disambiguation block to synthesize the missing images. Meanwhile, we use a self-representation module with a classification-guided branch to effectively extract task-relevant image features. Finally, multimodal features are fused to accomplish the differential diagnosis of OLPDs in the downstream task. Experiments on internal datasets demonstrated that OMGAN outperforms state-of-the-art GAN-based models, with the area-under-the-curve and accuracy improving by 8.18-14.04% and 12.53-16.39%, respectively. Codes are available at https://github.com/3Iasticheart/OMGAN.