General Diffusion Transformer for Arbitrary Medical Image Translation

Medical image translation plays a crucial role in assisting clinical diagnosis by enabling cross-modal synthesis (e.g., Computed Tomography to Magnetic Resonance Imaging) and super-resolution, effectively addressing clinical challenges such as radiation exposure, prolonged scan times, and allergic reactions to contrast agents. However, existing approaches primarily focus on developing specialized models for specific tasks, limiting their adaptability across different applications. Developing a general model capable of handling arbitrary medical image translation tasks not only enhances cross-domain generalization but also aligns with the broader trend of artificial intelligence evolving from specialized to general-purpose solutions. Achieving such one-for-all model, however, presents three key challenges: (1) varying task complexity, (2) modality discrepancies, and (3) structural variations across anatomical regions within the same modality. To tackle the first challenge, we utilize an advanced diffusion-based training paradigm to endow the denoising model with extensive pattern coverage capabilities, thereby handling tasks of varying difficulty levels. Subsequently, a General Diffusion Transformer incorporating a Fuzzy Mixture-of-Experts (FMoE) module and an Entropy-guided Attention Soft Prompt (EASP) module is proposed. The FMoE module, equipped with nonlinear modeling capabilities, is designed to address modality discrepancies, while the EASP module is employed to enhance the model's perception of structural variations in images. Extensive qualitative and quantitative experiments demonstrate the effectiveness of the proposed model in the arbitrary medical image translation task.