Diffusion models have achieved significant success in both the natural image and medical image domains, encompassing a wide range of applications. Previous investigations in medical images have often been constrained to specific anatomical regions, particular applications, and limited datasets, resulting in isolated diffusion models. This paper introduces a diffusion-based foundation model to address a diverse range of medical image tasks, namely MedDiff-FM. MedDiff-FM leverages 3D CT images from multiple publicly available datasets, covering anatomical regions from head to abdomen, to pre-train a diffusion foundation model, and explores the capabilities of the diffusion foundation model across a variety of application scenarios. The diffusion foundation model handles multi-level image processing both at the image-level and patch-level, and utilizes position embedding to establish multi-level spatial relationships as well as anatomical structures and region classes to control certain anatomical regions. MedDiff-FM manages several downstream tasks seamlessly, including image denoising, anomaly detection, and image synthesis. MedDiff-FM is also capable of performing lesion generation and lesion inpainting by rapidly fine-tuning the diffusion foundation model using ControlNet with task-specific conditions. Experimental results demonstrate the effectiveness of MedDiff-FM in addressing diverse downstream medical image tasks.
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