Medical imaging analysis faces challenges such as data scarcity, high annotation costs, and privacy concerns. This paper introduces the Medical AI for Synthetic Imaging (MAISI), an innovative approach using the diffusion model to generate synthetic 3D computed tomography (CT) images to address those challenges. MAISI leverages the foundation volume compression network and the latent diffusion model to produce high-resolution CT images (up to a landmark volume dimension of 512 x 512 x 768 ) with flexible volume dimensions and voxel spacing. By incorporating ControlNet, MAISI can process organ segmentation, including 127 anatomical structures, as additional conditions and enables the generation of accurately annotated synthetic images that can be used for various downstream tasks. Our experiment results show that MAISI's capabilities in generating realistic, anatomically accurate images for diverse regions and conditions reveal its promising potential to mitigate challenges using synthetic data.

本研究解决了医学影像分析中的数据稀缺、高标注成本和隐私问题，提出一种基于扩散模型的医学人工智能合成成像（MAISI）新方法。研究表明，MAISI能够生成高分辨率、解剖结构准确的合成CT图像，提供了对各种下游任务的有价值支持，具有显著潜力以缓解现实医学影像中的挑战。