Efficient Atlas Generation for Medical Imaging Via Groupwise Latent Diffusion Models

Groupwise registration has been an essential technique for addressing the growing demands for batch processing of medical images collected from various sites or time points. It is vital to select or construct an atlas for a given group when performing groupwise registration. This paper proposes a novel atlas generation framework with a latent diffusion model that generates an atlas from noise by leveraging the latent vectors of all subjects in the group. By incorporating the attention mechanism, the model accommodates groups of arbitrary sizes and enhances scalability during the atlas generation. A minimum group construction module is introduced to create minimal group units and their corresponding atlases for training the diffusion model. Experimental results on brain MRI slices demonstrate that the proposed method can generate comparable or higher quality atlases compared to baselines while significantly reducing runtime. To conclude, the method effectively balances image quality, registration actuary, atlas unbiasedness, and computational efficiency.