  Human organs constantly undergo anatomical changes due to a complex mix of
short-term (e.g., heartbeat) and long-term (e.g., aging) factors. Evidently,
prior knowledge of these factors will be beneficial when modeling their future
state, i.e., via image generation. However, most of the medical image
generation tasks only rely on the input from a single image, thus ignoring the
sequential dependency even when longitudinal data is available. Sequence-aware
deep generative models, where model input is a sequence of ordered and
timestamped images, are still underexplored in the medical imaging domain that
is featured by several unique challenges: 1) Sequences with various lengths; 2)
Missing data or frame, and 3) High dimensionality. To this end, we propose a
sequence-aware diffusion model (SADM) for the generation of longitudinal
medical images. Recently, diffusion models have shown promising results in
high-fidelity image generation. Our method extends this new technique by
introducing a sequence-aware transformer as the conditional module in a
diffusion model. The novel design enables learning longitudinal dependency even
with missing data during training and allows autoregressive generation of a
sequence of images during inference. Our extensive experiments on 3D
longitudinal medical images demonstrate the effectiveness of SADM compared with
baselines and alternative methods. The code is available at
https://github.com/ubc-tea/SADM-Longitudinal-Medical-Image-Generation.
