  Generating realistic images to accurately predict changes in the structure of
brain MRI is a crucial tool for clinicians. Such applications help assess
patients' outcomes and analyze how diseases progress at the individual level.
However, existing methods for this task present some limitations. Some
approaches attempt to model the distribution of MRI scans directly by
conditioning the model on patients' ages, but they fail to explicitly capture
the relationship between structural changes in the brain and time intervals,
especially on age-unbalanced datasets. Other approaches simply rely on
interpolation between scans, which limits their clinical application as they do
not predict future MRIs. To address these challenges, we propose a
Temporally-Aware Diffusion Model (TADM), which introduces a novel approach to
accurately infer progression in brain MRIs. TADM learns the distribution of
structural changes in terms of intensity differences between scans and combines
the prediction of these changes with the initial baseline scans to generate
future MRIs. Furthermore, during training, we propose to leverage a pre-trained
Brain-Age Estimator (BAE) to refine the model's training process, enhancing its
ability to produce accurate MRIs that match the expected age gap between
baseline and generated scans. Our assessment, conducted on the OASIS-3 dataset,
uses similarity metrics and region sizes computed by comparing predicted and
real follow-up scans on 3 relevant brain regions. TADM achieves large
improvements over existing approaches, with an average decrease of 24% in
region size error and an improvement of 4% in similarity metrics. These
evaluations demonstrate the improvement of our model in mimicking temporal
brain neurodegenerative progression compared to existing methods. Our approach
will benefit applications, such as predicting patient outcomes or improving
treatments for patients.
