In this work, we introduce Brain Latent Progression (BrLP), a novel
spatiotemporal disease progression model based on latent diffusion. BrLP is
designed to predict the evolution of diseases at the individual level on 3D
brain MRIs. Existing deep generative models developed for this task are
primarily data-driven and face challenges in learning disease progressions.
BrLP addresses these challenges by incorporating prior knowledge from disease
models to enhance the accuracy of predictions. To implement this, we propose to
integrate an auxiliary model that infers volumetric changes in various brain
regions. Additionally, we introduce Latent Average Stabilization (LAS), a novel
technique to improve spatiotemporal consistency of the predicted progression.
BrLP is trained and evaluated on a large dataset comprising 11,730 T1-weighted
brain MRIs from 2,805 subjects, collected from three publicly available,
longitudinal Alzheimer's Disease (AD) studies. In our experiments, we compare
the MRI scans generated by BrLP with the actual follow-up MRIs available from
the subjects, in both cross-sectional and longitudinal settings. BrLP
demonstrates significant improvements over existing methods, with an increase
of 22% in volumetric accuracy across AD-related brain regions and 43% in image
similarity to the ground-truth scans. The ability of BrLP to generate
conditioned 3D scans at the subject level, along with the novelty of
integrating prior knowledge to enhance accuracy, represents a significant
advancement in disease progression modeling, opening new avenues for precision
medicine. The code of BrLP is available at the following link:
this https URL

Brain Latent Progression (BrLP) is a disease progression model that predicts the evolution of diseases at the individual level on 3D brain MRIs. By incorporating prior knowledge from disease models and using Latent Average Stabilization (LAS), BrLP achieves significant improvements in volumetric accuracy and image similarity, thus representing a significant advancement in disease progression modeling for precision medicine.