ProgFormer: Hierarchical Voxel Diffusion Transformer for Longitudinal Brain MRI Prediction

2026-07-29

AI Summary

Existing longitudinal brain MRI prediction methods struggle to simultaneously preserve global structural stability and fine-grained details of local pathological changes. To address this, this work proposes a hierarchical voxel diffusion Transformer architecture featuring dual pathways: a coarse path models the overall brain structure and temporal context, while a fine path refines voxel-level local details using the coarse representation as a spatiotemporal anchor. The model jointly estimates a velocity field in voxel space and employs Euler integration for end-to-end future MRI synthesis. This approach introduces, for the first time, a dual-path hierarchical mechanism that directly models longitudinal dynamics in voxel space, circumventing reconstruction losses inherent in latent-space methods. By integrating conditional flow matching, it co-optimizes global consistency and local sensitivity. Comprehensive experiments on ADNI, AIBL, and OASIS benchmarks demonstrate significant improvements over state-of-the-art methods in both pairwise and trajectory-based evaluations.

Abstract

Predicting future structural MRI of a brain is challenging because longitudinal changes are often subtle and confined to specific anatomical regions, while most subject-specific brain structure remains stable over time. An effective model should therefore preserve global brain structural consistency while remaining sensitive to fine-grained disease progression. Existing latent-space-based methods improve computational efficiency, but suffer from information loss during their compression-reconstruction procedure. In contrast, direct voxel-space methods avoid latent reconstruction but commonly use a unified prediction pathway to model brain structure and progression-related changes. Subtle local changes may therefore be overshadowed by the dominant stable brain structure. To address these challenges, we propose ProgFormer, a hierarchical voxel-space Diffusion Transformer for longitudinal brain MRI prediction. ProgFormer uses a coarse pathway to perform the primary volumetric prediction from 3D patch tokens. This pathway models overall brain structure and longitudinal context. The fine pathway then uses the coarse representations as spatio-temporal grounding for voxel-level refinement within individual patches. The two pathways jointly estimate a velocity field directly in voxel space through conditional flow matching, enabling end-to-end prediction without a separately learned image autoencoder. The predicted future scan is then generated from Gaussian noise by integrating the estimated velocity field over a sequence of Euler steps. Extensive experimental results on three widely used benchmarks, ADNI, AIBL, and OASIS, under both pairwise and trajectory settings demonstrate favourable performance compared against several state-of-the-art methods.

Problem

• longitudinal brain MRI prediction
• subtle anatomical changes
• structural consistency
• disease progression
• voxel-space modeling

Innovation

• Hierarchical Diffusion Transformer
• Voxel-space Modeling
• Conditional Flow Matching
• Longitudinal MRI Prediction
• Dual-path Architecture