Forecasting Future Anatomies: Longitudinal Brain Mri-to-Mri Prediction

TL;DR

This paper demonstrates that deep learning models can accurately predict future brain MRI scans from baseline images, enabling individualized neurodegenerative disease prognosis with high fidelity and cross-cohort robustness.

Contribution

It introduces and evaluates five deep learning architectures for longitudinal MRI prediction, advancing personalized neuroimaging analysis.

Findings

High-fidelity MRI predictions achieved by all models.

Models generalize well across different datasets.

Deep learning enables voxel-level brain MRI forecasting.

Abstract

Predicting future brain state from a baseline magnetic resonance image (MRI) is a central challenge in neuroimaging and has important implications for studying neurodegenerative diseases such as Alzheimer's disease (AD). Most existing approaches predict future cognitive scores or clinical outcomes, such as conversion from mild cognitive impairment to dementia. Instead, here we investigate longitudinal MRI image-to-image prediction that forecasts a participant's entire brain MRI several years into the future, intrinsically modeling complex, spatially distributed neurodegenerative patterns. We implement and evaluate five deep learning architectures (UNet, U2-Net, UNETR, Time-Embedding UNet, and ODE-UNet) on two longitudinal cohorts (ADNI and AIBL). Predicted follow-up MRIs are directly compared with the actual follow-up scans using metrics that capture global similarity and local differences. The best performing models achieve high-fidelity predictions, and all models generalize well to an independent external dataset, demonstrating robust cross-cohort performance. Our results indicate that deep learning can reliably predict participant-specific brain MRI at the voxel level, offering new opportunities for individualized prognosis.