# TADM: Temporally-Aware Diffusion Model for Neurodegenerative Progression on Brain MRI arXiv: https://arxiv.org/abs/2406.12411 ยท HTML: https://mlsanity.com/paper/2406.12411/ ## Key facts - Cited by 18 papers in the map (data through 14 Oct 2026). - Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. - Builds on 8 of its 15 references that are in the map. - First posted on arXiv 18 Jun 2024. ## Placed in - Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/) ## Builds on (most cited first, 8 of 8) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models](https://mlsanity.com/paper/2104.14951/) (2021) - [Diffusion Deformable Model for 4D Temporal Medical Image Generation](https://mlsanity.com/paper/2206.13295/) (2022) - [DiffuseMorph: Unsupervised Deformable Image Registration Using Diffusion Model](https://mlsanity.com/paper/2112.05149/) (2021) - [SADM: Sequence-Aware Diffusion Model for Longitudinal Medical Image Generation](https://mlsanity.com/paper/2212.08228/) (2022) - [Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models](https://mlsanity.com/paper/2111.14923/) (2021) - [Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression](https://mlsanity.com/paper/1907.02787/) (2019) ## Built on by (most cited first, 18 of 18) - [Brain Latent Progression: Individual-based Spatiotemporal Disease Progression on 3D Brain MRIs via Latent Diffusion](https://mlsanity.com/paper/2502.08560/) (2025) - [Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation](https://mlsanity.com/paper/2508.09177/) (2025) - [Learning Patient-Specific Disease Dynamics with Latent Flow Matching for Longitudinal Imaging Generation](https://mlsanity.com/paper/2512.09185/) (2025) - [Temporal Flow Matching for Learning Spatio-Temporal Trajectories in 4D Longitudinal Medical Imaging](https://mlsanity.com/paper/2508.21580/) (2025) - [FlowLet: Conditional 3D Brain MRI Synthesis using Wavelet Flow Matching](https://mlsanity.com/paper/2601.05212/) (2026) - [TimeFlow: Temporal Conditioning for Longitudinal Brain MRI Registration and Aging Analysis](https://mlsanity.com/paper/2501.08667/) (2025) - [Temporally-Aware Diffusion Model for Brain Progression Modelling with Bidirectional Temporal Regularisation](https://mlsanity.com/paper/2509.03141/) (2025) - [Mechanistic Learning with Guided Diffusion Models to Predict Spatio-Temporal Brain Tumor Growth](https://mlsanity.com/paper/2509.09610/) (2025) - [Anatomically Guided Latent Diffusion for Brain MRI Progression Modeling](https://mlsanity.com/paper/2601.14584/) (2026) - [Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis](https://mlsanity.com/paper/2506.11753/) (2025) - [Integrating Anatomical Priors into a Causal Diffusion Model](https://mlsanity.com/paper/2509.09054/) (2025) - [CRONOS: Continuous Time Reconstruction for 4D Medical Longitudinal Series](https://mlsanity.com/paper/2512.16577/) (2025) - [4DLoG: Generative Modeling of Neurodegenerative Brain Anatomy with 4D Longitudinal Diffusion Model](https://mlsanity.com/paper/2604.22700/) (2026) - [Align-cDAE: Alzheimer's Disease Progression Modeling with Attention-Aligned Conditional Diffusion Auto-Encoder](https://mlsanity.com/paper/2603.01552/) (2026) - [Longitudinal NSCLC Treatment Progression via Multimodal Generative Models](https://mlsanity.com/paper/2603.06147/) (2026) - [Reinforcing the Weakest Links: Modernizing SIENA with Targeted Deep Learning Integration](https://mlsanity.com/paper/2603.12951/) (2026) - [Diagnostic-Guided Longitudinal Modeling for Forecasting Retinal Atrophy Progression](https://mlsanity.com/paper/2604.16955/) (2026) - [Data-driven techniques for translational neuroscience and personalized neuro-health](https://mlsanity.com/paper/2608.13749/) (2026) ## Abstract (arXiv) 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.