Generative Modeling of Neurodegenerative Brain Anatomy with 4D Longitudinal Diffusion Model

• URL: http://arxiv.org/abs/2604.22700v1
• Date: Fri, 24 Apr 2026 16:33:48 GMT
• Title: Generative Modeling of Neurodegenerative Brain Anatomy with 4D Longitudinal Diffusion Model
• Abstract summary: Most longitudinal neuroimaging datasets are temporally sparse with a few follow-up scans per subject.<n>We propose a novel 4D (3DxT) diffusion-based generative framework that effectively models and synthesizes longitudinal anatomy over time.<n>Our method outperforms state-of-the-art baselines in generating anatomically accurate, temporally consistent, and clinically meaningful brain trajectories.
• Score: 4.65021411381981
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding and predicting the progression of neurodegenerative diseases remains a major challenge in medical AI, with significant implications for early diagnosis, disease monitoring, and treatment planning. However, most available longitudinal neuroimaging datasets are temporally sparse with a few follow-up scans per subject. This scarcity of temporal data limits our ability to model and accurately capture the continuous anatomical changes related to disease progression in individual subjects. To address this problem, we propose a novel 4D (3DxT) diffusion-based generative framework that effectively models and synthesizes longitudinal brain anatomy over time, conditioned on available clinical variables such as health status, age, sex, and other relevant factors. Moreover, while most current approaches focus on manipulating image intensity or texture, our method explicitly learns the data distribution of topology-preserving spatiotemporal deformations to effectively capture the geometric changes of brain structures over time. This design enables the realistic generation of future anatomical states and the reconstruction of anatomically consistent disease trajectories, providing a more faithful representation of longitudinal brain changes. We validate our model through both synthetic sequence generation and downstream longitudinal disease classification, as well as brain segmentation. Experiments on two large-scale longitudinal neuroimage datasets demonstrate that our method outperforms state-of-the-art baselines in generating anatomically accurate, temporally consistent, and clinically meaningful brain trajectories. Our code is available on Github.

Related papers

• Conditional Neural ODE for Longitudinal Parkinson's Disease Progression Forecasting [51.906871559732245]
  Parkinson's disease (PD) shows heterogeneous, evolving brain-morphometry patterns.<n>Modeling these longitudinal trajectories enables mechanistic insight, treatment development, and individualized 'digital-twin' forecasting.<n>We propose CNODE, a novel framework for continuous, individualized PD progression forecasting.
  arXiv  Detail & Related papers  (2025-11-06T20:16:33Z)
• Disentangling Neurodegeneration with Brain Age Gap Prediction Models: A Graph Signal Processing Perspective [89.99666725996975]
  The brain age gap prediction (BAGP) models estimate the difference between a person's predicted brain age from data and their chronological age.<n>This tutorial article provides an overview of BAGP and introduces a principled framework for this application based on recent advancements in graph signal processing (GSP)<n>VNNs offer strong theoretical grounding and operational interpretability, enabling robust estimation of brain age gap predictions.
  arXiv  Detail & Related papers  (2025-10-14T17:44:45Z)
• Integrating Anatomical Priors into a Causal Diffusion Model [14.471851828800055]
  3D brain MRI studies often examine subtle morphometric differences that are hard to detect visually.<n>Counterfactual models struggle to produce plausible MRIs due to the lack of explicit inductive biases to preserve fine-grained anatomical details.<n>We propose to explicitly integrate anatomical constraints on a voxel-level as prior into a generative diffusion framework.
  arXiv  Detail & Related papers  (2025-09-10T23:22:05Z)
• A Stage-Aware Mixture of Experts Framework for Neurodegenerative Disease Progression Modelling [6.577465893404979]
  Long-term progression of neurodegenerative diseases is commonly conceptualized as a graph diffusion process.<n>We propose a novel stage-aware Mixture Experts framework that explicitly models how different contributing mechanisms dominate at different disease stages.<n>The resulting IGND-MoE model dynamically integrates these components across temporal states, offering a principled way to understand how stage-specific pathological mechanisms contribute to progression.
  arXiv  Detail & Related papers  (2025-08-09T16:09:00Z)
• Causal Disentanglement for Robust Long-tail Medical Image Generation [80.15257897500578]
  We propose a novel medical image generation framework, which generates independent pathological and structural features.<n>We leverage a diffusion model guided by pathological findings to model pathological features, enabling the generation of diverse counterfactual images.
  arXiv  Detail & Related papers  (2025-04-20T01:54:18Z)
• Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis [55.959002385347645]
  Latent Drifting enables diffusion models to be conditioned for medical images fitted for the complex task of counterfactual image generation.<n>We evaluate our method on three public longitudinal benchmark datasets of brain MRI and chest X-rays for counterfactual image generation.
  arXiv  Detail & Related papers  (2024-12-30T01:59:34Z)
• Inpainting Pathology in Lumbar Spine MRI with Latent Diffusion [4.410798232767917]
  We propose an efficient method for inpainting pathological features onto healthy anatomy in MRI. We evaluate the method's ability to insert disc herniation and central canal stenosis in lumbar spine sagittal T2 MRI.
  arXiv  Detail & Related papers  (2024-06-04T16:47:47Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• Learning Spatio-Temporal Model of Disease Progression with NeuralODEs from Longitudinal Volumetric Data [4.998875488622879]
  We develop a deep learning method that models the evolution of age-related disease by processing a single medical scan. For Geographic Atrophy, the proposed method outperformed the related baseline models in the atrophy growth prediction. For Alzheimer's Disease, the proposed method demonstrated remarkable performance in predicting the brain ventricle changes induced by the disease.
  arXiv  Detail & Related papers  (2022-11-08T13:28:26Z)
• Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes [102.02672176520382]
  Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition. We develop deep diffusion processes to model "dynamic comorbidity networks"
  arXiv  Detail & Related papers  (2020-01-08T15:47:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.