ArticleMedical image analysis2022
Degenerative adversarial neuroimage nets for brain scan simulations: Application in ageing and dementia.
Article in Medical image analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Integrating Anatomical Priors Into a Causal Diffusion Model.IEEE transactions on medical imaging · 2026Article
- Forecasting individualized progression of Alzheimer's disease using structural MRI and population spatiotemporal priors.Frontiers in aging neuroscience · 2026Article
- Neural Autoregressive Modeling of Brain Aging.Deep generative models : 5th MICCAI workshop, DGM4MICCAI 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings. DGM4MICCAI (Workshop) (5th : 2025 : Taejon-si, Korea) · 2026Article
- AI digital-twin ecosystem translating gut-microbiome-neuroimmune signals into precision sleep-mood interventions.Frontiers in psychiatry · 2026Review
- Brain Latent Progression: Individual-based spatiotemporal disease progression on 3D Brain MRIs via latent diffusion.Medical image analysis · 2025Article
- Anatomy-Guided Surface Diffusion Model for Alzheimer's Disease Normative Modeling.Proceedings of machine learning research · 2025Article
- Progressive Disease Image Generation with Ordinal-Aware Diffusion Models.Diagnostics (Basel, Switzerland) · 2025Article
- Population-Driven Synthesis of Personalized Cranial Development From Cross-Sectional Pediatric CT Images.IEEE transactions on bio-medical engineering · 2025Article
- AI in Neurology: Everything, Everywhere, all at Once PART 2: Speech, Sentience, Scruples, and Service.Annals of neurology · 2025Review
- Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2025Article
- Pioneering new paths: the role of generative modelling in neurological disease research.Pflugers Archiv : European journal of physiology · 2025Review
- Deep learning and generative artificial intelligence in aging research and healthy longevity medicine.Aging · 2025Review
- Shape modeling of longitudinal medical images: from diffeomorphic metric mapping to deep learning.Frontiers in artificial intelligence · 2025Review
- Metadata-conditioned generative models to synthesize anatomically-plausible 3D brain MRIs.Medical image analysis · 2024Article
- Generative AI unlocks PET insights: brain amyloid dynamics and quantification.Frontiers in aging neuroscience · 2024Article
- A synthetic data generation system for myalgic encephalomyelitis/chronic fatigue syndrome questionnaires.Scientific reports · 2023Article
- Applications of generative adversarial networks in neuroimaging and clinical neuroscience.NeuroImage · 2023Review
- Fast three-dimensional image generation for healthy brain aging using diffeomorphic registration.Human brain mapping · 2023Article
- Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models.Medical image analysis · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Accurate and realistic simulation of high-dimensional medical images has become an important research area relevant to many AI-enabled healthcare applications. However, current state-of-the-art approaches lack the ability to produce satisfactory high-resolution and accurate subject-specific images. In this work, we present a deep learning framework, namely 4D-Degenerative Adversarial NeuroImage Net (4D-DANI-Net), to generate high-resolution, longitudinal MRI scans that mimic subject-specific neurodegeneration in ageing and dementia. 4D-DANI-Net is a modular framework based on adversarial training and a set of novel spatiotemporal, biologically-informed constraints. To ensure efficient training and overcome memory limitations affecting such high-dimensional problems, we rely on three key technological advances: i) a new 3D training consistency mechanism called Profile Weight Functions (PWFs), ii) a 3D super-resolution module and iii) a transfer learning strategy to fine-tune the system for a given individual. To evaluate our approach, we trained the framework on 9852 T1-weighted MRI scans from 876 participants in the Alzheimer's Disease Neuroimaging Initiative dataset and held out a separate test set of 1283 MRI scans from 170 participants for quantitative and qualitative assessment of the personalised time series of synthetic images. We performed three evaluations: i) image quality assessment; ii) quantifying the accuracy of regional brain volumes over and above benchmark models; and iii) quantifying visual perception of the synthetic images by medical experts. Overall, both quantitative and qualitative results show that 4D-DANI-Net produces realistic, low-artefact, personalised time series of synthetic T1 MRI that outperforms benchmark models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.