# Degenerative Adversarial NeuroImage Nets for Brain Scan Simulations: Application in Ageing and Dementia

arXiv: https://arxiv.org/abs/1912.01526 · HTML: https://mlsanity.com/paper/1912.01526/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2019.

## Built on by (most cited first, 30 of 34)

- [Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models](https://mlsanity.com/paper/2111.14923/) (2021)
- [Enhancing Spatiotemporal Disease Progression Models via Latent Diffusion and Prior Knowledge](https://mlsanity.com/paper/2405.03328/) (2024)
- [Low-field magnetic resonance image enhancement via stochastic image quality transfer](https://mlsanity.com/paper/2304.13385/) (2023)
- [Brain Latent Progression: Individual-based Spatiotemporal Disease Progression on 3D Brain MRIs via Latent Diffusion](https://mlsanity.com/paper/2502.08560/) (2025)
- [Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience](https://mlsanity.com/paper/2206.07081/) (2022)
- [Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET](https://mlsanity.com/paper/2401.16363/) (2024)
- [Synthesizing Individualized Aging Brains in Health and Disease with Generative Models and Parallel Transport](https://mlsanity.com/paper/2502.21049/) (2025)
- [Inflating 2D Convolution Weights for Efficient Generation of 3D Medical Images](https://mlsanity.com/paper/2208.03934/) (2022)
- [Multiscale Metamorphic VAE for 3D Brain MRI Synthesis](https://mlsanity.com/paper/2301.03588/) (2023)
- [Learning Patient-Specific Disease Dynamics with Latent Flow Matching for Longitudinal Imaging Generation](https://mlsanity.com/paper/2512.09185/) (2025)
- [Individualized multi-horizon MRI trajectory prediction for Alzheimer's Disease](https://mlsanity.com/paper/2408.02018/) (2024)
- [PIE: Simulating Disease Progression via Progressive Image Editing](https://mlsanity.com/paper/2309.11745/) (2023)
- [DeepBrainPrint: A Novel Contrastive Framework for Brain MRI Re-Identification](https://mlsanity.com/paper/2302.13057/) (2023)
- [Interpretability of Machine Learning Methods Applied to Neuroimaging](https://mlsanity.com/paper/2204.07005/) (2022)
- [Generative Aging of Brain Images with Diffeomorphic Registration](https://mlsanity.com/paper/2205.15607/) (2022)
- [Temporally-Aware Diffusion Model for Brain Progression Modelling with Bidirectional Temporal Regularisation](https://mlsanity.com/paper/2509.03141/) (2025)
- [Neural Autoregressive Modeling of Brain Aging](https://mlsanity.com/paper/2507.22954/) (2025)
- [Anatomically Guided Latent Diffusion for Brain MRI Progression Modeling](https://mlsanity.com/paper/2601.14584/) (2026)
- [Aging prediction using deep generative model toward the development of preventive medicine](https://mlsanity.com/paper/2208.10797/) (2022)
- [Anatomy-Guided Surface Diffusion Model for Alzheimer's Disease Normative Modeling](https://mlsanity.com/paper/2403.04531/) (2024)
- [Integrating Anatomical Priors into a Causal Diffusion Model](https://mlsanity.com/paper/2509.09054/) (2025)
- [Forecasting Future Anatomies: Longitudinal Brain Mri-to-Mri Prediction](https://mlsanity.com/paper/2511.02558/) (2025)
- [Temporally Adjustable Longitudinal Fluid-Attenuated Inversion Recovery MRI Estimation / Synthesis for Multiple Sclerosis](https://mlsanity.com/paper/2209.04275/) (2022)
- [Deformably-Scaled Transposed Convolution](https://mlsanity.com/paper/2210.09446/) (2022)
- [Shape Modeling of Longitudinal Medical Images: From Diffeomorphic Metric Mapping to Deep Learning](https://mlsanity.com/paper/2503.21489/) (2025)
- [Controllable Surface Diffusion Generative Model for Neurodevelopmental Trajectories](https://mlsanity.com/paper/2508.03706/) (2025)
- [BrainPath: A Biologically-Informed AI Framework for Individualized Aging Brain Generation](https://mlsanity.com/paper/2508.16667/) (2025)
- [MRExtrap: Longitudinal Aging of Brain MRIs using Linear Modeling in Latent Space](https://mlsanity.com/paper/2508.19482/) (2025)
- [TraceTrans: Translation and Spatial Tracing for Surgical Prediction](https://mlsanity.com/paper/2510.22379/) (2025)
- [AD-DAE: Alzheimer's Disease Progression Modeling with Unpaired Longitudinal MRI using Diffusion Auto-Encoders](https://mlsanity.com/paper/2511.05934/) (2025)

## Abstract (arXiv)

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.