Degenerative adversarial neuroimage nets for brain scan simulations: Application in ageing and dementia

for the Alzheimer's Disease Neuroimaging Initiative, Daniele Ravi, Stefano B. Blumberg, Silvia Ingala, Frederik Barkhof, Daniel C. Alexander, Neil P. Oxtoby

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number102257
Pages (from-to)1-15
Number of pages15
JournalMedical Image Analysis
Early online date14 Oct 2021
Publication statusPublished - 1 Jan 2022


  • 4D-DANI-Net
  • 4D-MRI
  • Adversarial training
  • Ageing
  • Brain
  • Dementia
  • Disease progression modelling
  • Generative models
  • Neuro-image
  • Neurodegeneration
  • Synthetic-images
  • Neuroimaging
  • Humans
  • Brain/diagnostic imaging
  • Magnetic Resonance Imaging
  • Image Processing, Computer-Assisted
  • Aging
  • Alzheimer Disease/diagnostic imaging


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