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Accurate and realistic simulation of high-dimensional medical images hasbecome an important research area relevant to many AI-enabled healthcareapplications. However, current state-of-the-art approaches lack the ability toproduce satisfactory high-resolution and accurate subject-specific images. Inthis work, we present a deep learning framework, namely 4D-DegenerativeAdversarial NeuroImage Net (4D-DANI-Net), to generate high-resolution,longitudinal MRI scans that mimic subject-specific neurodegeneration in ageingand dementia. 4D-DANI-Net is a modular framework based on adversarial trainingand a set of novel spatiotemporal, biologically-informed constraints. To ensureefficient training and overcome memory limitations affecting suchhigh-dimensional problems, we rely on three key technological advances: i) anew 3D training consistency mechanism called Profile Weight Functions (PWFs),ii) a 3D super-resolution module and iii) a transfer learning strategy tofine-tune the system for a given individual. To evaluate our approach, wetrained the framework on 9852 T1-weighted MRI scans from 876 participants inthe Alzheimer's Disease Neuroimaging Initiative dataset and held out a separatetest set of 1283 MRI scans from 170 participants for quantitative andqualitative assessment of the personalised time series of synthetic images. Weperformed three evaluations: i) image quality assessment; ii) quantifying theaccuracy 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-Netproduces realistic, low-artefact, personalised time series of synthetic T1 MRIthat outperforms benchmark models.

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