  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.
