TY  - JOUR
N1  - This work is licensed under a Creative Commons Attribution 4.0 International License. The images
or other third party material in this article are included in the Creative Commons license,
unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license,
users will need to obtain permission from the license holder to reproduce the material. To view a copy of this
license, visit http://creativecommons.org/licenses/by/4.0/
JF  - Medical Image Analysis
TI  - Degenerative adversarial neuroimage nets for brain scan simulations: Application in ageing and dementia
N2  - 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.
Y1  - 2021/10/14/
UR  - https://doi.org/10.1016/j.media.2021.102257
KW  - 4D-DANI-Net
KW  -  4D-MRI
KW  -  Adversarial training
KW  -  Ageing
KW  -  Brain
KW  -  Dementia
KW  -  Disease progression modelling
KW  -  Generative models
KW  -  Neuro-image
KW  -  Neurodegeneration
KW  -  Synthetic-images
A1  - Ravi, D
A1  - Blumberg, SB
A1  - Ingala, S
A1  - Barkhof, F
A1  - Alexander, DC
A1  - Oxtoby, NP
A1  - Alzheimer?s Disease Neuroimaging Initiative
AV  - public
ID  - discovery10138058
VL  - 75
ER  -