Being able to accurately model the progression of Alzheimer’s disease (AD) is important for the diagnosis and prognosis of the disease, as well as to evaluate the effect of disease modifying treatments. Whilst there has been success in modeling the progression of AD related clinical biomarkers and image derived features over the course of the disease, modeling the expected progression as observed by magnetic resonance (MR) images directly remains a challenge. Here, we apply some recently developed ideas from the field of generative adversarial networks (GANs) which provide a powerful way to model and manipulate MR images directly though the technique of image arithmetic. This allows for synthetic images based upon an individual subject’s MR image to be produced expressing different levels of the features associated with AD. We demonstrate how the model can be used to both introduce and remove AD-like features from two regions in the brain, and show that these predicted changes correspond well to the observed changes over a longitudinal examination. We also propose a modification to the GAN training procedure to encourage the model to better represent the more extreme cases of AD present in the dataset. We show the benefit of this modification by comparing the ability of the resulting models to encode and reconstruct real images with high atrophy and other unusual features.
Christopher Bowles, Roger Gunn, Alexander Hammers, and Daniel Rueckert, "Modelling the progression of Alzheimer's disease in MRI using generative adversarial networks," Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105741K (Presented at SPIE Medical Imaging: February 13, 2018; Published: 2 March 2018); https://doi.org/10.1117/12.2293256.
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