Modeling and simulation of realistic longitudinal structural brain MRIs with atrophy in Alzheimer's disease

Atrophy of the brain due to the death of neurons in Alzheimer’s Disease (AD) is well observed in longitudinal structural magnetic resonance images (MRIs). This thesis focuses on developing a biophysical model able to reproduce changes observed in the longitudinal MRIs of AD patients. Simulating realistic longitudinal MRIs with atrophy from such a model could be used to evaluate brain morphometry algorithms and data driven disease progression models that use information extracted from structural MRIs. The long term perspectives of such a model would be in simulating different scenarios of disease evolution, and trying to disentangle potentially different mechanisms of structural changes and their relationship in time. In this thesis, we develop a framework to simulate realistic longitudinal brain images with atrophy (and potentially growth). The core component of the framework is a brain deformation model: a carefully designed biomechanics-based tissue loss model to simulate the deformations with prescribed atrophy patterns. We also develop a method to interpolate or extrapolate longitudinal images of a subject by simulating images with subject-specific atrophy patterns. The method was used to simulate interpolated time-point MRIs of 46 AD patients by prescribing atrophy estimated for each patient from the available two time-point MRIs. Real images have noise and image acquisition artefacts, and real longitudinal images have variation of intensity characteristics among the individual images. We present a method that uses the brain deformation model and different available images of a subject to add realistic variations of intensities in the synthetic longitudinal images. Finally, the software developed during the thesis, named Simul@trophy, to simulate realistic longitudinal brain images with our brain deformation model is released in an open-source repository.