Bishesh Khanal11INRIA Sophia Antipolis Méditerranée, Asclepios Research Project, Marco Lorenzi1,22University College London, Translational Imaging Group, London, UK, Nicholas Ayache1 and Xavier Pennec1
This is a pre-print (a working paper) of the following published paper:
Bishesh Khanal, Marco Lorenzi, Nicholas Ayache, Xavier Pennec. A biophysical model of brain deformation to simulate and analyze longitudinal MRIs of patients with Alzheimer’s disease. NeuroImage, Elsevier, 2016, 134, pp.35-52.
We propose a framework for developing a comprehensive biophysical model that could predict and simulate realistic longitudinal MRIs of patients with Alzheimer’s Disease (AD). The framework includes three major building blocks: i) Atrophy generation ii) Brain deformation iii) Realistic MRI generation. Within this framework, this paper focuses on a detailed implementation of the brain deformation block with a carefully designed biomechanics-based tissue loss model. For a given baseline brain MRI, the model yields a deformation field imposing the desired atrophy at each voxel of the brain parenchyma while allowing the CSF to expand as required to globally compensate for the locally prescribed volume loss. Our approach is inspired by biomechanical principles and involves a system of equations similar to Stokes equations in fluid mechanics but with the presence of a non-zero mass source term. We use this model to simulate longitudinal MRIs by prescribing complex patterns of atrophy. We provide an application of the presented model as a benchmarking tool for longitudinal atrophy measurement algorithms such as FreeSurfer. We present experiments that provide an insight into the role of different biomechanical parameters in the model. The model allows simulating images with exactly the same tissue atrophy but with different underlying deformation fields in the image. We explore the influence of different spatial distributions of atrophy on the image appearance and on the measurements of atrophy reported by various global and local atrophy estimation algorithms. We also present a pipeline that allows evaluating atrophy estimation algorithms by simulating longitudinal MRIs from large number of real subject MRIs with complex subject-specific atrophy patterns. The proposed framework could help understand the implications of different model assumptions, regularization choices and spatial priors for the detection and measurement of brain atrophy from longitudinal brain MRIs.
biophysical model, Alzheimer’s disease, simulation of atrophy, longitudinal MRIs simulation, longitudinal modeling
Alzheimer’s Disease (AD) is one of the most common types of dementia. It is a neurodegenerative disease that progresses gradually over several years with the accumulation of neurofibrillary tangles (NFTs) and amyloid-\(\beta\) (A-\(\beta\)) plaques (Braak 1991). These microscopic neurobiological changes are followed by the progressive neuronal damage that leads to the atrophy of the brain tissue. The atrophy or the volume changes of brain tissue is a macroscopic change that structural Magnetic Resonance Imaging (MRI) can estimate in different brain regions (Frisoni 2010).
Various image segmentation and registration methods for the brain structural MRIs have been proposed to model and quantify volume loss or atrophy in AD. For instance, non-linear registration of time series of brain MRIs is used to quantify in