Simulating Patient Specific Multiple Time-point MRIs From a Biophysical Model of Brain Deformation in Alzheimer's Disease

Bishesh Khanal, Marco Lorenzi, Nicholas Ayache and Xavier Pennec

Abstract

This paper proposes a framework to simulate patient specific structural Magnetic Resonance Images (MRIs) from the available time-points of Alzheimer’s Disease(AD) subjects. We use a biophysical model of brain deformation due to atrophy that can generate biologically plausible deformation for any given desired volume changes at the voxel level of the brain MRI. Large number of brain regions are segmented in 45 AD patients and the atrophy rates per year are estimated in these regions from two extremal available scans. Assuming linear progression of atrophy, the volume changes in scans closest to the middle time-point images from the baseline scans are computed. These atrophy maps are prescribed to the baseline images to simulate the middle time-point images by using the biophysical model of brain deformation. The volume changes from the baseline image to the real middle time-point are compared to the volume changes in the simulated middle time-point images. This present framework also allows to introduce desired atrophy patterns at different time-points to simulate non-linear progression of atrophy. This opens a way to use a biophysical model of brain deformation to evaluate methods that study the temporal progression and spatial relationships of atrophy evolution in AD.

Keywords: Alzheimer’s disease, biophysical modeling, biomechanical simulation

This is a pre-print of the following published article: Bishesh Khanal, Marco Lorenzi, Nicholas Ayache, Xavier Pennec. Simulating Patient Specific Multiple Time-point MRIs From a Biophysical Model of Brain Deformation in Alzheimer’s Disease. Workshop on Computational Biomechanics for Medicine - X, Oct 2015, Munich, France. 2015.

Introduction

\label{sec:introduction} 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. Many different methods have been proposed to estimate atrophy in some particular regions of brain that are known to be affected in AD (Frisoni 2010).

In addition to estimating specific brain structures with atrophy, longitudinal imaging data could also potentially be used to study the temporal inter-relationship of atrophy in different structures. For instance in (Carmichael 2013), authors estimate per-individual rates of atrophy in \(34\) cortical regions and in hippocampus. Then they study the groupings of these structures based on the correlation of the atrophy rates. In (Fonteijn 2012), authors define AD progression as a series of discrete events. Atrophy in different parts of the brain are taken as different events along with clinical events. Without any prior to their ordering, the model finds most probable order for these events from the data itself. They use Bayesian statistical algorithms for fitting in the event-based disease progression model. The objective of these kinds of studies is to understand how different regions of brain interact during the neurodegeneration and find its trajectory. Such studies can benefit with large number of longitudinal images of AD patients. In this context, a model that can simulate many time-point images from a few available longitudinal images can be a valuable tool.