A biophysical model of brain deformation to simulate and analyze longitudinal MRIs of patients with Alzheimer's disease

Abstract

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, and 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 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.

On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey

“…This has allowed to better analyse and model the progression of the disease [108] and its effects on individuals' spatiotemporal brain atrophy patterns [109]. Moreover, it has been possible to verify that the brain atrophy in AD patients and in subjects converting from MCI to AD happens much faster than in healthy adults [110,111].…”

Simulating Longitudinal Brain MRIs with Known Volume Changes and Realistic Variations in Image Intensity

“…A number of atrophy simulators have been proposed in the literature to produce ground truth MRIs (Smith et al, 2003; Camara et al, 2006; Karaçali and Davatzikos, 2006; Pieperhoff et al, 2008; Sharma et al, 2010; Modat et al, 2014; Radua et al, 2014; Khanal et al, 2016a). Most of these simulators use a model that attempts to produce a deformation field with the specified volume changes in the input brain MRI.…”

“…Inability to produce realistic intensity variation and noise in simulated longitudinal images is one of the key limitations in the state-of-the-art atrophy simulators, including our previous work (Khanal et al, 2016a). In this work, we propose a simple but elegant solution to remove the limitation of previous atrophy simulators.…”

“…In this work, we propose a simple but elegant solution to remove the limitation of previous atrophy simulators. First, our biophysical model of brain deformation (Khanal et al, 2016a) is used to obtain a dense deformation field with specified volume changes. Then, to obtain realistic intensity variations, intensities in the simulated images are resampled from baseline repeat scans of the same patient.…”

“…To our knowledge, is the first atrophy simulator to be made open-source. uses the biophysical model presented in Khanal et al (2016a) but introduces a new numerical scheme to compute divergence, which removes the numerical inconsistency presented in the previous work. This is further explained in detail in Section 4.2.…”