title: 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.
authors.affiliation: INRIA Sophia Antipolis Méditerranée, Asclepios Research Project. Electronic address: bishesh.khanal@inria.fr.
authors.forename: Bishesh
authors.initials: B
authors.lastname: Khanal
authors.affiliation: INRIA Sophia Antipolis Méditerranée, Asclepios Research Project; University College London, Translational Imaging Group, London, UK.
authors.forename: Marco
authors.initials: M
authors.lastname: Lorenzi
authors.affiliation: INRIA Sophia Antipolis Méditerranée, Asclepios Research Project.
authors.forename: Nicholas
authors.initials: N
authors.lastname: Ayache
authors.affiliation: INRIA Sophia Antipolis Méditerranée, Asclepios Research Project.
authors.forename: Xavier
authors.initials: X
authors.lastname: Pennec
mesh_terms: D000375:Aging / Q000473:pathology*; D000544:Alzheimer Disease / Q000473:pathology / Q000503:physiopathology*; D001921:Brain / Q000473:pathology* / Q000503:physiopathology*; D019245:Compressive Strength; D003198:Computer Simulation; D055119:Elastic Modulus; D006244:Hardness; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008137:Longitudinal Studies; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D009929:Organ Size; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013314:Stress, Mechanical
keywords: Alzheimer's disease; Biophysical model; Longitudinal MRIs simulation; Longitudinal modeling; Simulation of atrophy