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Abstract #1908

Full posterior estimation of Gray Matter cytoarchitecture using a three-compartment model with exchange: a simulation-based study

Thomas Meunier1, Chengran Fang2, Maëliss Jallais1, and Demian Wassermann1
1INRIA Saclay, Paris, France, 2INRIA Saclay, Palaiseau, France


We extract cytoarchitectural characteristics of brain gray matter from diffusion MRI signals including soma size, neurite signal fraction and water exchange. Our model improves on state-of-the-art in that 1) we extract an invertible system leading to stable parameters estimation, 2) our simulation-based inference approach allows to obtain the full posterior distribution of the parameters given a signal. Our solution is a two-step model. First, a new forward model relates summary statistics of the dMRI signal to different tissue parameters. Then, a likelihood-free inference-based algorithm is applied to invert the model, and returns a full posterior distribution over the parameter space.

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