Non-Parametric GraphNet-Regularized Representation of dMRI in Space and Time

Abstract : Effective representation of the four-dimensional diffusion MRI signal – varying over three-dimensional q-space and diffusion time τ – is a sought-after and still unsolved challenge in diffusion MRI (dMRI). We propose a functional basis approach that is specifically designed to represent the dMRI signal in this qτ-space. Following recent terminology, we refer to our qτ-functional basis as " qτ-dMRI ". qτ-dMRI can be seen as a time-dependent realization of q-space imaging by Paul Callaghan and colleagues. We use GraphNet regularization – imposing both signal smoothness and sparsity – to drastically reduce the number of diffusion-weighted images (DWIs) that is needed to represent the dMRI signal in the qτ-space. As the main contribution, qτ-dMRI provides the framework to – without making biophysical assumptions – represent the qτ-space signal and estimate time-dependent q-space indices (qτ-indices), providing a new means for studying diffusion in nervous tissue. We validate our method on both in-silico generated data using Monte-Carlo simulations and an in-vivo test-retest study of two C57Bl6 wild-type mice, where we found good reproducibility of estimated qτ-index values and trends. In the hopes of opening up new τ-dependent venues of studying nervous tissues, qτ-dMRI is the first of its kind in being specifically designed to provide open interpretation of the qτ-diffusion signal.
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Article dans une revue
Medical Image Analysis, Elsevier, 2017, 〈10.1016/〉
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Soumis le : lundi 28 août 2017 - 23:15:20
Dernière modification le : samedi 15 décembre 2018 - 01:45:27


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Rutger Fick, Alexandra Petiet, Mathieu Santin, Anne-Charlotte Philippe, Stephane Lehericy, et al.. Non-Parametric GraphNet-Regularized Representation of dMRI in Space and Time. Medical Image Analysis, Elsevier, 2017, 〈10.1016/〉. 〈hal-01578296〉



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