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An evolutionary framework for microstructure-sensitive generalized diffusion gradient waveforms

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Abstract

In diffusion-weighted MRI, general gradient waveforms became of interest for their sensitivity to microstructure features of the brain white matter. However, the design of such waveforms remains an open problem. In this work, we propose a framework for generalized gradient waveform design with optimized sensitivity to selected microstruc-ture features. In particular, we present a rotation-invariant method based on a genetic algorithm to maximize the sensitivity of the signal to the intra-axonal volume fraction. The sensitivity is evaluated by computing a score based on the Fisher information matrix from Monte-Carlo simulations , which offer greater flexibility and realism than conventional analytical models. As proof of concept, we show that the optimized waveforms have higher scores than the conventional pulsed-field gradients experiments. Finally, the proposed framework can be generalized to optimize the waveforms for to any microstructure feature of interest.
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Dates and versions

inserm-02910086 , version 1 (31-07-2020)

Identifiers

  • HAL Id : inserm-02910086 , version 1

Cite

Raphaël Truffet, Jonathan Rafael-Patino, Gabriel Girard, Marco Pizzolato, Christian Barillot, et al.. An evolutionary framework for microstructure-sensitive generalized diffusion gradient waveforms. MICCAI 2020 - 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, Oct 2020, Lima, Peru. pp.1-11. ⟨inserm-02910086⟩
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