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Abstract #3487

Optimal selection of diffusion-weighting gradient waveforms using compressed sensing and dictionary learning

Raphaƫl Malak Truffet1, Christian Barillot1, and Emmanuel Caruyer1

1Univ Rennes, CNRS, Inria, Inserm, IRISA - UMR 6074, VisAGeS - ERL U 1228, F-35000 Rennes, France, Rennes, France

Acquisition sequences in diffusion MRI rely on the use time-dependent magnetic field gradients. Each gradient waveform encodes a diffusion-weighted measure; a large number of such measurements are necessary for the in vivo reconstruction of microstructure parameters. We propose here a method to select only a subset of the measurements while being able to predict the unseen data using compressed sensing. We learn a dictionary using a training dataset generated with Monte-Carlo simulations; we then compare two different heuristics to select the measures to use for the prediction. We found that an undersampling strategy limiting the redundancy of the measures allows for a more accurate reconstruction when compared with random undersampling with similar sampling rate.

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