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Abstract #3532

Gini reweighted ℓ1 minimization for rapid MRI

Carlos Castillo-Passi1,2, Claudia Prieto1,2,3, Gabriel Varela-Mattatall1,2, Carlos Sing-Long2,4,5, and Pablo Irarrazaval1,2,5

1Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, 2Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile, 3Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, United Kingdom, 4Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, United States, 5Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile

Under-sampling acquisition is oftenly used to reduce the scan time. Compressed Sensing allows the reconstruction of these data by solving a convex optimization problem. This is done to exploit the sparsity of the signals using the ℓ1-norm. We propose to use the Gini Index as a sparsity measure. In this work we demonstrate that this index allow to further increase the under-sampling factor. Interestingly this non-linear index can be computed by solving iteratively reweighted ℓ1 problems, without excessive computational load.

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