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Sparsity-Driven Sparse-Aperture Ultrasound Imaging
Authors: Müjdat Çetin, Emmanuel Bossy, Robin Cleveland, and W. Clem Karl
Published in: IEEE International Conference on Acoustics, Speech, and Signal Processing 2006
Publication year: 2006
Abstract: We propose an image formation algorithm for ultrasound
imaging based on sparsity-driven regularization functionals.
We consider data collected by synthetic transducer arrays,
with the primary motivating application being nondestructive
evaluation. Our framework involves the use of a physical
optics-based forward model of the observation process; the
formulation of an optimization problem for image formation;
and the solution of that problem through efficient numerical
algorithms. Our sparsity-driven, model-based approach
achieves the preservation of physical features while suppressing
spurious artifacts. It also provides robust reconstructions
in the case of sparse observation apertures. We demonstrate
the effectiveness of our imaging strategy on real ultrasound
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