Preprint Article Version 1 This version is not peer-reviewed

Sparse Ultrasound Imaging via Manifold Low-Rank Approximation and Non-Convex Greedy Pursuit

Version 1 : Received: 19 October 2018 / Approved: 22 October 2018 / Online: 22 October 2018 (06:02:47 CEST)

A peer-reviewed article of this Preprint also exists.

Rigo Passarin, T.A.; Wüst Zibetti, M.V.; Rodrigues Pipa, D. Sparse Ultrasound Imaging via Manifold Low-Rank Approximation and Non-Convex Greedy Pursuit. Sensors 2018, 18, 4097. Rigo Passarin, T.A.; Wüst Zibetti, M.V.; Rodrigues Pipa, D. Sparse Ultrasound Imaging via Manifold Low-Rank Approximation and Non-Convex Greedy Pursuit. Sensors 2018, 18, 4097.

Journal reference: Sensors 2018, 18, 4097
DOI: 10.3390/s18124097

Abstract

Model-based image reconstruction has brought improvements in terms of contrast and spatial resolution to imaging applications such as magnetic resonance imaging and emission computed tomography. However, their use for pulse-echo techniques like ultrasound imaging is limited by the fact that model-based algorithms assume a finite grid of possible locations of scatterers in a medium -- which does not reflect the continuous nature of real world objects and creates a problem known as off-grid deviation. To cope with this problem, we present a method of dictionary expansion and constrained reconstruction that approximates the continuous manifold of all possible scatterer locations within a region of interest. The expanded dictionary is created using a highly coherent sampling of the region of interest, followed by a rank reduction procedure based on a truncated singular value decomposition. We develop a greedy algorithm, based on the Orthogonal Matching Pursuit (OMP), that uses a correlation-based non-convex constraint set that allows for the division of the region of interest into cells of any size. To evaluate the performance of the method, we present results of 2-dimensional ultrasound image reconstructions with simulated data in a nondestructive testing application. Our method succeeds in the reconstructions of sparse images from noisy measurements, providing higher accuracy than previous approaches based on regular discrete models.

Subject Areas

ultrasound; nondestructive testing; manifolds; inverse problems; dictionary; rank reduction

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