Working Paper Article Version 1 This version is not peer-reviewed

Subsampling Approaches for Compressed Sensing with Ultrasound Arrays in Non-Destructive Testing

Version 1 : Received: 2 November 2020 / Approved: 3 November 2020 / Online: 3 November 2020 (14:11:06 CET)

How to cite: Pérez, E.; Kirchhof, J.; Krieg, F.; Römer, F. Subsampling Approaches for Compressed Sensing with Ultrasound Arrays in Non-Destructive Testing. Preprints 2020, 2020110154 Pérez, E.; Kirchhof, J.; Krieg, F.; Römer, F. Subsampling Approaches for Compressed Sensing with Ultrasound Arrays in Non-Destructive Testing. Preprints 2020, 2020110154

Abstract

Full Matrix Capture is a multi-channel data acquisition method which enables flexible, high resolution imaging using ultrasound arrays. However, the measurement time and data volume are increased considerably. Both of these costs can be circumvented via compressed sensing, which exploits prior knowledge of the underlying model and its sparsity to reduce the amount of data needed to produce a high resolution image. In order to design compression matrices that are physically realizable without sophisticated hardware constraints, structured subsampling patterns are designed and evaluated in this work. The design is based on the analysis of the Cramér-Rao Bound of a single scatterer in a homogeneous, isotropic medium. A numerical comparison of the point spread functions obtained with different compression matrices and the Fast Iterative Shrinkage/Thresholding Algorithm shows that the best performance is achieved when each transmit event can use a different subset of receiving elements and each receiving element uses a different section of the echo signal spectrum. Such a design has the advantage of outperforming other structured patterns to the extent that suboptimal selection matrices provide a good performance and can be efficiently computed with greedy approaches.

Subject Areas

Full Matrix Capture; Compressed Sensing; Sparse Array

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