A compressed-sensing approach for ultrasound imaging

Adrien Besson¹, Rafael E. Carrillo², Dimitris Perdios¹, Marcel Arditi¹, Yves Wiaux³ and Jean-Philippe Thiran^{1, 4}

¹Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland

²Centre Suisse d'Electronique et de Microtechnique (CSEM), Switzerland

³Institute of Sensors, Signals and Systems, Heriot-Watt University , UK

⁴Department of Radiology, University Hospital Center (CHUV), Switzerland

Poster presented at the Signal Processing with Adaptive Sparse Structured Representations (SPARS) workshop.

Abstract

Ultrasonography uses multiple piezoelectric element probes to image tissues. Current time-domain beamforming techniques require the signal at each transducer-element to be sampled at a rate higher than the Nyquist criterion, resulting in an extensive amount of data to be received, stored and processed. In this work, we propose to exploit sparsity of the signal received at each transducer-element. The proposed approach uses multiple compressive multiplexers for signal encoding and solves an l₁-minimization in the decoding step, resulting in the reduction of 75% of the amount of data, the number of cables and the number of analog-to-digital converters required to perform high quality reconstruction.