A New Sparsification and Reconstruction Strategy for Compressed Sensing Photoacoustic Tomography

TL;DR

This paper introduces a novel sparsification and reconstruction method for compressed sensing in photoacoustic tomography, leveraging the sparsity of the Laplacian of the source to improve image recovery from fewer measurements.

Contribution

It proposes a new approach that uses the second derivative of pressure data to exploit source sparsity, enhancing compressed sensing reconstruction in PAT.

Findings

Effective reconstruction with simulated data

Successful application to experimental data

Improved sparsity exploitation in CS PAT

Abstract

Compressed sensing (CS) is a promising approach to reduce the number of measurements in photoacoustic tomography (PAT) while preserving high spatial resolution. This allows to increase the measurement speed and to reduce system costs. Instead of collecting point-wise measurements, in CS one uses various combinations of pressure values at different sensor locations. Sparsity is the main condition allowing to recover the photoacoustic (PA) source from compressive measurements. In this paper we introduce a new concept enabling sparse recovery in CS PAT. Our approach is based on the fact that the second time derivative applied to the measured pressure data corresponds to the application of the Laplacian to the original PA source. As typical PA sources consist of smooth parts and singularities along interfaces the Laplacian of the source is sparse (or at least compressible). To efficiently exploit the induced sparsity we develop a reconstruction framework to jointly recover the initial and the modified sparse source. Reconstruction results with simulated as well as experimental data are given.