A sparsification and reconstruction strategy for compressed sensing photoacoustic tomography

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 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, a different concept enabling sparse recovery in CS PAT is introduced. This 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, a reconstruction framework is developed to jointly recover the initial and modified sparse sources. Reconstruction results with simulated as well as experimental data are given.