Acoustic Wave Field Reconstruction from Compressed Measurements with Application in Photoacoustic Tomography

TL;DR

This paper introduces a novel method for reconstructing acoustic wave fields from compressed patterned measurements, leveraging Curvelet sparsity and ADMM algorithms, with applications in photoacoustic tomography.

Contribution

It proposes a new compressed sensing approach for acoustic field recovery using patterned detection and a modified Curvelet frame tailored for photoacoustic data.

Findings

Effective reconstruction of acoustic fields from limited measurements.

Improved photoacoustic image quality using the proposed method.

Demonstrated applicability in photoacoustic tomography scenarios.

Abstract

We present a method for the recovery of compressively sensed acoustic fields using patterned, instead of point-by-point, detection. From a limited number of such compressed measurements, we propose to reconstruct the field on the sensor plane in each time step independently assuming its sparsity in a Curvelet frame. A modification of the Curvelet frame is proposed to account for the smoothing effects of data acquisition and motivated by a frequency domain model for photoacoustic tomography. An ADMM type algorithm, SALSA, is used to recover the pointwise data in each individual time step from the patterned measurements. For photoacoustic applications, the photoacoustic image of the initial pressure is reconstructed using time reversal in ${\bf k}$-Wave Toolbox.