Compressed sensing photoacoustic tomography reduces to compressed sensing for undersampled Fourier measurements

TL;DR

This paper demonstrates that compressed sensing photoacoustic tomography can be effectively reduced to compressed sensing for undersampled Fourier measurements, supported by theoretical analysis and extensive numerical validation.

Contribution

It establishes a theoretical link between compressed sensing PAT and undersampled Fourier measurements, providing a foundation for improved reconstruction methods.

Findings

Theoretical reduction of PAT to undersampled Fourier CS in practical setups.

Application of Riesz bases and nonuniform Fourier series theories.

Numerical simulations validating the approach.

Abstract

Photoacoustic tomography (PAT) is an emerging imaging modality that aims at measuring the high-contrast optical properties of tissues by means of high-resolution ultrasonic measurements. The interaction between these two types of waves is based on the thermoacoustic effect. In recent years, many works have investigated the applicability of compressed sensing to PAT, in order to reduce measuring times while maintaining a high reconstruction quality. However, in most cases, theoretical guarantees are missing. In this work, we show that in many measurement setups of practical interest, compressed sensing PAT reduces to compressed sensing for undersampled Fourier measurements. This is achieved by applying known reconstruction formulae in the case of the free-space model for wave propagation, and by applying the theories of Riesz bases and nonuniform Fourier series in the case of the bounded domain model. Extensive numerical simulations illustrate and validate the approach.