Photoacoustic imaging beyond the acoustic diffraction-limit with dynamic speckle illumination and sparse joint support recovery

TL;DR

This paper introduces a compressed sensing-based method to enhance resolution and reconstruction quality in deep tissue photoacoustic imaging beyond the acoustic diffraction limit by leveraging speckle fluctuations and prior knowledge.

Contribution

It presents a novel computational framework that combines speckle fluctuation analysis with sparsity priors to improve photoacoustic image resolution and reduce the number of required measurements.

Findings

Improved reconstruction fidelity with fewer images.

Enhanced resolution surpassing the acoustic diffraction limit.

Performance depends on signal-to-noise ratio and sample sparsity.

Abstract

In deep tissue photoacoustic imaging the spatial resolution is inherently limited by the acoustic wavelength. Recently, it was demonstrated that it is possible to surpass the acoustic diffraction limit by analyzing fluctuations in a set of photoacoustic images obtained under unknown speckle illumination patterns. Here, we purpose an approach to boost reconstruction fidelity and resolution, while reducing the number of acquired images by utilizing a compressed sensing computational reconstruction framework. The approach takes into account prior knowledge of the system response and sparsity of the target structure. We provide proof of principle experiments of the approach and demonstrate that improved performance is obtained when both speckle fluctuations and object priors are used. We numerically study the expected performance as a function of the measurements signal to noise ratio and sample spatial-sparsity. The presented reconstruction framework can be applied to analyze existing photoacoustic experimental datasets containing dynamic fluctuations.