Model-Based Photoacoustic Image Reconstruction using Compressed Sensing and Smoothed L0 Norm

TL;DR

This paper introduces a novel photoacoustic image reconstruction method combining compressed sensing with a smoothed L0 norm, significantly reducing transducer requirements while improving image quality over traditional algorithms.

Contribution

It proposes using the smoothed L0 norm within a model-based compressed sensing framework for photoacoustic imaging, enhancing image quality with fewer transducers.

Findings

SL0 outperforms iterative reconstruction and basis pursuit in image quality.

SL0 achieves approximately double the peak-signal-to-noise ratio of basis pursuit.

Fewer transducers are needed without compromising image accuracy.

Abstract

Photoacoustic imaging (PAI) is a novel medical imaging modality that uses the advantages of the spatial resolution of ultrasound imaging and the high contrast of pure optical imaging. Analytical algorithms are usually employed to reconstruct the photoacoustic (PA) images as a result of their simple implementation. However, they provide a low accurate image. Model-based (MB) algorithms are used to improve the image quality and accuracy while a large number of transducers and data acquisition are needed. In this paper, we have combined the theory of compressed sensing (CS) with MB algorithms to reduce the number of transducer. Smoothed version of L0-norm (SL0) was proposed as the reconstruction method, and it was compared with simple iterative reconstruction (IR) and basis pursuit. The results show that S$\ell_0$ provides a higher image quality in comparison with other methods while a low number of transducers were. Quantitative comparison demonstrates that, at the same condition, the SL0 leads to a peak-signal-to-noise ratio for about two times of the basis pursuit.