Photoacoustic image improvement based on a combination of sparse coding and filtering.

Ebrahim Najafzadeh,Mohammad Mehrmohammadi,Saeedeh N Lavasani,Hossein Ghadiri,Parastoo Farnia,Alireza Ahmadian,Maryam Basij,Yan Yan

doi:10.1117/1.jbo.25.10.106001

Abstract

.Significance: Photoacoustic imaging (PAI) has been greatly developed in a broad range of diagnostic applications. The efficiency of light to sound conversion in PAI is limited by the ubiquitous noise arising from the tissue background, leading to a low signal-to-noise ratio (SNR), and thus a poor quality of images. Frame averaging has been widely used to reduce the noise; however, it compromises the temporal resolution of PAI.Aim: We propose an approach for photoacoustic (PA) signal denoising based on a combination of low-pass filtering and sparse coding (LPFSC).Approach: LPFSC method is based on the fact that PA signal can be modeled as the sum of low frequency and sparse components, which allows for the reduction of noise levels using a hybrid alternating direction method of multipliers in an optimization process.Results: LPFSC method was evaluated using in-silico and experimental phantoms. The results show a 26% improvement in the peak SNR of PA signal compared to the averaging method for in-silico data. On average, LPFSC method offers a 63% improvement in the image contrast-to-noise ratio and a 33% improvement in the structural similarity index compared to the averaging method for objects located at three different depths, ranging from 10 to 20 mm, in a porcine tissue phantom.Conclusions: The proposed method is an effective tool for PA signal denoising, whereas it ultimately improves the quality of reconstructed images, especially at higher depths, without limiting the image acquisition speed.

Highlights

IntroductionIn the last two decades, photoacoustic imaging (PAI) as a non-invasive hybrid imaging modality has been used in a wide range of preclinical and clinical applications, such as functional brain mapping,[1,2] molecular imaging,[3,4] cancer diagnosis and staging,[5,6,7,8] tissue vasculature imaging,[9,10,11,12] guiding interventional procedures,[13,14] and dental health.[15,16] PAI detects the optical absorption contrast in tissue through the conversion of light to heat and thermoelastic effect, leading to the generation of acoustic waves.[17,18,19] When the tissue is illuminated by short light pulses, the endogenous chromophores, such as hemoglobin, generate a photoacoustic (PA) signal due to their optical absorption.[20,21] In this procedure, the light energy is transformed into acoustic waves, and the efficacy of this conversion is often affected by the presence of noise arising from the surrounding background.[22,23] the PA signal is often mixed by background noise, including thermalacoustic noise in the medium as well as the transducer and electronic noises.[24]
Najafzadeh et al.: Photoacoustic image improvement based on a combination of sparse coding
For the first time, we proposed an approach for PA signals denoising based on a combination of low-pass filtering and total variation (TV) denoising, allowing for using a hybrid alternating direction method of multipliers (ADMM) in the optimization processes

Summary

Introduction

In the last two decades, photoacoustic imaging (PAI) as a non-invasive hybrid imaging modality has been used in a wide range of preclinical and clinical applications, such as functional brain mapping,[1,2] molecular imaging,[3,4] cancer diagnosis and staging,[5,6,7,8] tissue vasculature imaging,[9,10,11,12] guiding interventional procedures,[13,14] and dental health.[15,16] PAI detects the optical absorption contrast in tissue through the conversion of light to heat and thermoelastic effect, leading to the generation of acoustic waves.[17,18,19] When the tissue is illuminated by short light pulses, the endogenous chromophores, such as hemoglobin, generate a photoacoustic (PA) signal due to their optical absorption.[20,21] In this procedure, the light energy is transformed into acoustic waves, and the efficacy of this conversion is often affected by the presence of noise arising from the surrounding background.[22,23] the PA signal is often mixed by background noise, including thermalacoustic noise in the medium as well as the transducer and electronic noises.[24]. The combination of these different types of noise in the PA signal leads to a low signal-to-noise ratio (SNR) and results in a poor quality reconstructed PA image.[27,28,29,30]

Objectives

Methods

Results

Conclusion