Photoacoustic Signal Enhancement: Towards Utilization of Low Energy Laser Diodes in Real-Time Photoacoustic Imaging.

Rayyan Manwar,Karl Kratkiewicz,Matin Hosseinzadeh,Mohammad N Avanaki,Ali Hariri,Shahryar Noei

doi:10.3390/s18103498

Abstract

In practice, photoacoustic (PA) waves generated with cost-effective and low-energy laser diodes, are weak and almost buried in noise. Reconstruction of an artifact-free PA image from noisy measurements requires an effective denoising technique. Averaging is widely used to increase the signal-to-noise ratio (SNR) of PA signals; however, it is time consuming and in the case of very low SNR signals, hundreds to thousands of data acquisition epochs are needed. In this study, we explored the feasibility of using an adaptive and time-efficient filtering method to improve the SNR of PA signals. Our results show that the proposed method increases the SNR of PA signals more efficiently and with much fewer acquisitions, compared to common averaging techniques. Consequently, PA imaging is conducted considerably faster.

Highlights

Photoacoustic (PA) imaging, referred to as optoacoustic (OA) imaging, is a fast-developing technique that utilizes the PA effect to noninvasively visualize the concentration of compartments in biological tissues [1,2,3,4,5,6,7]
To evaluate the proposed denoising method we used a trimmed homogenous black tape strip made of vinyl as the image target
Height and width of our PA images were set to 20 pixels

Summary

Introduction

Photoacoustic (PA) imaging, referred to as optoacoustic (OA) imaging, is a fast-developing technique that utilizes the PA effect to noninvasively visualize the concentration of compartments in biological tissues [1,2,3,4,5,6,7]. In PA imaging, a short-pulsed laser is used to illuminate a biological tissue. The absorbed energy is converted to heat, causing thermoelastic expansion, resulting in ultrasonic waves, which when detected by ultrasound transducers, will be converted to PA signals [5,9]. We call the combination of these unwanted signals, the “measurement noise”, known to complicate the interpretation of resulting

Methods

Results

Conclusion