Image enhancement in acoustic-resolution photoacoustic microscopy enabled by a novel directional algorithm

TL;DR

This paper introduces a novel directional algorithm combining Fourier accumulation and model-based deconvolution to significantly improve resolution, SNR, and fidelity in acoustic-resolution photoacoustic microscopy, enabling better microvascular imaging.

Contribution

The proposed FA-SAFT and D-MB deconvolution algorithm enhances AR-PAM images by addressing resolution, SNR, and fidelity limitations of previous methods, especially for in vivo applications.

Findings

Achieved 26-31 um resolution over 1.8 mm DOF

Resolved microvasculature with high fidelity in vivo

Demonstrated improved image quality in complex structures

Abstract

Acoustic-resolution photoacoustic microscopy (AR-PAM) is a promising tool for microvascular imaging. In the focal region, resolution of AR-PAM is determined by the ultrasound transducer and ultimately limited by acoustic diffraction. In the out-of-focus region, resolution deteriorates with increasing distance from the focal plane, which restricts depth of focus (DOF). Besides, a trade-off exists between resolution and DOF. Previously, synthetic aperture focusing technique (SAFT) and/or deconvolution methods have been demonstrated to enhance AR-PAM images. However, they suffer from issues in low resolution, low signal-to-noise ratio (SNR), and/or poor image fidelity. Here, we propose a novel algorithm for AR-PAM to enhance image resolution, SNR, and fidelity. The algorithm consists of a Fourier accumulation SAFT (FA-SAFT) and a directional model-based (D-MB) deconvolution method. Inspired from Fourier denoising technique and directional SAFT, FA-SAFT mainly compensates for the defocusing effect. Besides, D-MB deconvolution enhances the resolution as well as preserves the image fidelity, especially for the objects with line patterns such as microvasculature. Full width at half maximum of 26-31 um over DOF of 1.8 mm and minimum resolvable distance of 46-49 um are experimentally achieved by imaging tungsten wire phantom. Moreover, imaging of leaf skeleton phantom and in vivo imaging of mouse blood vessels also prove that our algorithm is capable of providing high-resolution, high-SNR, and good-fidelity results for complex structures and for in vivo applications.