Dc-EEMF: Pushing depth-of-field limit of photoacoustic microscopy via decision-level constrained learning

TL;DR

This paper introduces Dc-EEMF, a novel deep learning-based image fusion method that significantly extends the depth-of-field in photoacoustic microscopy, enabling clearer imaging of deeper structures without sacrificing lateral resolution.

Contribution

The paper presents a lightweight siamese network with a U-Net-based perceptual loss for end-to-end multi-focus image fusion in PAM, pushing the depth-of-field limit effectively.

Findings

Achieves robust fusion of PAM images with extended DoF.

Maintains lateral resolution while increasing depth-of-field.

Demonstrates superior performance over existing methods.

Abstract

Photoacoustic microscopy holds the potential to measure biomarkers' structural and functional status without labels, which significantly aids in comprehending pathophysiological conditions in biomedical research. However, conventional optical-resolution photoacoustic microscopy (OR-PAM) is hindered by a limited depth-of-field (DoF) due to the narrow depth range focused on a Gaussian beam. Consequently, it fails to resolve sufficient details in the depth direction. Herein, we propose a decision-level constrained end-to-end multi-focus image fusion (Dc-EEMF) to push DoF limit of PAM. The DC-EEMF method is a lightweight siamese network that incorporates an artifact-resistant channel-wise spatial frequency as its feature fusion rule. The meticulously crafted U-Net-based perceptual loss function for decision-level focus properties in end-to-end fusion seamlessly integrates the complementary advantages of spatial domain and transform domain methods within Dc-EEMF. This approach can be trained end-to-end without necessitating post-processing procedures. Experimental results and numerical analyses collectively demonstrate our method's robust performance, achieving an impressive fusion result for PAM images without a substantial sacrifice in lateral resolution. The utilization of Dc-EEMF-powered PAM has the potential to serve as a practical tool in preclinical and clinical studies requiring extended DoF for various applications.