Dc-EEMF: Pushing depth-of-field limit of photoacoustic microscopy via decision-level constrained learning
Journal:
arXiv
Published Date:
May 29, 2025
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.
