arXiv 2506.031812025-05-29eess.IVcs.CV

Dc-EEMF：通过决策级约束学习突破光声显微镜的景深极限

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

Center for Biomedical-photonics and Molecular Imaging（生物医学光子学与分子成像中心）；Xi’an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information（西安智能感知与跨尺度生命信息调节重点实验室）；School of Life Science and Technology（生命科学与技术学院）；School of Computer Science and Engineering（计算机科学与工程学院）；Faculty of Health Sciences（健康科学学院）；Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing)（致癌研究与转化医学实验室（教育部/北京））

Wangting Zhou, Jiangshan He, Tong Cai, Lin Wang, Zhen Yuan, Xunbin Wei, Xueli Chen

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AI总结：

本文提出决策级约束的端到端多焦点图像融合方法Dc-EEMF，通过轻量级孪生网络和U-Net感知损失突破光声显微镜景深限制，在不显著牺牲横向分辨率的情况下实现高质量融合。

摘要：

光声显微镜具有无需标记即可测量生物标志物结构和功能状态的潜力，这极大地有助于理解生物医学研究中的病理生理状况。然而，传统的光学分辨率光声显微镜（OR-PAM）由于聚焦于高斯光束的深度范围较窄，景深（DoF）有限，因而无法在深度方向上分辨足够的细节。在此，我们提出了一种决策级约束的端到端多焦点图像融合方法（Dc-EEMF），以突破光声显微镜的景深极限。Dc-EEMF方法是一种轻量级孪生网络，采用抗伪影的通道级空间频率作为其特征融合规则。精心设计的基于U-Net的感知损失函数用于端到端融合中的决策级焦点特性，将空间域方法和变换域方法的互补优势无缝集成到Dc-EEMF中。该方法可以进行端到端训练，无需后处理程序。实验结果和数值分析共同证明了我们方法的稳健性能，在不显著牺牲横向分辨率的情况下，为光声显微镜图像实现了令人印象深刻的融合结果。利用Dc-EEMF增强的光声显微镜有潜力成为临床前和临床研究中需要扩展景深的各种应用的实用工具。

英文摘要：

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.

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