用深度学习拓展光声显微镜的焦深，让深层结构更清晰。
Dc-EEMF: Pushing depth-of-field limit of photoacoustic microscopy via decision-level constrained learning
- 基于决策层约束的轻量级双流网络，融合多焦点图像。
- 焦深显著扩展，横向分辨率损失小，无需后处理。
- 适合需要大焦深的生物医学成像研究，如临床前实验。
光声显微镜可无标记地测量生物标志物的结构与功能状态，对生物医学研究具有重要意义。然而，传统光学分辨率光声显微镜（OR-PAM）受限于高斯光束的窄焦深，难以在深度方向解析足够细节。为此，本文提出决策层约束的端到端多焦点图像融合方法（Dc-EEMF），采用轻量级孪生网络，结合抗伪影的通道级空间频率特征融合规则，并设计基于U-Net的感知损失函数，融合空域与变换域优势。该方法可端到端训练，无需后处理。实验与数值分析表明，该方法显著扩展了焦深，保持了良好的横向分辨率，为需要大焦深的临床前与临床研究提供了实用工具。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。