用物理约束神经网络解决光声成像中染料浓度难估算问题
Optical Inversion and Spectral Unmixing of Spectroscopic Photoacoustic Images with Physics-Informed Neural Networks
- 基于物理约束的神经网络，无需假设线性关系
- 在活体小鼠淋巴结图像上实现更优像素重建与浓度估计
- 适合生物医学成像中需要精准分子分析的研究者
光谱光声成像（sPA）中准确估计染色体相对浓度可揭示生理过程的结构、功能和分子信息。但由于成像固有的非线性和病态性，浓度估计难以实现。本文提出的SPOI-AE方法不依赖线性假设，直接解决sPA光学反演与光谱解混问题。该模型在无真实浓度标签的活体小鼠淋巴结sPA图像上训练与测试，其重建性能优于传统算法，并能提供具有生物学合理性的光学参数、染色体浓度及组织氧饱和度估计。通过模拟小鼠淋巴结幻影的真值验证了其解混精度。
原文摘要 · Abstract (English)
Accurate estimation of the relative concentrations of chromophores in a spectroscopic photoacoustic (sPA) image can reveal immense structural, functional, and molecular information about physiological processes. However, due to nonlinearities and ill-posedness inherent to sPA imaging, concentration estimation is intractable. The Spectroscopic Photoacoustic Optical Inversion Autoencoder (SPOI-AE) aims to address the sPA optical inversion and spectral unmixing problems without assuming linearity. Herein, SPOI-AE was trained and tested on \textit{in vivo} mouse lymph node sPA images with unknown ground truth chromophore concentrations. SPOI-AE better reconstructs input sPA pixels than conventional algorithms while providing biologically coherent estimates for optical parameters, chromophore concentrations, and the percent oxygen saturation of tissue. SPOI-AE's unmixing accuracy was validated using a simulated mouse lymph node phantom ground truth.
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