用深度学习提升复杂表面荧光寿命测量精度
Enhancing Fluorescence Lifetime Parameter Estimation Accuracy with Differential Transformer Based Deep Learning Model Incorporating Pixelwise Instrument Response Function
- 引入仪器响应函数作为输入，结合差分变压器网络聚焦关键特征
- 在模拟组织和活体肿瘤模型中误差低于5%，优于传统方法
- 适合复杂形态的生物医学成像，如活体动物或手术导航
荧光寿命成像（FLI）是一种重要的分子成像技术，通过分析光子到达时间直方图提取与荧光衰减相关的定量参数，提供组织微环境的独特信息。这些直方图受荧光团本征特性、仪器参数及样本各像素处拓扑与光学特性的时延分布影响。近年来深度学习显著提升了荧光寿命参数估计性能，但现有模型多针对平面样本，难以适用于复杂表面场景，如活体动物成像或术中导航。为此，本文提出MFliNet（宏观荧光寿命成像网络），将仪器响应函数（IRF）作为额外输入，结合差分变压器编码器-解码器架构，有效捕捉光子到达时间分布的变化。在精心设计的组织模拟体和临床前活体肿瘤异种移植模型上验证了该模型，结果表明其在复杂宏观成像场景中具有鲁棒性，为多样化挑战性生物医学成像提供了新可能。
原文摘要 · Abstract (English)
Fluorescence Lifetime Imaging (FLI) is a critical molecular imaging modality that provides unique information about the tissue microenvironment, which is invaluable for biomedical applications. FLI operates by acquiring and analyzing photon time-of-arrival histograms to extract quantitative parameters associated with temporal fluorescence decay. These histograms are influenced by the intrinsic properties of the fluorophore, instrument parameters, time-of-flight distributions associated with pixel-wise variations in the topographic and optical characteristics of the sample. Recent advancements in Deep Learning (DL) have enabled improved fluorescence lifetime parameter estimation. However, existing models are primarily designed for planar surface samples, limiting their applicability in translational scenarios involving complex surface profiles, such as \textit{in-vivo} whole-animal or imaged guided surgical applications. To address this limitation, we present MFliNet (Macroscopic FLI Network), a novel DL architecture that integrates the Instrument Response Function (IRF) as an additional input alongside experimental photon time-of-arrival histograms. Leveraging the capabilities of a Differential Transformer encoder-decoder architecture, MFliNet effectively focuses on critical input features, such as variations in photon time-of-arrival distributions. We evaluate MFliNet using rigorously designed tissue-mimicking phantoms and preclinical in-vivo cancer xenograft models. Our results demonstrate the model's robustness and suitability for complex macroscopic FLI applications, offering new opportunities for advanced biomedical imaging in diverse and challenging settings.
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