Yuan Nanxue, Ragab Saif, Nizam Navid, Pandey Vikas, Verma Amit, Young Tynan, Williams John, Barroso Margarida, Intes Xavier
Center for modeling, simulation and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, New York 12180, USA.
Department of Molecular and Cellular Physiology, Albany Medical College, Albany, New York 12208, USA.
bioRxiv. 2025 Jun 15:2025.06.10.658928. doi: 10.1101/2025.06.10.658928.
Macroscopic Fluorescence Lifetime Imaging (MFLI) is a powerful, non-invasive imaging modality that offers robust, physiologically relevant contrast largely independent of fluorophore concentration, excitation intensity, and tissue signal attenuation. However, accurately determining the depth of signal origin remains challenging, potentially leading to ambiguity in biological interpretation. Here, we present a novel optical correction method that effectively eliminates surface signal bias, such as that from skin in preclinical imaging, without the need for chemical clearance. This advancement supports the robust applicability of MFLI in translational research.
Establishment of a High Spatial Frequency-Fluorescence Lifetime Imaging (HSF-FLI) framework to selectively isolate subsurface fluorescence (deeper signals) from surface fluorescence, while preserving the accuracy of lifetime estimation.
A modulation transfer function (MTF) that relates spatial frequency to penetration depth was derived using Monte Carlo eXtreme (MCX) simulations (for physics-based modeling) and validated with agar-based capillary phantoms on a time-gated ICCD-DMD system. Depth-independent fluorescence was decomposed into surface and subsurface components through structured three-phase sinusoidal illumination, and nonlinear least squares fitting was applied to recover lifetime or lifetime based parameters maps. HSF-FLI was demonstrated in vivo in mouse models bearing tumor xenogratfs and was cross validated with ex vivo measurements.
We extensively characterized the performance of High Spatial Frequency-Fluorescence Lifetime Imaging (HSF-FLI) through simulations and tissue-mimicking phantoms. The approach was further validated in vivo by assessing drug delivery in preclinical models using MFLI-FRET (Förster Resonance Energy Transfer).
By coupling structured illumination with physics-based depth modeling, HSF-FLI delivers accurate, depth-selective lifetime readouts, setting the stage for robust and fast FLI implementation in translational studies.
宏观荧光寿命成像（MFLI）是一种强大的非侵入性成像方式，它能提供强大的、与生理相关的对比度，很大程度上独立于荧光团浓度、激发强度和组织信号衰减。然而，准确确定信号源的深度仍然具有挑战性，这可能导致生物学解释的模糊性。在此，我们提出一种新颖的光学校正方法，该方法可有效消除表面信号偏差，如临床前成像中皮肤产生的偏差，而无需化学清除。这一进展支持了MFLI在转化研究中的强大适用性。
建立一个高空间频率 - 荧光寿命成像（HSF - FLI）框架，以从表面荧光中选择性分离出皮下荧光（更深层信号），同时保持寿命估计的准确性。
使用蒙特卡罗极限（MCX）模拟（用于基于物理的建模）推导了一个将空间频率与穿透深度相关联的调制传递函数（MTF），并在时间选通ICCD - DMD系统上用基于琼脂的毛细管模型进行了验证。通过结构化的三相正弦照明将与深度无关的荧光分解为表面和皮下成分，并应用非线性最小二乘法拟合来恢复寿命或基于寿命的参数图。HSF - FLI在携带肿瘤异种移植的小鼠模型中进行了体内验证，并与离体测量进行了交叉验证。
我们通过模拟和组织模拟模型广泛表征了高空间频率 - 荧光寿命成像（HSF - FLI）的性能。该方法通过使用MFLI - FRET（Förster共振能量转移）评估临床前模型中的药物递送在体内进一步得到验证。
通过将结构化照明与基于物理的深度建模相结合，HSF - FLI提供了准确的、深度选择性的寿命读数，为转化研究中强大而快速的FLI实施奠定了基础。
