Near-infrared (NIR) fluorescence lifetime imaging (FLI) provides a unique contrast mechanism to monitor biological parameters and molecular events in vivo. Single-photon avalanche diode (SPAD) cameras have been recently demonstrated in FLI microscopy (FLIM) applications, but their suitability for in vivo macroscopic FLI (MFLI) in deep tissues remains to be demonstrated. Herein, we report in vivo NIR MFLI measurement with SwissSPAD2, a large time-gated SPAD camera. We first benchmark its performance in well-controlled in vitro experiments, ranging from monitoring environmental effects on fluorescence lifetime, to quantifying Förster resonant energy transfer (FRET) between dyes. Next, we use it for in vivo studies of target-drug engagement in live and intact tumor xenografts using FRET. Information obtained with SwissSPAD2 was successfully compared to that obtained with a gated intensified charge-coupled device (ICCD) camera, using two different approaches. Our results demonstrate that SPAD cameras offer a powerful technology for in vivo preclinical applications in the NIR window.

Significance: Deep learning (DL) models are being increasingly developed to map sensor data to the image domain directly. However, DL methodologies are data-driven and require large and diverse data sets to provide robust and accurate image formation performances. For research modalities such as 2D/3D diffuse optical imaging, the lack of large publicly available data sets and the wide variety of instrumentation designs, data types, and applications leads to unique challenges in obtaining well-controlled data sets for training and validation. Meanwhile, great efforts over the last four decades have focused on developing accurate and computationally efficient light propagation models that are flexible enough to simulate a wide variety of experimental conditions.Aim: Recent developments in Monte Carlo (MC)-based modeling offer the unique advantage of simulating accurately light propagation spatially, temporally, and over an extensive range of optical parameters, including minimally to highly scattering tissue within a computationally efficient platform. Herein, we demonstrate how such MC platforms, namely "Monte Carlo eXtreme" and "Mesh-based Monte Carlo," can be leveraged to generate large and representative data sets for training the DL model efficiently. Approach:We propose data generator pipeline strategies using these platforms and demonstrate their potential in fluorescence optical topography, fluorescence optical tomography, and singlepixel diffuse optical tomography. These applications represent a large variety in instrumentation design, sample properties, and contrast function.Results: DL models trained using the MC-based in silico datasets, validated further with experimental data not used during training, show accurate and promising results. Conclusion:Overall, these MC-based data generation pipelines are expected to support the development of DL models for rapid, robust, and user-friendly image formation in a wide variety of applications.

We report on the system design and instrumental characteristics of a novel time-domain mesoscopic fluorescence molecular tomography (TD-MFMT) system for multiplexed molecular imaging in turbid media. The system is equipped with a supercontinuum pulsed laser for broad spectral excitation, based on a high-density descanned raster scanning intensity-based acquisition for 2D and 3D imaging and augmented with a high-dynamical range linear time-resolved single-photon avalanche diode (SPAD) array for lifetime quantification. We report on the system’s spatio-temporal and spectral characteristics and its sensitivity and specificity in controlled experimental settings. Also, a phantom study is undertaken to test the performance of the system to image deeply-seated fluorescence inclusions in tissue-like media. In addition, ex vivo tumor xenograft imaging is performed to validate the system’s applicability to the biological sample. The characterization results manifest the capability to sense small fluorescence concentrations (on the order of nanomolar) while quantifying fluorescence lifetimes and lifetime-based parameters at high resolution. The phantom results demonstrate the system’s potential to perform 3D multiplexed imaging thanks to spectral and lifetime contrast in the mesoscopic range (at millimeters depth). The ex vivo imaging exhibits the prospect of TD-MFMT to resolve intra-tumoral heterogeneity in a depth-dependent manner.

Early spontaneous detection of thrombin activation benefits precise theranostics for thrombotic vascular disease. Herein, a thrombin-responsive nanoprobe conjugated by a FITC dye, PEGylated Fe 3 O 4 nanoparticles, and a thrombin-sensitive peptide (LASG) was constructed to visualize thrombin activation and subsequent thrombosis in vivo. The FITC dye was linked to the LASG coated on the Fe 3 O 4 nanoparticles for sensing the thrombin activity via the Forster resonance energy transfer effect. In vitro fluorescence imaging showed that the fluorescence signal intensity increased significantly after incubation with thrombin in contrast to that of the control group (p < 0.05), and the signal intensity was enhanced with the increase in thrombin concentration. Further in vivo fluorescence imaging also revealed that the signal elevated markedly in the left common carotid artery (LCCA) lesion of the mice thrombosis model after nanoprobe injection, in contrast to that of the control + nanoprobe group (p < 0.05). Moreover, the thrombin inhibitor bivalirudin could decrease the filling defect of the LCCA. Three-dimensional fusion images of micro-CT and fluorescence confirmed that filling defects in the LCCA were nicely colocalized with fluorescence signal caused by nanoprobes. The nanoplatform based on a thrombin-activatable visualization system could provide smart responsive and dynamic imaging of thrombosis in vivo.

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