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    Area of Science:

    • Biomedical Imaging
    • Optics
    • Data Science

    Background:

    • Time-resolved fluorescence imaging is crucial for non-invasive biomedical analysis.
    • Large datasets in imaging present challenges for acquisition speed and processing.
    • Existing methods struggle with the high data volume generated.

    Purpose of the Study:

    • To introduce a novel technique for fast giga-voxel 4D hypercube acquisition.
    • To address the limitations of current time-resolved fluorescence imaging methods.
    • To enable efficient collection and processing of complex imaging data.

    Main Methods:

    • A system combining two single-pixel cameras and a 2D array detector operating in parallel.
    • Development of data fusion techniques to integrate data from multiple sensors.
    • Acquisition of a 4D hypercube by measuring only 0.03% of the full dataset.

    Main Results:

    • Successful acquisition of a giga-voxel 4D hypercube with high resolution.
    • Demonstration of a significantly reduced data measurement requirement.
    • Validation of the technique's ability to capture detailed spectral and temporal signatures.

    Conclusions:

    • The novel technique offers a fast and efficient approach to time-resolved fluorescence imaging.
    • Data fusion enables the reconstruction of high-resolution 4D hypercubes from sparse measurements.
    • This method has potential for identifying fluorophore species based on their unique signatures.