Fluorescence microscopy is an indispensable tool in biological research and has been used extensively in widespread applications [1
]. The continuing development of fluorescent probes bringing new spectral characteristics and new functional properties for cellular labeling has widened the possibilities of what biological features can be measured by a microscope. Taking full advantage of their properties, however, often requires imaging systems have a spectral resolving power to discriminate those fluorescent probes which have significant spectral overlaps.
Hyperspectral imaging is a hybrid imaging modality that combines the advantages of traditional imaging cameras and spectrometers [2
]. It measures sample’s spatial and spectral information, and constructs a 3D (x, y, λ
) datacube for spectral unmixing analysis [3
]. However, most hyperspectral imagers are scanning-based systems. They either scan in the spatial domain, e.g. hyperspectral confocal microscope [4
] and slit-scanning microscope [5
], or scan in the spectral domain, e.g., liquid crystal tunable filters or acoustic optic tunable filters [6
]. Because scanning-based systems cannot collect light from all voxels of the datacube in parallel, there is a loss of light throughput by a factor of N
when measuring N
scan elements. To overcome this limitation, snapshot hyperspectral imagers such as Computed Tomography Imaging Spectrometer (CTIS) [7
], Coded Aperture Snapshot Spectral Imager (CASSI) [8
] and Image-Replicating Imaging Spectrometer (IRIS) [9
] have been developed. Although the spectral imaging capabilities of these snapshot hyperspectral imagers have been demonstrated, they suffer many problems, e.g., CTIS and CASSI require extensive computational cost, while IRIS is limited in the number of spectral bands which can acquire at high spatial resolution.
The Image Mapping Spectrometer (IMS) is a novel snapshot hyperspectral imager that is developed for full-throughput measurement of spectrally resolved scenes [10
]. It replaces the regular camera in a digital imaging system, allowing one to add spectrum acquisition capability to a variety of imaging modalities, such as microscopy [10
] and endoscopy [12
]. The operation of the IMS is based on the image mapping principle [10
]. Briefly, a custom-fabricated optical component – termed image mapper – is utilized to remap a sample’s 3D (x, y, λ
) datacube onto a 2D CCD camera, so that a sample’s spatial and spectral information can be measured in parallel. Since no scanning is employed, the IMS features high-speed (currently up to 7.2 fps [12
]) (x, y, λ
) datacube acquisition without sacrificing spatial and spectral resolution. In addition, since the IMS is a direct imaging device, little computational cost is required to reconstruct a (x, y, λ
Previous IMS-based experiments were implemented in wide-field imaging, but for imaging volumetric samples such as biological tissues, wide-field measurements suffer from spatial-spectral crosstalk due to light contributed from out-of-focus layers. This crosstalk decreases the ability to resolve all voxels in the datacube and compromises spectral unmixing capability. To address this problem, here we add optical sectioning capability to the IMS with structured illumination (SI). By acquiring three (x, y, λ
phase-shifted sinusoid illumination patterns and subsequent demodulation, the IMS achieves ~1 µm axial resolution for the acquired spectral channel images.
Although the throughput of the IMS is halved by the added sectioning hardware (see Section 2), high contrast depth-resolved spectral channel images were still acquired in a mouse kidney tissue fluorescence imaging experiment (see Section 3). In addition, due to the added sectioning capability, a 4D (x, y, z, λ) datacube is also successfully acquired by the IMS. The 4D imaging results demonstrate that no artifacts are introduced by integrating traditional 3D (x, y, z) imaging with IMS’ multispectral imaging.
To show the advantages of depth-resolved IMS over other commercially available multispectral imagers, we quantitatively compare the depth-resolved IMS with a hyperspectral confocal microscope (HCM) (Zeiss Meta 510) in the context of photon collection and image signal-to-noise ratio (SNR) (see Section 4). The experimental results indicate that, at a given frame rate, the depth-resolved IMS surpasses the corresponding HCM approach 130 times in the photon collection, and 2.6 times in the image SNR respectively.