Line-scanning hyperspectral imaging based on structured illumination optical sectioning

Coded aperture snapshot hyperspectral light field tomography

Multidimensional imaging has emerged as a powerful technology capable of simultaneously acquiring spatial, spectral, and depth information about a scene. However, existing approaches often rely on mechanical scanning or multi-modal sensing configurations, leading to prolonged acquisition times and increased system complexity. Coded aperture snapshot spectral imaging (CASSI) has introduced compressed sensing to recover three-dimensional (3D) spatial-spectral datacubes from single snapshot two-dimensional (2D) measurements. Despite its advantages, the reconstruction problem remains severely underdetermined due to the high compression ratio, resulting in limited spatial and spectral reconstruction quality. To overcome this challenge, we developed a novel two-stage cascaded compressed sensing scheme called coded aperture snapshot hyperspectral light field tomography (CASH-LIFT). By appropriately distributing the computation load to each stage, this method utilizes the compressibility of natural scenes in multiple domains, reducing the ill-posed nature of datacube recovery and achieving enhanced spatial resolution, suppressed aliasing artifacts, and improved spectral fidelity. Additionally, leveraging the snapshot 3D imaging capability of LIFT, our approach efficiently records a five-dimensional (5D) plenoptic function in a single snapshot.

Hyperspectral imaging (HSI) for meat quality evaluation across the supply chain: Current and future trends

Meat products are particularly plagued by safety problems because of their complicated structure, various production processes and complex supply chains. Rapid and non-invasive analytical methods to evaluate meat quality have become a priority for the industry over the conventional chemical methods. To achieve rapid analysis of safety and quality parameters of meat products, hyperspectral imaging (HSI) is now widely applied in research studies for detecting the various components of different meat products, but its application in meat production and supply chain integrity as a quality control (QC) solution is still ambiguous. This review presents the fresh look at the current states of HSI research as both the scope and the applicability of the HSI in the meat quality evaluation expanded. The future application scenarios of HSI in the supply chain and the future development of HSI hardware and software are also discussed, by which HSI technology has the potential to enable large scale meat product testing. With a fully adapted for factory setting HSI, the inspection coverage can reliably identify the chemical properties of meat products. With the introduction of Food Industry 4.0, HSI advances can change the meat industry to become from reactive to predictive when facing meat safety issues.

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HSI has shown promising early signs in the non-destructive analysis of meat quality and safety.

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Hyperspectral imaging (HSI) is now widely applied in research studies for different meat products with the help of machine learning methods.

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With a fully adapted factory setting and robust machine learning of HSI, the inspection coverage can reach 100% of the target meat.

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HSI can change the meat industry to become from reactive to predictive when facing issues, this will be translated into fewer recalls, less meat fraud, and less waste.

Snapshot hyperspectral light field tomography

We present snapshot hyperspectral light field tomography (Hyper-LIFT), a highly efficient method in recording a 5D (x, y, spatial coordinates; θ, φ, angular coordinates; λ, wavelength) plenoptic function. Using a Dove prism array and a cylindrical lens array, we simultaneously acquire multi-angled 1D en face projections of the object like those in standard sparse-view computed tomography. We further disperse those projections and measure the spectra in parallel using a 2D image sensor. Within a single snapshot, the resultant system can capture a 5D data cube with 270 × 270 × 4 × 4 × 360 voxels. We demonstrated the performance of Hyper-LIFT in imaging spectral volumetric scenes.

Combination of Structured Illumination Microscopy with Hyperspectral Imaging for Cell Analysis

Existing structured illumination microscopy (SIM) allows super-resolution live-cell imaging in few color channels that provide merely morphological information but cannot acquire the sample spectrum that is strongly relevant to the underlying physicochemical property. We develop hyperspectral SIM which enables high-speed spectral super-resolution imaging in SIM for the first time. Through optically mapping the three-dimensional (x, y, and λ) datacube of the sample to the detector plane, hyperspectral SIM allows snapshot spectral imaging of the SIM raw image, detecting the sample spectrum while retaining the high-speed and super-resolution characteristics of SIM. We demonstrate hyperspectral SIM imaging and reconstruct a datacube containing 31 super-resolution images of different wavelengths from only 9 exposures, achieving a 15 nm spectral resolution. We show time-lapse hyperspectral SIM imaging that achieves an imaging speed of 2.7 s per datacube-31-fold faster than the existing wavelength scanning strategy. To demonstrate the great prospects for further combining hyperspectral SIM with various spectral analysis methods, we also perform spectral unmixing of the hyperspectral SIM result while imaging the spectrally overlapped sample.

DMD-based hyperspectral microscopy with flexible multiline parallel scanning

As one of the most common hyperspectral microscopy (HSM) techniques, line-scanning HSM is currently utilized in many fields. However, its scanning efficiency is still considered to be inadequate since many biological and chemical processes occur too rapidly to be captured. Accordingly, in this work, a digital micromirror device (DMD) based on microelectromechanical systems (MEMS) is utilized to demonstrate a flexible multiline scanning HSM system. To the best of our knowledge, this is the first line-scanning HSM system in which the number of scanning lines N can be tuned by simply changing the DMD's parallel scanning units according to diverse applications. This brilliant strategy of effortless adjustability relies only on on-chip scanning methods and totally exploits the benefits of parallelization, aiming to achieve nearly an N-time improvement in the detection efficiency and an N-time decrease in the scanning time and data volume compared with the single-line method under the same operating conditions. To validate this, we selected a few samples of different spectral wavebands to perform reflection imaging, transmission imaging, and fluorescence imaging with varying numbers of scanning lines. The results show the great potential of our DMD-based HSM system for the rapid development of cellular biology, material analysis, and so on. In addition, its on-chip scanning process eliminates the inherent microscopic architecture, making the whole system compact, lightweight, portable, and not subject to site constraints.

Hyperspectral two-photon excitation microscopy using visible wavelength

We demonstrate hyperspectral imaging by visible-wavelength two-photon excitation microscopy using line illumination and slit-confocal detection. A femtosecond pulsed laser light at 530 nm was used for the simultaneous excitation of fluorescent proteins with different emission wavelengths. The use of line illumination enabled efficient detection of hyperspectral images and achieved simultaneous detection of three fluorescence spectra in the observation of living HeLa cells with an exposure time of 1 ms per line, which is equivalent to about 2 µs per pixel in point scanning, with 160 data points per spectrum. On combining linear spectral unmixing techniques, localization of fluorescent probes in the cells was achieved. A theoretical investigation of the imaging property revealed high-depth discrimination property attained through the combination of nonlinear excitation and slit detection.

Development of a fast calibration method for image mapping spectrometry

An image mapping spectrometer (IMS) is a snapshot hyperspectral imager that simultaneously captures both the spatial (x, y) and spectral (λ) information of incoming light. The IMS maps a three-dimensional (3D) datacube (x, y, λ) to a two-dimensional (2D) detector array (x, y) for parallel measurement. To reconstruct the original 3D datacube, one must construct a lookup table that connects voxels in the datacube and pixels in the raw image. Previous calibration methods suffer from either low speed or poor image quality. We herein present a slit-scan calibration method that can significantly reduce the calibration time while maintaining high accuracy. Moreover, we quantitatively analyzed the major artifact in the IMS, the striped image, and developed three numerical methods to correct for it.

Comparing mapping and direct hyperspectral imaging in stand-off Raman spectroscopy for remote material identification

Stand-off Raman spectroscopy offers a highly selective technique to probe unknown substances from a safe distance. Often, it is necessary to scan large areas of interest. This can be done by pointwise imaging (PI), that is, spectra are sequentially acquired from an array of points over the region of interest (point-by-point mapping). Alternatively, in this paper a direct hyperspectral Raman imager is presented, where a defocused laser beam illuminates a wide area of the sample and the Raman scattered light is collected from the whole field of view (FOV) at once as a spectral snapshot filtered by a liquid crystal tunable filter to select a specific Raman shift. Both techniques are compared in terms of achievable FOV, spectral resolution, signal-to-noise performance, and time consumption during a measurement at stand-off distance of 15 m. The HSRI showed superior spectral resolution and signal-to-noise ratio, while more than doubling the FOV of the PI at laser power densities reduced by a factor of 277 at the target. Further, the output hyperspectral image data cube can be processed with state of the art chemometric algorithms like vertex component analysis in order to get a simple deterministic false color image showing the chemical composition of the target. This is shown for an artificial polymer sample, measured at a distance of 15 m.

Optimization of wavelength selection for multispectral image acquisition: a case study of atrial ablation lesions

In vivo autofluorescence hyperspectral imaging of moving objects can be challenging due to motion artifacts and to the limited amount of acquired photons. To address both limitations, we selectively reduced the number of spectral bands while maintaining accurate target identification. Several downsampling approaches were applied to data obtained from the atrial tissue of adult pigs with sites of radiofrequency ablation lesions. Standard image qualifiers such as the mean square error, the peak signal-to-noise ratio, the structural similarity index map, and an accuracy index of lesion component images were used to quantify the effects of spectral binning, an increased spectral distance between individual bands, as well as random combinations of spectral bands. Results point to several quantitative strategies for deriving combinations of a small number of spectral bands that can successfully detect target tissue. Insights from our studies can be applied to a wide range of applications.