High performance image mapping spectrometer (IMS) for snapshot hyperspectral imaging applications.

A high performance, snapshot Image Mapping Spectrometer was developed that provides fast image acquisition (100 Hz) of 16 bit hyperspectral data cubes (210x210x46) over a spectral range of 515-842 nm. Essential details of the opto-mechanical design are presented. Spectral accuracy, precision, and image reconstruction metrics such as resolution are discussed. Fluorescently stained cell samples were used to directly compare the data obtained using newly developed and the reference image mapping spectrometer. Additional experimental results are provided to demonstrate the abilities of the new spectrometer to acquire highly-resolved, motion-artifact-free hyperspectral images at high temporal sampling rates.

Light-guide snapshot imaging spectrometer for remote sensing applications.

A fiber-based snapshot imaging spectrometer was developed with a maximum of 31853 (~188 x 170) spatial sampling and 61 spectral channels in the 450nm-750nm range. A compact, custom-fabricated fiber bundle was used to sample the object image at the input and create void spaces between rows at the output for dispersion. The bundle was built using multicore 6x6 fiber block ribbons. To avoid overlap between the cores in the direction of dispersion, we selected a subset of cores using two alternative approaches; a lenslet array and a photomask. To calibrate the >30000 spatial samples of the system, a rapid spatial calibration method was developed based on phase-shifting interferometry (PSI). System crosstalk and spectral resolution were also characterized. Preliminary hyperspectral imaging results of the Rice University campus landscape, obtained with the spectrometer, are presented to demonstrate the system's spectral imaging capability for distant scenes. The spectrum of different plant species with different health conditions, obtained with the spectrometer, was in accordance with reference instrument measurements. We also imaged Houston traffic to demonstrate the system's snapshot hyperspectral imaging capability. Potential applications of the system include terrestrial monitoring, land use, air pollution, water resources, and lightning spectroscopy. The fiber-based system design potentially allows tuning between spatial and spectral sampling to meet specific imaging requirements.

Snapshot Hyperspectral Light-Sheet Imaging of Signal Transduction in Live Pancreatic Islets.

The observation of ionic signaling dynamics in intact pancreatic islets has contributed greatly to our understanding of both α- and ß-cell function. Insulin secretion from ß-cells depends on the firing of action potentials and consequent rises of intracellular calcium activity ([Ca(2+)]i). Zinc (Zn(2+)) is cosecreted with insulin, and has been postulated to play a role in cell-to-cell cross talk within an islet, in particular inhibiting glucagon secretion from α-cells. Thus, measuring [Ca(2+)]i and Zn(2+) dynamics from both α- and ß-cells will elucidate mechanisms underlying islet hormone secretion. [Ca(2+)]i and intracellular Zn(2+) can be measured using fluorescent biosensors, but the most efficient sensors have overlapping spectra that complicate their discrimination. Hyperspectral imaging can be used to distinguish signals from multiple fluorophores, but available hyperspectral implementations are either too slow to measure the dynamics of ionic signals or not suitable for thick samples. We have developed a five-dimensional (x,y,z,t,λ) imaging system that leverages a snapshot hyperspectral imaging method, image mapping spectrometry, and light-sheet microscopy. This system provides subsecond temporal resolution from deep within multicellular structures. Using a single excitation wavelength (488 nm) we acquired images from triply labeled samples with two biosensors and a genetically expressing fluorescent protein (spectrally overlapping with one of the biosensors) with high temporal resolution. Measurements of [Ca(2+)]i and Zn(2+) within both α- and ß-cells as a function of glucose concentration show heterogeneous uptake of Zn(2+) into α-cells that correlates to the known heterogeneities in [Ca(2+)]i. These differences in intracellular Zn(2+) among α-cells may contribute to the inhibition in glucagon secretion observed at elevated glucose levels.

Feasibility of intra-operative image guidance in burn excision surgery with multispectral imaging and deep learning.

BACKGROUND: Exposing a healthy wound bed for skin grafting is an important step during burn surgery to ensure graft take and maintain good functional outcomes. Currently, the removal of non-viable tissue in the burn wound bed during excision is determined by expert clinician judgment. Using a porcine model of tangential burn excision, we investigated the effectiveness of an intraoperative multispectral imaging device combined with artificial intelligence to aid clinician judgment for the excision of non-viable tissue. METHODS: Multispectral imaging data was obtained from serial tangential excisions of thermal burn injuries and used to train a deep learning algorithm to identify the presence and location of non-viable tissue in the wound bed. Following algorithm development, we studied the ability of two surgeons to estimate wound bed viability, both unaided and aided by the imaging device. RESULTS: The deep learning algorithm was 87% accurate in identifying the viability of a burn wound bed. When paired with the surgeons, this device significantly improved their abilities to determine the viability of the wound bed by 25% (p = 0.03). Each time a surgeon changed their decision after seeing the AI model output, it was always a change from an incorrect decision to excise more tissue to a correct decision to stop excision. CONCLUSION: This study provides insight into the feasibility of image-guided burn excision, its effect on surgeon decision making, and suggests further investigation of a real-time imaging system for burn surgery could reduce over-excision of burn wounds.

Clinical Investigation of a Rapid Non-invasive Multispectral Imaging Device Utilizing an Artificial Intelligence Algorithm for Improved Burn Assessment.

Currently, the incorrect judgment of burn depth remains common even among experienced surgeons. Contributing to this problem are change in burn appearance throughout the first week requiring periodic evaluation until a confident diagnosis can be made. To overcome these issues, we investigated the feasibility of an artificial intelligence algorithm trained with multispectral images of burn injuries to predict burn depth rapidly and accurately, including burns of indeterminate depth. In a feasibility study, 406 multispectral images of burns were collected within 72 hours of injury and then serially for up to 7 days. Simultaneously, the subject's clinician indicated whether the burn was of indeterminate depth. The final depth of burned regions within images were agreed upon by a panel of burn practitioners using biopsies and 21-day healing assessments as reference standards. We compared three convolutional neural network architectures and an ensemble in their capability to automatically highlight areas of nonhealing burn regions within images. The top algorithm was the ensemble with 81% sensitivity, 100% specificity, and 97% positive predictive value (PPV). Its sensitivity and PPV were found to increase in a sigmoid shape during the first week postburn, with the inflection point at day 2.5. Additionally, when burns were labeled as indeterminate, the algorithm's sensitivity, specificity, PPV, and negative predictive value were: 70%, 100%, 97%, and 100%. These results suggest multispectral imaging combined with artificial intelligence is feasible for detecting nonhealing burn tissue and could play an important role in aiding the earlier diagnosis of indeterminate burns.

A Dye-Free Analog to Retinal Angiography Using Hyperspectral Unmixing to Retrieve Oxyhemoglobin Abundance.

PURPOSE: Retinal angiography evaluates retinal and choroidal perfusion and vascular integrity and is used to manage many ophthalmic diseases, such as age-related macular degeneration. The most common method, fluorescein angiography (FA), is invasive and can lead to untoward effects. As an emerging replacement, noninvasive OCT angiography (OCTA) is used regularly as a dye-free substitute with superior resolution and additional depth-sectioning abilities; however, general trends in FA as signified by varying intensity in images are not always reproducible in the fine structural detail in an OCTA image stack because of the source of their respective signals, OCT speckle decorrelation versus fluorescein emission. METHODS: We present a noninvasive/dye-free analog to angiography imaging using retinal hyperspectral imaging with a nonscanning spectral imager, the image mapping spectrometer (IMS), to reproduce perfusion-related data based on the abundance of oxyhemoglobin (HbO2) in the retina. With a new unmixing procedure of the IMS-acquired spectral data cubes (350 × 350 × 43), we produced noninvasive HbO2 maps unmixed from reflectance spectra. RESULTS: Here, we present 15 HbO2 maps from seven healthy and eight diseased retinas and compare these maps with corresponding FA and OCTA results with a discussion of each technique. CONCLUSIONS: Our maps showed visual agreement with hypo- and hyperfluorescence trends in venous phase FA images, suggesting that our method provides a new use for hyperspectral imaging as a noninvasive angiography-analog technique and as a complementary technique to OCTA. TRANSLATIONAL RELEVANCE: The application of hyperspectral imaging and spectral analysis can potentially improve/broaden retinal disease screening and enable a noninvasive technique, which complements OCTA.

Lenslet array tunable snapshot imaging spectrometer (LATIS) for hyperspectral fluorescence microscopy.

Snapshot hyperspectral imaging augments pixel dwell time and acquisition speeds over existing scanning systems, making it a powerful tool for fluorescence microscopy. While most snapshot systems contain fixed datacube parameters (x,y,λ), our novel snapshot system, called the lenslet array tunable snapshot imaging spectrometer (LATIS), demonstrates tuning its average spectral resolution from 22.66 nm (80x80x22) to 13.94 nm (88x88x46) over 485 to 660 nm. We also describe a fixed LATIS with a datacube of 200x200x27 for larger field-of-view (FOV) imaging. We report <1 sec exposure times and high resolution fluorescence imaging with minimal artifacts.