Medical hyperspectral imaging: a review

Cutaneous Imaging Technologies in Acute Burn and Chronic Wound Care

BACKGROUND

Wound assessment relies on visual evaluation by physicians. Such assessment is largely subjective and presents the opportunity to explore the use of emergent technologies.

METHODS

Emergent and powerful noninvasive imaging technologies applicable to assess burn and chronic wounds are reviewed.

RESULTS

The need to estimate wound depth is critical in both chronic wound and burn injury settings. Harmonic ultrasound technology is powerful to study wound depth. It addresses the limitations of optical imaging with limited depth of penetration. What if a wound appears epithelialized by visual inspection, which shows no discharge yet is covered by repaired skin that lacks barrier function? In this case although the wound is closed as defined by current standards, it remains functionally open, presenting the risk of infection and other postclosure complications. Thus, assessment of skin barrier function is valuable in the context of assessing wound closure. Options for the study of tissue vascularization are many. If noncontact and noninvasive criteria are of importance, laser speckle imaging is powerful. Fluorescence imaging is standard in several clinical settings and is likely to serve the wound clinics well as long as indocyanine green injection is not of concern. A major advantage of harmonic ultrasound imaging of wound depth is that the same system is capable of providing information on blood flow dynamics in arterial perforators.

CONCLUSION

With many productive imaging platforms to choose from, wound care is about to be transformed by technology that would help assess wound severity.

A graphene-based Fabry-Pérot spectrometer in mid-infrared region

Mid-infrared spectroscopy is of great importance in many areas and its integration with thin-film technology can economically enrich the functionalities of many existing devices. In this paper we propose a graphene-based ultra-compact spectrometer (several micrometers in size) that is compatible with complementary metal-oxide-semiconductor (CMOS) processing. The proposed structure uses a monolayer graphene as a mid-infrared surface waveguide, whose optical response is spatially modulated using electric fields to form a Fabry-Pérot cavity. By varying the voltage acting on the cavity, we can control the transmitted wavelength of the spectrometer at room temperature. This design has potential applications in the graphene-silicon-based optoelectronic devices as it offers new possibilities for developing new ultra-compact spectrometers and low-cost hyperspectral imaging sensors in mid-infrared region.

A fast multispectral light synthesiser based on LEDs and a diffraction grating

Optical experiments often require fast-switching light sources with adjustable bandwidths and intensities. We constructed a wavelength combiner based on a reflective planar diffraction grating and light emitting diodes with emission peaks from 350 to 630 nm that were positioned at the angles corresponding to the first diffraction order of the reversed beam. The combined output beam was launched into a fibre. The spacing between 22 equally wide spectral bands was about 15 nm. The time resolution of the pulse-width modulation drivers was 1 ms. The source was validated with a fast intracellular measurement of the spectral sensitivity of blowfly photoreceptors. In hyperspectral imaging of Xenopus skin circulation, the wavelength resolution was adequate to resolve haemoglobin absorption spectra. The device contains no moving parts, has low stray light and is intrinsically capable of multi-band output. Possible applications include visual physiology, biomedical optics, microscopy and spectroscopy.

Lasing in blood

Indocyanine green (ICG) is the only near-infrared dye approved by the U.S. Food and Drug Administration for clinical use. When injected in blood, ICG binds primarily to plasma proteins and lipoproteins, resulting in enhanced fluorescence. Recently, the optofluidic laser has emerged as a novel tool in bio-analysis. Laser emission has advantages over fluorescence in signal amplification, narrow linewidth, and strong intensity, leading to orders of magnitude increase in detection sensitivity and imaging contrast. Here we successfully demonstrate, to the best of our knowledge, the first ICG lasing in human serum and whole blood with the clinical ICG concentrations and the pump intensity far below the clinically permissible level. Furthermore, we systematically study ICG laser emission within each major serological component (albumins, globulins, and lipoproteins) and reveal the critical elements and conditions responsible for lasing. Our work marks a critical step toward eventual clinical and biomedical applications of optofluidic lasers using FDA approved fluorophores, which may complement or even supersede conventional fluorescence-based sensing and imaging.

Effects of hair sprays on colour perception: a hyperspectral imaging approach to shine and chroma on heads

OBJECTIVES

Hair sprays apply fixative ingredients to provide hold to a hair style as well as weather resistance and optical properties such as shine. Generally, sprays distribute fine particles containing polymeric ingredients to form a thin film on the surface of hair. Different hair types require different strengths of the formed deposit on the hair surface. The present study shows how sprays also alter the visibility of the hair colour by altering the surface topology of the hair fibres.

METHODS

Hyperspectral imaging is used to map spectral characteristics of hair on mannequins and panelists over the curvature of heads. Spectral and spatial characteristics are measured before and after hair spray applications. The hair surface is imaged by SEM to visualize the degree of cuticle coverage. Finally, the perception of hair colour was evaluated on red-coloured mannequins by consumer questionnaire.

RESULTS

Hair sprays deposit different degrees of fixatives, which lead to a progressive leveling of the cuticle natural tilt angle with respect to the fibre axis. As a result, shine is progressively shifting towards the region of hair colour visibility and decreases the perceived colour of hair seen by consumers. Lighter sprays show thinner film formation on the hair surface and less of a shine shift than strong hold hair sprays.

CONCLUSIONS

Hair sprays are generally employed for hair style hold and weather resistance and considered without effect on hair colour. Our approach shows that spray-deposited films can affect colour perception by altering the microstructure of the hair surface. Thin films deposited on the hair fibre surface can partially fill gaps between cuticles, which reduces the cuticle natural angle. This partial erasure results in a angle shift of the shine regions towards the angle of internal reflection, thus decreasing the perceived hair colour regions as experienced by a group of consumers.

Hyperspectral Imaging Using Flexible Endoscopy for Laryngeal Cancer Detection

Hyperspectral imaging (HSI) is increasingly gaining acceptance in the medical field. Up until now, HSI has been used in conjunction with rigid endoscopy to detect cancer in vivo. The logical next step is to pair HSI with flexible endoscopy, since it improves access to hard-to-reach areas. While the flexible endoscope's fiber optic cables provide the advantage of flexibility, they also introduce an interfering honeycomb-like pattern onto images. Due to the substantial impact this pattern has on locating cancerous tissue, it must be removed before the HS data can be further processed. Thereby, the loss of information is to minimize avoiding the suppression of small-area variations of pixel values. We have developed a system that uses flexible endoscopy to record HS cubes of the larynx and designed a special filtering technique to remove the honeycomb-like pattern with minimal loss of information. We have confirmed its feasibility by comparing it to conventional filtering techniques using an objective metric and by applying unsupervised and supervised classifications to raw and pre-processed HS cubes. Compared to conventional techniques, our method successfully removes the honeycomb-like pattern and considerably improves classification performance, while preserving image details.

Experimental demonstration of an adaptive architecture for direct spectral imaging classification

Spectral imaging is a powerful tool for providing in situ material classification across a spatial scene. Typically, spectral imaging analyses are interested in classification, though often the classification is performed only after reconstruction of the spectral datacube. We present a computational spectral imaging system, the Adaptive Feature-Specific Spectral Imaging Classifier (AFSSI-C), which yields direct classification across the spatial scene without reconstruction of the source datacube. With a dual disperser architecture and a programmable spatial light modulator, the AFSSI-C measures specific projections of the spectral datacube which are generated by an adaptive Bayesian classification and feature design framework. We experimentally demonstrate multiple order-of-magnitude improvement of classification accuracy in low signal-to-noise (SNR) environments when compared to legacy spectral imaging systems.

Imaging Techniques for Clinical Burn Assessment with a Focus on Multispectral Imaging

Significance: Burn assessments, including extent and severity, are some of the most critical diagnoses in burn care, and many recently developed imaging techniques may have the potential to improve the accuracy of these evaluations. Recent Advances: Optical devices, telemedicine, and high-frequency ultrasound are among the highlights in recent burn imaging advancements. We present another promising technology, multispectral imaging (MSI), which also has the potential to impact current medical practice in burn care, among a variety of other specialties. Critical Issues: At this time, it is still a matter of debate as to why there is no consensus on the use of technology to assist burn assessments in the United States. Fortunately, the availability of techniques does not appear to be a limitation. However, the selection of appropriate imaging technology to augment the provision of burn care can be difficult for clinicians to navigate. There are many technologies available, but a comprehensive review summarizing the tissue characteristics measured by each technology in light of aiding clinicians in selecting the proper device is missing. This would be especially valuable for the nonburn specialists who encounter burn injuries. Future Directions: The questions of when burn assessment devices are useful to the burn team, how the various imaging devices work, and where the various burn imaging technologies fit into the spectrum of burn care will continue to be addressed. Technologies that can image a large surface area quickly, such as thermography or laser speckle imaging, may be suitable for initial burn assessment and triage. In the setting of presurgical planning, ultrasound or optical microscopy techniques, including optical coherence tomography, may prove useful. MSI, which actually has origins in burn care, may ultimately meet a high number of requirements for burn assessment in routine clinical use.

Hyperspectral interventional imaging for enhanced tissue visualization and discrimination combining band selection methods

PURPOSE

Hyperspectral imaging is an emerging technology recently introduced in medical applications inasmuch as it provides a powerful tool for noninvasive tissue characterization. In this context, a new system was designed to be easily integrated in the operating room in order to detect anatomical tissues hardly noticed by the surgeon's naked eye.

METHOD

Our LCTF-based spectral imaging system is operative over visible, near- and middle-infrared spectral ranges (400-1700 nm). It is dedicated to enhance critical biological tissues such as the ureter and the facial nerve. We aim to find the best three relevant bands to create a RGB image to display during the intervention with maximal contrast between the target tissue and its surroundings. A comparative study is carried out between band selection methods and band transformation methods. Combined band selection methods are proposed. All methods are compared using different evaluation criteria.

RESULTS

Experimental results show that the proposed combined band selection methods provide the best performance with rich information, high tissue separability and short computational time. These methods yield a significant discrimination between biological tissues.

CONCLUSION

We developed a hyperspectral imaging system in order to enhance some biological tissue visualization. The proposed methods provided an acceptable trade-off between the evaluation criteria especially in SWIR spectral band that outperforms the naked eye's capacities.

Towards automated spectroscopic tissue classification in thyroid and parathyroid surgery

BACKGROUND

In (para-)thyroid surgery iatrogenic parathyroid injury should be prevented. To aid the surgeons' eye, a camera system enabling parathyroid-specific image enhancement would be useful. Hyperspectral camera technology might work, provided that the spectral signature of parathyroid tissue offers enough specific features to be reliably and automatically distinguished from surrounding tissues. As a first step to investigate this, we examined the feasibility of wide band diffuse reflectance spectroscopy (DRS) for automated spectroscopic tissue classification, using silicon (Si) and indium-gallium-arsenide (InGaAs) sensors.

METHODS

DRS (350-1830 nm) was performed during (para-)thyroid resections. From the acquired spectra 36 features at predefined wavelengths were extracted. The best features for classification of parathyroid from adipose or thyroid were assessed by binary logistic regression for Si- and InGaAs-sensor ranges. Classification performance was evaluated by leave-one-out cross-validation.

RESULTS

In 19 patients 299 spectra were recorded (62 tissue sites: thyroid = 23, parathyroid = 21, adipose = 18). Classification accuracy of parathyroid-adipose was, respectively, 79% (Si), 82% (InGaAs) and 97% (Si/InGaAs combined). Parathyroid-thyroid classification accuracies were 80% (Si), 75% (InGaAs), 82% (Si/InGaAs combined).

CONCLUSIONS

Si and InGaAs sensors are fairly accurate for automated spectroscopic classification of parathyroid, adipose and thyroid tissues. Combination of both sensor technologies improves accuracy. Follow-up research, aimed towards hyperspectral imaging seems justified. Copyright © 2016 John Wiley & Sons, Ltd.

Spatial and Spectral Characterization of Human Retinal Pigment Epithelium Fluorophore Families by Ex Vivo Hyperspectral Autofluorescence Imaging

Purpose

Discovery of candidate spectra for abundant fluorophore families in human retinal pigment epithelium (RPE) by ex vivo hyperspectral imaging.

Methods

Hyperspectral autofluorescence emission images were captured between 420 and 720 nm (10-nm intervals), at two excitation bands (436–460, 480–510 nm), from three locations (fovea, perifovea, near-periphery) in 20 normal RPE/Bruch's membrane (BrM) flatmounts. Mathematical factorization extracted a BrM spectrum (S0) and abundant lipofuscin/melanolipofuscin (LF/ML) spectra of RPE origin (S1, S2, S3) from each tissue.

Results

Smooth spectra S1 to S3, with perinuclear localization consistent with LF/ML at all three retinal locations and both excitations in 14 eyes (84 datasets), were included in the analysis. The mean peak emissions of S0, S1, and S2 at λ_ex 436 nm were, respectively, 495 ± 14, 535 ± 17, and 576 ± 20 nm. S3 was generally trimodal, with peaks at either 580, 620, or 650 nm (peak mode, 650 nm). At λ_ex 480 nm, S0, S1, and S2 were red-shifted to 526 ± 9, 553 ± 10, and 588 ± 23 nm, and S3 was again trimodal (peak mode, 620 nm). S1 often split into two spectra, S1A and S1B. S3 strongly colocalized with melanin. There were no significant differences across age, sex, or retinal location.

Conclusions

There appear to be at least three families of abundant RPE fluorophores that are ubiquitous across age, retinal location, and sex in this sample of healthy eyes. Further molecular characterization by imaging mass spectrometry and localization via super-resolution microscopy should elucidate normal and abnormal RPE physiology involving fluorophores.

Translational Relevance

Our results help establish hyperspectral autofluorescence imaging of the human retinal pigment epithelium as a useful tool for investigating retinal health and disease.

A four-dimensional snapshot hyperspectral video-endoscope for bio-imaging applications

Hyperspectral imaging has proven significance in bio-imaging applications and it has the ability to capture up to several hundred images of different wavelengths offering relevant spectral signatures. To use hyperspectral imaging for in vivo monitoring and diagnosis of the internal body cavities, a snapshot hyperspectral video-endoscope is required. However, such reported systems provide only about 50 wavelengths. We have developed a four-dimensional snapshot hyperspectral video-endoscope with a spectral range of 400–1000 nm, which can detect 756 wavelengths for imaging, significantly more than such systems. Capturing the three-dimensional datacube sequentially gives the fourth dimension. All these are achieved through a flexible two-dimensional to one-dimensional fiber bundle. The potential of this custom designed and fabricated compact biomedical probe is demonstrated by imaging phantom tissue samples in reflectance and fluorescence imaging modalities. It is envisaged that this novel concept and developed probe will contribute significantly towards diagnostic in vivo biomedical imaging in the near future.

Hyperspectral Imaging of Neoplastic Progression in a Mouse Model of Oral Carcinogenesis

Hyperspectral imaging (HSI) is an emerging modality for medical applications and holds great potential for noninvasive early detection of cancer. It has been reported that early cancer detection can improve the survival and quality of life of head and neck cancer patients. In this paper, we explored the possibility of differentiating between premalignant lesions and healthy tongue tissue using hyperspectral imaging in a chemical induced oral cancer animal model. We proposed a novel classification algorithm for cancer detection using hyperspectral images. The method detected the dysplastic tissue with an average area under the curve (AUC) of 0.89. The hyperspectral imaging and classification technique may provide a new tool for oral cancer detection.

Development of an image pre-processor for operational hyperspectral laryngeal cancer detection

Hyperspectral imaging (HSI) is a technology with high potential in the field of non-invasive detection of cancer. However, in complex imaging situations like HSI of the larynx with a rigid endoscope, various image interferences can disable a proper classification of cancerous tissue. We identified three main problems: i) misregistration of single images in a HS cube due to patient heartbeat ii) image noise and iii) specular reflections (SR). Consequently, an image pre-processor is developed in the current paper to overcome these image interferences. It encompasses i) image registration ii) noise removal by minimum noise fraction (MNF) transformation and iii) a novel SR detection method. The results reveal that the pre-processor improves classification performance, while the newly developed SR detection method outperforms global thresholding technique hitherto used by 46%. The novel pre-processor will be used for future studies towards the development of an operational scheme for HS-based larynx cancer detection. RGB image of the larynx derived from the hyperspectral cube and corresponding specular reflections (a) manually segmented and (b) detected by a novel specular reflection detection method.

A Minimum Spanning Forest-Based Method for Noninvasive Cancer Detection With Hyperspectral Imaging

GOAL

The purpose of this paper is to develop a classification method that combines both spectral and spatial information for distinguishing cancer from healthy tissue on hyperspectral images in an animal model.

METHODS

An automated algorithm based on a minimum spanning forest (MSF) and optimal band selection has been proposed to classify healthy and cancerous tissue on hyperspectral images. A support vector machine classifier is trained to create a pixel-wise classification probability map of cancerous and healthy tissue. This map is then used to identify markers that are used to compute mutual information for a range of bands in the hyperspectral image and thus select the optimal bands. An MSF is finally grown to segment the image using spatial and spectral information.

CONCLUSION

The MSF based method with automatically selected bands proved to be accurate in determining the tumor boundary on hyperspectral images.

SIGNIFICANCE

Hyperspectral imaging combined with the proposed classification technique has the potential to provide a noninvasive tool for cancer detection.

A review of snapshot multidimensional optical imaging: measuring photon tags in parallel

Multidimensional optical imaging has seen remarkable growth in the past decade. Rather than measuring only the two-dimensional spatial distribution of light, as in conventional photography, multidimensional optical imaging captures light in up to nine dimensions, providing unprecedented information about incident photons' spatial coordinates, emittance angles, wavelength, time, and polarization. Multidimensional optical imaging can be accomplished either by scanning or parallel acquisition. Compared with scanning-based imagers, parallel acquisition-also dubbed snapshot imaging-has a prominent advantage in maximizing optical throughput, particularly when measuring a datacube of high dimensions. Here, we first categorize snapshot multidimensional imagers based on their acquisition and image reconstruction strategies, then highlight the snapshot advantage in the context of optical throughput, and finally we discuss their state-of-the-art implementations and applications.

Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging

Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications. HSI acquires two dimensional images at various wavelengths. The combination of both spectral and spatial information provides quantitative information for cancer detection and diagnosis. This paper proposes using superpixels, principal component analysis (PCA), and support vector machine (SVM) to distinguish regions of tumor from healthy tissue. The classification method uses 2 principal components decomposed from hyperspectral images and obtains an average sensitivity of 93% and an average specificity of 85% for 11 mice. The hyperspectral imaging technology and classification method can have various applications in cancer research and management.

[Methods for measuring skin aging]

Aging affects human skin and is becoming increasingly important with regard to medical, social and aesthetic issues. Detection of intrinsic and extrinsic components of skin aging requires reliable measurement methods. Modern techniques, e.g., based on direct imaging, spectroscopy or skin physiological measurements, provide a broad spectrum of parameters for different applications.

Variable optofluidic slit aperture

The shape of liquid interfaces can be precisely controlled using electrowetting, an actuation mechanism which has been widely used for tunable optofluidic micro-optical components such as lenses or irises. We have expanded the considerable flexibility inherent in electrowetting actuation to realize a variable optofluidic slit, a tunable and reconfigurable two-dimensional aperture with no mechanically moving parts. This optofluidic slit is formed by precisely controlled movement of the liquid interfaces of two highly opaque ink droplets. The 1.5 mm long slit aperture, with controllably variable discrete widths down to 45 µm, may be scanned across a length of 1.5 mm with switching times between adjacent slit positions of less than 120 ms. In addition, for a fixed slit aperture position, the width may be tuned to a minimum of 3 µm with high uniformity and linearity over the entire slit length. This compact, purely fluidic device offers an electrically controlled aperture tuning range not achievable with extant mechanical alternatives of a similar size.

Single-Cell Quantification of Cytosine Modifications by Hyperspectral Dark-Field Imaging

Epigenetic modifications on DNA, especially on cytosine, play a critical role in regulating gene expression and genome stability. It is known that the levels of different cytosine derivatives are highly dynamic and are regulated by a variety of factors that act on the chromatin. Here we report an optical methodology based on hyperspectral dark-field imaging (HSDFI) using plasmonic nanoprobes to quantify the recently identified cytosine modifications on DNA in single cells. Gold (Au) and silver (Ag) nanoparticles (NPs) functionalized with specific antibodies were used as contrast-generating agents due to their strong local surface plasmon resonance (LSPR) properties. With this powerful platform we have revealed the spatial distribution and quantity of 5-carboxylcytosine (5caC) at the different stages in cell cycle and demonstrated that 5caC was a stably inherited epigenetic mark. We have also shown that the regional density of 5caC on a single chromosome can be mapped due to the spectral sensitivity of the nanoprobes in relation to the interparticle distance. Notably, HSDFI enables an efficient removal of the scattering noises from nonspecifically aggregated nanoprobes, to improve accuracy in the quantification of different cytosine modifications in single cells. Further, by separating the LSPR fingerprints of AuNPs and AgNPs, multiplex detection of two cytosine modifications was also performed. Our results demonstrate HSDFI as a versatile platform for spatial and spectroscopic characterization of plasmonic nanoprobe-labeled nuclear targets at the single-cell level for quantitative epigenetic screening.

Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging

Multispectral imaging (MSI) was implemented to develop a burn tissue classification device to assist burn surgeons in planning and performing debridement surgery. To build a classification model via machine learning, training data accurately representing the burn tissue was needed, but assigning raw MSI data to appropriate tissue classes is prone to error. We hypothesized that removing outliers from the training dataset would improve classification accuracy. A swine burn model was developed to build an MSI training database and study an algorithm’s burn tissue classification abilities. After the ground-truth database was generated, we developed a multistage method based on Z -test and univariate analysis to detect and remove outliers from the training dataset. Using 10-fold cross validation, we compared the algorithm’s accuracy when trained with and without the presence of outliers. The outlier detection and removal method reduced the variance of the training data. Test accuracy was improved from 63% to 76%, matching the accuracy of clinical judgment of expert burn surgeons, the current gold standard in burn injury assessment. Given that there are few surgeons and facilities specializing in burn care, this technology may improve the standard of burn care for patients without access to specialized facilities.

Highly sensitive image-derived indices of water-stressed plants using hyperspectral imaging in SWIR and histogram analysis

The optical signature of leaves is an important monitoring and predictive parameter for a variety of biotic and abiotic stresses, including drought. Such signatures derived from spectroscopic measurements provide vegetation indices - a quantitative method for assessing plant health. However, the commonly used metrics suffer from low sensitivity. Relatively small changes in water content in moderately stressed plants demand high-contrast imaging to distinguish affected plants. We present a new approach in deriving sensitive indices using hyperspectral imaging in a short-wave infrared range from 800 nm to 1600 nm. Our method, based on high spectral resolution (1.56 nm) instrumentation and image processing algorithms (quantitative histogram analysis), enables us to distinguish a moderate water stress equivalent of 20% relative water content (RWC). The identified image-derived indices 15XX nm/14XX nm (i.e. 1529 nm/1416 nm) were superior to common vegetation indices, such as WBI, MSI, and NDWI, with significantly better sensitivity, enabling early diagnostics of plant health.

Hyperspectral imaging with a liquid crystal polarization interferometer

A novel hyperspectral imaging system has been developed that takes advantage of the tunable path delay between orthogonal polarization states of a liquid crystal variable retarder. The liquid crystal is placed in the optical path of an imaging system and the path delay between the polarization states is varied, causing an interferogram to be generated simultaneously at each pixel. A data set consisting of a series of images is recorded while varying the path delay; Fourier transforming the data set with respect to the path delay yields the hyperspectral data-cube. The concept is demonstrated with a prototype imager consisting of a liquid crystal variable retarder integrated into a commercial 640x480 pixel CMOS camera. The prototype can acquire a full hyperspectral data-cube in 0.4 s, and is sensitive to light over a 400 nm to 1100 nm range with a dispersion-dependent spectral resolution of 450 cm(-1) to 660 cm(-1). Similar to Fourier transform spectroscopy, the imager is spatially and spectrally multiplexed and therefore achieves high optical throughput. Additionally, the common-path nature of the polarization interferometer yields a vibration-insensitive device. Our concept allows for the spectral resolution, imaging speed, and spatial resolution to be traded off in software to optimally address a given application. The simplicity, compactness, potential low cost, and software adaptability of the device may enable a disruptive class of hyperspectral imaging systems with a broad range of applications.

Inverse-power-law behavior of cellular motility reveals stromal-epithelial cell interactions in 3D co-culture by OCT fluctuation spectroscopy

The progression of breast cancer is known to be affected by stromal cells within the local microenvironment. Here we study the effect of stromal fibroblasts on the in-place motions (motility) of mammary epithelial cells within organoids in 3D co-culture, inferred from the speckle fluctuation spectrum using optical coherence tomography (OCT). In contrast to Brownian motion, mammary cell motions exhibit an inverse power-law fluctuation spectrum. We introduce two complementary metrics for quantifying fluctuation spectra: the power-law exponent and a novel definition of the motility amplitude, both of which are signal- and position-independent. We find that the power-law exponent and motility amplitude are positively (p<0.001) and negatively (p<0.01) correlated with the density of stromal cells in 3D co-culture, respectively. We also show how the hyperspectral data can be visualized using these metrics to observe heterogeneity within organoids. This constitutes a simple and powerful tool for detecting and imaging cellular functional changes with OCT.

Intraoperative measurement of bowel oxygen saturation using a multispectral imaging laparoscope

Intraoperative monitoring of tissue oxygen saturation (StO2 ) has potentially important applications in procedures such as organ transplantation or colorectal surgery, where successful reperfusion affects the viability and integrity of repaired tissues. In this paper a liquid crystal tuneable filter-based multispectral imaging (MSI) laparoscope is described. Motion-induced image misalignments are reduced, using feature-based registration, before regression of the tissue reflectance spectra to calculate relative quantities of oxy- and deoxyhaemoglobin. The laparoscope was validated in vivo, during porcine abdominal surgery, by making parallel MSI and blood gas measurements of the small bowel vasculature. Ischaemic conditions were induced by local occlusion of the mesenteric arcade and monitored using the system. The MSI laparoscope was capable of measuring StO2 over a wide range (30-100%) with a temporal error of ± 7.5%. The imager showed sensitivity to spatial changes in StO2 during dynamic local occlusions, as well as tracking the recovery of tissues post-occlusion.

Multispectral tissue characterization for intestinal anastomosis optimization

Intestinal anastomosis is a surgical procedure that restores bowel continuity after surgical resection to treat intestinal malignancy, inflammation, or obstruction. Despite the routine nature of intestinal anastomosis procedures, the rate of complications is high. Standard visual inspection cannot distinguish the tissue subsurface and small changes in spectral characteristics of the tissue, so existing tissue anastomosis techniques that rely on human vision to guide suturing could lead to problems such as bleeding and leakage from suturing sites. We present a proof-of-concept study using a portable multispectral imaging (MSI) platform for tissue characterization and preoperative surgical planning in intestinal anastomosis. The platform is composed of a fiber ring light-guided MSI system coupled with polarizers and image analysis software. The system is tested on ex vivo porcine intestine tissue, and we demonstrate the feasibility of identifying optimal regions for suture placement.

Hyperspectral image analysis. A tutorial

This tutorial aims at providing guidelines and practical tools to assist with the analysis of hyperspectral images. Topics like hyperspectral image acquisition, image pre-processing, multivariate exploratory analysis, hyperspectral image resolution, classification and final digital image processing will be exposed, and some guidelines given and discussed. Due to the broad character of current applications and the vast number of multivariate methods available, this paper has focused on an industrial chemical framework to explain, in a step-wise manner, how to develop a classification methodology to differentiate between several types of plastics by using Near infrared hyperspectral imaging and Partial Least Squares - Discriminant Analysis. Thus, the reader is guided through every single step and oriented in order to adapt those strategies to the user's case.

Hyperspectral light sheet microscopy

To study the development and interactions of cells and tissues, multiple fluorescent markers need to be imaged efficiently in a single living organism. Instead of acquiring individual colours sequentially with filters, we created a platform based on line-scanning light sheet microscopy to record the entire spectrum for each pixel in a three-dimensional volume. We evaluated data sets with varying spectral sampling and determined the optimal channel width to be around 5 nm. With the help of these data sets, we show that our setup outperforms filter-based approaches with regard to image quality and discrimination of fluorophores. By spectral unmixing we resolved overlapping fluorophores with up to nanometre resolution and removed autofluorescence in zebrafish and fruit fly embryos.

Multicolour information is required to study the complex interplay of biological tissues. Here, Jahr et al. acquire spectral information at high resolution for each pixel in a hyperspectral light sheet microscope, while maintaining its perpendicular illumination and low phototoxicity.

Algorithm for mapping cutaneous tissue oxygen concentration using hyperspectral imaging

The measurement of tissue oxygenation plays an important role in the diagnosis and therapeutic assessment of a large variety of diseases. Many different methods have been developed and are currently applied in clinical practice for the measurement of tissue oxygenation. Unfortunately, each of these methods has its own limitations. In this paper we proposed the use of hyperspectral imaging as new method for the assessment of the tissue oxygenation level. To extract this information from hyperspectral images a new algorithm for mapping cutaneous tissue oxygen concentration was developed. This algorithm takes into account and solves some problems related to setting and calculation of some parameters derived from hyperspectral images. The algorithm was tested with good results on synthetic images and then validated on the fingers of a hand with different blood irrigation states. The results obtained have proved the ability of hyperspectral imaging together with the developed algorithm to map the oxy- and deoxyhemoglobin distribution on the analyzed fingers. These are only preliminary results and other studies should be done before this approach to be used in the clinical setting for the diagnosis and monitoring of various diseases.

Experimental system for measuring the full scattering profile of circular phantoms

Optical methods for monitoring physiological tissue state are important and useful because they are non-invasive and sensitive. Experimental measurements of the full scattering profile of circular phantoms are presented. We report, for the first time, an experimental observation of a typical reflected light intensity behavior for a circular structure characterized by the isobaric point. We previously suggested a new theoretically method for measuring the full scattering profile, which is the angular distribution of light intensity, of cylindrical tissues. In this work we present that the experimental result match the simulation results. We show the isobaric point at 105° for a cylindrical phantom with a 7mm diameter, while for a 16mm diameter phantom the isobaric point is at 125°. Furthermore, the experimental work present a new crossover point of the full scattering profiles of subjects with different diameters of the cylindrical tissues.

Optical hyperspectral imaging in microscopy and spectroscopy - a review of data acquisition

Rather than simply acting as a photographic camera capturing two-dimensional (x, y) intensity images or a spectrometer acquiring spectra (λ), a hyperspectral imager measures entire three-dimensional (x, y, λ) datacubes for multivariate analysis, providing structural, molecular, and functional information about biological cells or tissue with unprecedented detail. Such data also gives clinical insights for disease diagnosis and treatment. We summarize the principles underpinning this technology, highlight its practical implementation, and discuss its recent applications at microscopic to macroscopic scales. Datacube acquisition strategies in hyperspectral imaging x, y, spatial coordinates; λ, wavelength.

Spectral degree of linear polarization of light from healthy skin and melanoma

A non-invasive optical technique, based on a supercontinuum laser source and hyperspectral sensors, is established to measure the spectral degree of linear polarization (DOLP) in a broad spectral range from 525 nm to 1000 nm. Several biomaterials of interest, such as healthy and cancerous skins, are considered. The spectral DOLP of melanoma, from 5 mm to 9 mm diameter, are measured and analyzed. An increase of the spectral DOLP is reported for 100% of the melanoma samples compared to healthy skin samples. The spectral DOLP of a given melanoma appears to be correlated to the stage of its development: the larger the melanoma, the higher the DOLP. Such trend could be explained by a decrease of the surface roughness along the evolution of the disease. In addition, a significant spectral dependence of the DOLP is reported for melanoma samples as it exhibits a decrease in the near infrared from 750 nm to 1000 nm.

Blind source separation of ex-vivo aorta tissue multispectral images

Blind Source Separation methods (BSS) aim for the decomposition of a given signal in its main components or source signals. Those techniques have been widely used in the literature for the analysis of biomedical images, in order to extract the main components of an organ or tissue under study. The analysis of skin images for the extraction of melanin and hemoglobin is an example of the use of BSS. This paper presents a proof of concept of the use of source separation of ex-vivo aorta tissue multispectral Images. The images are acquired with an interference filter-based imaging system. The images are processed by means of two algorithms: Independent Components analysis and Non-negative Matrix Factorization. In both cases, it is possible to obtain maps that quantify the concentration of the main chromophores present in aortic tissue. Also, the algorithms allow for spectral absorbance of the main tissue components. Those spectral signatures were compared against the theoretical ones by using correlation coefficients. Those coefficients report values close to 0.9, which is a good estimator of the method's performance. Also, correlation coefficients lead to the identification of the concentration maps according to the evaluated chromophore. The results suggest that Multi/hyper-spectral systems together with image processing techniques is a potential tool for the analysis of cardiovascular tissue.

Quantitative Wavelength Analysis and Image Classification for Intraoperative Cancer Diagnosis with Hyperspectral Imaging

Complete surgical removal of tumor tissue is essential for postoperative prognosis after surgery. Intraoperative tumor imaging and visualization are an important step in aiding surgeons to evaluate and resect tumor tissue in real time, thus enabling more complete resection of diseased tissue and better conservation of healthy tissue. As an emerging modality, hyperspectral imaging (HSI) holds great potential for comprehensive and objective intraoperative cancer assessment. In this paper, we explored the possibility of intraoperative tumor detection and visualization during surgery using HSI in the wavelength range of 450 nm - 900 nm in an animal experiment. We proposed a new algorithm for glare removal and cancer detection on surgical hyperspectral images, and detected the tumor margins in five mice with an average sensitivity and specificity of 94.4% and 98.3%, respectively. The hyperspectral imaging and quantification method have the potential to provide an innovative tool for image-guided surgery.

Estimation of Tissue Optical Parameters with Hyperspectral Imaging and Spectral Unmixing

Early detection of oral cancer and its curable precursors can improve patient survival and quality of life. Hyperspectral imaging (HSI) holds the potential for noninvasive early detection of oral cancer. The quantification of tissue chromophores by spectral unmixing of hyperspectral images could provide insights for evaluating cancer progression. In this study, non-negative matrix factorization has been applied for decomposing hyperspectral images into physiologically meaningful chromophore concentration maps. The approach has been validated by computer-simulated hyperspectral images and in vivo tumor hyperspectral images from a head and neck cancer animal model.

Real-time noise removal for line-scanning hyperspectral devices using a minimum noise fraction-based approach

Processing line-by-line and in real-time can be convenient for some applications of line-scanning hyperspectral imaging technology. Some types of processing, like inverse modeling and spectral analysis, can be sensitive to noise. The MNF (minimum noise fraction) transform provides suitable denoising performance, but requires full image availability for the estimation of image and noise statistics. In this work, a modified algorithm is proposed. Incrementally-updated statistics enables the algorithm to denoise the image line-by-line. The denoising performance has been compared to conventional MNF and found to be equal. With a satisfying denoising performance and real-time implementation, the developed algorithm can denoise line-scanned hyperspectral images in real-time. The elimination of waiting time before denoised data are available is an important step towards real-time visualization of processed hyperspectral data. The source code can be found at http://www.github.com/ntnu-bioopt/mnf. This includes an implementation of conventional MNF denoising.

Automated Spectroscopic Tissue Classification in Colorectal Surgery

BACKGROUND

In colorectal surgery, detecting ureters and mesenteric arteries is of utmost importance to prevent iatrogenic injury and to facilitate intraoperative decision making. A tool enabling ureter- and artery-specific image enhancement within (and possibly through) surrounding adipose tissue would facilitate this need, especially during laparoscopy. To evaluate the potential of hyperspectral imaging in colorectal surgery, we explored spectral tissue signatures using single-spot diffuse reflectance spectroscopy (DRS). As hyperspectral cameras with silicon (Si) and indium gallium arsenide (InGaAs) sensor chips are becoming available, we investigated spectral distinctive features for both sensor ranges.

METHODS

In vivo wide-band (wavelength range 350-1830 nm) DRS was performed during open colorectal surgery. From the recorded spectra, 36 features were extracted at predefined wavelengths: 18 gradients and 18 amplitude differences. For classification of respectively ureter and artery in relation to surrounding adipose tissue, the best distinctive feature was selected using binary logistic regression for Si- and InGaAs-sensor spectral ranges separately. Classification performance was evaluated by leave-one-out cross-validation.

RESULTS

In 10 consecutive patients, 253 spectra were recorded on 53 tissue sites (including colon, adipose tissue, muscle, artery, vein, ureter). Classification of ureter versus adipose tissue revealed accuracy of 100% for both Si range and InGaAs range. Classification of artery versus surrounding adipose tissue revealed accuracies of 95% (Si) and 89% (InGaAs).

CONCLUSIONS

Intraoperative DRS showed that Si and InGaAs sensors are equally suited for automated classification of ureter versus surrounding adipose tissue. Si sensors seem better suited for classifying artery versus mesenteric adipose tissue. Progress toward hyperspectral imaging within this field is promising.

Identification of spectral biomarkers for type 1 diabetes mellitus using the combination of chiroptical and vibrational spectroscopy

Chiroptical spectroscopy is able to detect conformational changes of plasmatic biomolecules during type 1 diabetes mellitus.

Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery

Hyperspectral imaging (HSI) is an imaging modality that holds strong potential for rapid cancer detection during image-guided surgery. But the data from HSI often needs to be processed appropriately in order to extract the maximum useful information that differentiates cancer from normal tissue. We proposed a framework for hyperspectral image processing and quantification, which includes a set of steps including image preprocessing, glare removal, feature extraction, and ultimately image classification. The framework has been tested on images from mice with head and neck cancer, using spectra from 450- to 900-nm wavelength. The image analysis computed Fourier coefficients, normalized reflectance, mean, and spectral derivatives for improved accuracy. The experimental results demonstrated the feasibility of the hyperspectral image processing and quantification framework for cancer detection during animal tumor surgery, in a challenging setting where sensitivity can be low due to a modest number of features present, but potential for fast image classification can be high. This HSI approach may have potential application in tumor margin assessment during image-guided surgery, where speed of assessment may be the dominant factor.

Taking advantage of hyperspectral imaging classification of urinary stones against conventional infrared spectroscopy

The analysis of urinary stones is mandatory for the best management of the disease after the stone passage in order to prevent further stone episodes. Thus the use of an appropriate methodology for an individualized stone analysis becomes a key factor for giving the patient the most suitable treatment. A recently developed hyperspectral imaging methodology, based on pixel-to-pixel analysis of near-infrared spectral images, is compared to the reference technique in stone analysis, infrared (IR) spectroscopy. The developed classification model yields >90% correct classification rate when compared to IR and is able to precisely locate stone components within the structure of the stone with a 15 µm resolution. Due to the little sample pretreatment, low analysis time, good performance of the model, and the automation of the measurements, they become analyst independent; this methodology can be considered to become a routine analysis for clinical laboratories.