J Biomed Opt.
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Jan; 19(1): .doi:
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aEmory University and Georgia Institute of Technology, The Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia
Find articles by Guolan Lu
aEmory University and Georgia Institute of Technology, The Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia
bEmory University, School of Medicine, Department of Radiology and Imaging Sciences, Atlanta, Georgia
cEmory University, Department of Mathematics & Computer Science, Atlanta, Georgia
dEmory University, Winship Cancer Institute, Atlanta, Georgia
Find articles by Baowei Fei
Author information Article notes Copyright and License information PMC DisclaimeraEmory University and Georgia Institute of Technology, The Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia
bEmory University, School of Medicine, Department of Radiology and Imaging Sciences, Atlanta, Georgia
cEmory University, Department of Mathematics & Computer Science, Atlanta, Georgia
dEmory University, Winship Cancer Institute, Atlanta, Georgia
*Address all correspondence to: Baowei Fei, :Address all correspondence to: Baowei Fei, : ude.yrome@iefb
Copyright © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications, especially in disease diagnosis and image-guided surgery. HSI acquires a three-dimensional dataset called hypercube, with two spatial dimensions and one spectral dimension. Spatially resolved spectral imaging obtained by HSI provides diagnostic information about the tissue physiology, morphology, and composition. This review paper presents an overview of the literature on medical hyperspectral imaging technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application.
Keywords:
medical hyperspectral imaging, tissue optics, quantitative imaging, image analysis and classification, cancer detection and diagnosis, image-guided surgery
Hyperspectral imaging (HSI), also called imaging spectrometer,1 originated from remote sensing and has been explored for various applications by NASA.2 With the advantage of acquiring two-dimensional images across a wide range of electromagnetic spectrum, HSI has been applied to numerous areas, including archaeology and art conservation,3,4 vegetation and water resource control,5,6 food quality and safety control,7,8 forensic medicine,9,10 crime scene detection,11,12 biomedicine,13,14 etc.
As an emerging imaging modality for medical applications, HSI offers great potential for noninvasive disease diagnosis and surgical guidance. Light delivered to biological tissue undergoes multiple scattering from inhomogeneity of biological structures and absorption primarily in hemoglobin, melanin, and water as it propagates through the tissue.15,16 It is assumed that the absorption, fluorescence, and scattering characteristics of tissue change during the progression of disease.17 Therefore, the reflected, fluorescent, and transmitted light from tissue captured by HSI carries quantitative diagnostic information about tissue pathology.17&#;20 In recent years, advances in hyperspectral cameras, image analysis methods, and computational power make it possible for many exciting applications in the medical field.
In the following, we aim to introduce and explain medical hyperspectral imaging (MHSI) technology and to give an overview of the literature on MHSI hardware, software, and applications. This survey covers literature from fall to spring . We start at the basics with the mechanisms of HSI and its current development status. We then classify MHSI based on its acquisition mode, spectral range and spatial resolution, measurement mode, dispersive devices, detector arrays, and combination with other techniques. Image analysis methods for MHSI are summarized with an emphasis on preprocessing, feature extraction and selection, and classification methods. The section on applications refers to the available literature on disease diagnosis and surgery guidance. These applications mainly cover the ultraviolet (UV), visible (VIS), and near-infrared (near-IR or NIR) regions. Interested readers can refer to other review papers for more applications in mid-infrared (mid-IR or MIR) regions.21,22 Finally, we conclude with a discussion on the achievements of the past years and some future challenges.
The propagation of light within tissue is a significant problem in medical applications and in the development of diagnostic methods. Therefore, this section is dedicated to a brief review of the light tissue interaction mechanisms, optical processes involved in HSI, and useful diagnostic information provided by HSI.
Light entering biological tissue undergoes multiple scattering and absorption events as it propagates across the tissue.23 Biological tissues are heterogeneous in composition with spatial variations in optical properties.24 Scattering occurs where there is a spatial variation in the refractive index.24 In cellular media, the important scatters are the subcellular organelles, with their size running from <100&#;&#;nm to 6 μm. For example, mitochondria are the dominant scatterers among the organelles. The structure of a lipid membrane and lipid folds running inside gives mitochondria a high optical contrast to the surrounding cytoplasm and produces the observed strong scattering effects. The shape and size of the cells vary among different tissue types with dimensions of a few microns and larger.24 The scattering properties of support tissues composed of cells and extracellular proteins (elastin and collagen, etc.) are caused by the small-scale inhomogeneities and the large-scale variations in the structures they form.
The penetration depth of light into biological tissues depends on how strongly the tissue absorbs light. Most tissues are sufficiently weak absorbers to permit significant light penetration within the therapeutic window, ranging from 600 to nm.24 Within the therapeutic window, scattering is over absorption, so the propagating light becomes diffuse. Tissue absorption is a function of molecular composition. Molecules absorb photons when the photons&#; energy matches an interval between internal energy states, and the transition between quantum states obeys the selection rules for the species. During absorption processing, transitions between two energy levels of a molecule that are well defined at specific wavelengths could serve as a spectral fingerprint of the molecule for diagnostic purposes.24,25 For example, absorption spectra characterize the concentration and oxygen saturation of hemoglobin, which reveals two hallmarks of cancer: angiogenesis and hypermetabolism.16 Tissue components absorbing light are called chromophores. Some of the most important chromophores for visible wavelengths are blood and melanin, of which the absorption coefficient decreases monotonically with the increasing wavelength. The primary absorbers for UV wavelengths are protein and amino acids, while the important absorbing chromophore for IR wavelengths is water.26
Light absorbed by tissue constituents is either converted to heat or radiated in the form of luminescence, including fluorescence and phosphorescence.18,24,27 Fluorescence that originates from endogenous fluorescent chromophores is also called autofluorescence. Incident light, typically in the UV or VIS region, excites the tissue molecules and induces fluorescence emission. The majority of the endogenous fluorophores are associated with the structural matrix of tissue or with various cellular metabolic pathways.24,28 The most common fluorophores in the structural matrix are collagen and elastin, while the predominant fluorophores involved in cellular metabolism are the nicotinamide adenine dinucleotide (NADH), flavin adenine dinucleotide (FAD), and lipopigments.29 These intrinsic fluorophores exhibit different strengths and cover spectral ranges in the UV and VIS regions. For example, fluorescence from collagen or elastin using excitation between 300 and 400 nm shows broad emission bands between 400 and 600 nm, which can be used to distinguish various types of tissues, e.g., epithelial and connective tissue.30 Cells in different disease states often have different structures or undergo different rates of metabolism, which result in different fluorescence emission spectra. Therefore, fluorescence imaging makes it possible to investigate tissues for diagnosis of diseases in real time without administrating exogenous fluorescent agents.24 Various exogenous fluorophores have also been created and studied for biological diagnostics using HSI,29 but this review will mainly discuss the intrinsic fluorescence.
Incident light can be directly reflected on the surface of the tissue or be scattered due to random spatial variations in tissue density (membranes, nuclei, etc.) and then be remitted to the tissue surface.27 Light becomes randomized in direction due to multiple scattering, and this is known as diffuse reflectance, which provides information about scattering and absorbing components deep within the tissue.31 The measured reflectance signal represents light that has sampled a variety of sampling depths within the tissue and is, therefore, an average measure of the properties over a certain volume of tissue.31 Knowledge of the origin of the scattering and absorption signals would facilitate accurate modeling and interpretation of the reflectance data. The reflectance signal measured from epithelial tissue is determined by the structural and biochemical properties of the tissue; therefore, changes in optical properties can be used to noninvasively probe the tissue microenvironment.31 Alterations in tissue morphology, including hyperplasia, nuclear crowding, degradation of collagen in the extracellular matrix by matrix metalloproteinases, and increased nuclear/cytoplasmic ratio, which are associated with disease progression, can affect the scattering signals. As diseases progress, hemoglobin absorption may be affected by angiogenesis and tissue hypoxia, etc. Therefore, changes in the disease states should lead to corresponding changes in the patterns of light reflected from the tissue.
Reflectance imaging can detect local changes in scattering and absorption properties of tissue, and fluorescence imaging can probe changes in the biochemical composition of tissue by revealing levels of endogenous fluorophores.32 Multimodal HSI combining reflectance and fluorescence has been investigated for cancer diagnosis.19,33 Furthermore, the HSI system can be adapted to other existing techniques, such as microscope and colposcope, to provide complementary information in a more accurate and reliable manner. Transmission HSI microscope is one example of these combinatory technologies and has been used in tissue pathology.
Image analysis enables the extraction of diagnostically useful information from a large medical hyperspectral dataset at the tissue, cellular, and molecular levels and is, therefore, critical for disease screening, diagnosis, and treatment. Hypercube with high spatial and spectral resolution may potentially contain more diagnostic information. However, high spatial and spectral dimensions also make it difficult to perform automatic analysis of hyperspectral data. In particular, it is complex in many aspects: (1) high data redundancy due to high correlation in the adjacent bands, (2) variability of hyperspectral signatures, and (3) curse of dimensionality.125 With abundant spatial and spectral information available, advanced image classification methods for hyperspectral datasets are required to extract, unmix, and classify relevant spectral information. The goal is not only to discriminate between different tissues (such as healthy and malignant tissue) and provide diagnostic maps, but also to decompose mixtures into the spectra of pure molecular constituents and correlate these molecular fingerprints (biomarkers) with disease states. Although hyperspectral image analysis methods have been intensively investigated in the remote sensing area, their development and application in medical domain lag far behind. The relationships between spectral features and underlying biomedical mechanisms are not well understood. The basic steps for hyperspectral image analysis generally involve preprocessing, feature extraction and feature selection, and classification or unmixing.
HSI preprocessing mainly involves data normalization and image registration. Gaussian filter was also used in the literature to smooth spectral signatures and reduce the noise effect.108
Data normalization converts or normalizes hyperspectral radiance observations to reflectance93&#;126 or absorbance127,128 values that describe the intrinsic properties of biological samples. Such normalization also reduces system noise and image artifacts arising from uneven surface illumination or large redundant information in the subbands of hyperspectral imagery, and better prepares data for further analysis. Two most commonly used normalization methods are as follows:
CCD arrays used in HSI systems generally have dark current even without light shining on it. Dark current is dependent on temperature and is proportional to integration time. So, to convert raw intensity into reflectance, reference and dark images are taken before acquiring sample images. The reference image is taken with a standard reflectance surface placed in the scene, and the dark current is measured by keeping the camera shutter closed. Currently, the widely used standard reflectance surface is the National Institute of Standards and Technology certified 99% Spectralon white diffuse reflectance target. The raw data were then corrected using the following equation:93,126
Iref=Iraw&#;IdarkIwhite&#;Idark,
(1)
where Iref is the calculated reflectance value, Iraw is the raw data radiance value of a given pixel, and Idark and Iwhite are the dark current and the white reference intensity of the given pixel, respectively.
The absorbance Iabs is usually calculated by taking the ratio of the sample images (Iraw) with respect to a reference image (Iref).127,128
Iabs=&#;logIrawIref.
(2)
The reference material provides a measure of the instrument response function, and therefore, the method effectively ratios out the instrument response function from the resultant optical density image set.
Image registration finds a geometric transformation of multiple images of the same scene taken at different wavelengths. The correspondence between the images is maximized when an image pair is correctly aligned. To obtain accurate spectral information for each pixel, image registration may be necessary to spatially align all spectral band images within one hypercube or between different hypercubes. Kong et al.108 utilized mutual information (MI) as a metric for searching the offset of the band images along the horizontal axis, and an image pair with maximum MI shows the best match between a reference image and an input image. Each band image was spatially coregistered to eliminate the spectral offset caused during the image acquisition procedure. Panasyuk et al.45 performed image registration as a preprocessing step to account for slight motion during the imaging of anesthetized mice. Lange et al.129 developed an elastic image registration algorithm to match reflectance and fluorescence images to compensate for soft tissue movement during the acquisition of reflectance and fluorescence image cubes. A detailed description of image registration algorithms is beyond the scope of this paper. Interested readers may check relevant references to identify a suitable approach for a specific study.
The goal of feature extraction and selection is to obtain the most relevant information from the original data and represent that information in a lower-dimensionality space. For hyperspectral datasets, a larger number of spectral bands may potentially make the discrimination between more detailed classes possible. But due to the curse of dimensionality, too many spectral bands used in classification may decrease the classification accuracy.125 Moreover, not all of the intensities measured at a given wavelength are important for understanding the underlying characteristics of biological tissue17 since the reflectance or fluorescence features of biological tissue is wavelength dependent. Therefore, it is important to perform feature extraction and selection to extract the most relevant diagnostic information and process the dataset more efficiently and accurately. In hyperspectral datasets, each pixel can be represented in the form of an N-dimensional vector, where N is the number of the spectral bands. Such pixel-based representation has been widely used for hyperspectral image processing tasks. This method treats hyperspectral data as unordered listings of spectral measurements without particular spatial arrangement,130 which may result in a salt-and-pepper look for the classification map. Therefore, feature extraction methods incorporating both spatial and spectral information have been investigated intensively in the remote sensing area to improve classification accuracy. Recent advances of spatial-spectral classification have been summarized in Ref. 131.
To exploit the information in these datasets effectively, dimensionality reduction methods are required to extract the most useful information, reduce the dimensionality of the datasets, and handle highly correlated bands. Methods of dimensionality reduction can be divided into two categories: feature extraction and band selection. The most widely used dimensionality reduction method for medical hyperspectral dataset analysis is principle component analysis (PCA). PCA reduces redundant information in the bands of hyperspectral imagery while preserving as much of the variance in the high-dimensional space as possible. Assume a hypercube consists of N spectral images, and each image has a dimension of m×n;; then each image has M=m×n pixels, and the i&#;th pixel within an image can be represented as a spectra vector xi=[x1i,x2i,&#;,xNi]T, i=1,2,&#;,M. Therefore, each hypercube can be represented as an N×M matrix, where X=(x1,x2,&#;,xM). The steps to compute the PCA transform of the N×M matrix are as follows:132
1.
Center the matrix as X¯=[x1&#;μ,x2&#;μ,&#;,xM&#;μ], where μ=1M&#;i=1Mxi is the mean spectral vector of all pixels.
2.
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Compute the covariance matrix Σ=1M&#;i=1M(xi&#;μ)(xi&#;μ)T=X¯X¯T.
3.
Decompose the covariance matrix as Σ=UΛUT, where Λ=diag(λ1,λ2,&#;,λN) is a diagonal matrix with eigenvalues in the diagonal entries, and U=[u1,u2,&#;,uN]T is an orthonormal matrix composed of the corresponding eigenvectors u1,u2,&#;,uN.
4.
Sort the eigenvalues and eigenvectors in descending order, and the first K eigenvectors UK=(u1,u2,&#;,uK) are used to approximate the original images: zi=[z1i,z2i,&#;,zKi]T=UKTxi, where vector zi, i=[1,2,&#;M] will form the first K bands of the PCA images.
PCA of hyperspectral image data can highlight the relative distributions of different molecular component mixtures,46,133 identify key discriminative features,19,134 and estimate spectrum in the spectroscopic data.86 PCA is optimal in the sense of minimizing the mean square error. However, PCA transforms the original data to a subspace spanned by eigenvectors, which makes it difficult to interpret the biological meaning after transformation.
Several PCA variants, such as minimum noise fraction (MNF) and independent component analysis (ICA) are also used for feature extraction and dimensionality reduction. MNF transform is essentially two cascaded PCA transformations for reducing the spectra dimensionality and separating noise from the image data.50 ICA is also a useful extension of PCA by making the spectral features as independent as possible. The key idea of the ICA assumes that data are linearly mixed by a set of separate independent sources and demix these signal sources according to their statistical independency measured by mutual information.135
Hyperspectral image classification methods applied in the medical area mainly include pixel and subpixel classification based on the type of pixel information used. Pixel-wise classification can be parametric or nonparametric. Parametric classifiers generally assume normal distribution for the data, which is often violated in practice.136 Nonparametric methods, such as support vector machines (SVMs) and artificial neural networks (ANN) are widely used in medical hyperspectral image processing. The subpixel method assumes the spectral value of each pixel to be a linear or nonlinear combination of pure components. Pixel- and subpixel-based methods can be supervised or unsupervised. Commonly used supervised classification methods include SVMs, ANN, spectral information divergence (SID), and spectral angle mapper (SAM). The following sections will discuss some of these methods in detail.
SVM is a kernel-based machine learning technique that has been widely used in the classification of hyperspectral images.48,52,95,108,137&#;144 Due to its strong theoretical foundation, good generalization capability, low sensitivity to the curse of dimensionality,145 and ability to find global classification solutions, SVM is usually preferred by many researchers over other classification paradigms. Given training vectors xi&#;RN, i=1,2,&#;,M in two classes, and an indicator vector y=[y1,y2,&#;,yM]T&#;RM such that yi&#;{1,&#;1}, C-support vector classification146,147 solves the following primal optimization problem:
minw,b,ε&#;12wTw+C&#;i=1Mεisubject to&#;&#;yi(wTφ(xi)+b)&#;1&#;εi,εi&#;0,i=1,&#;,M.
(3)
φ(xi) maps xi into a higher-dimensional space and C>0 is the regularization parameter. Due to the possible high dimensionality of the vector variable w, usually we solve the following dual problem:
minα&#;12αTQα&#;eTα&#;subject to&#;&#;yTα=0,&#;0&#;αi&#;C,i=1,&#;,M.
(4)
e=[1,&#;,1]T is the vector of all ones, Q is an M by M positive semidefinite matrix, Qij&#;yiyjK(xi,xj), and K(xi,xj)&#;φ(xi)Tφ(xj) is the kernel function.
After Eq. (4) is solved, using the primal-dual relationship, the optimal w satisfies
w=&#;i=1Myiαiφ(xi).
(5)
So, for a new test point x, the decision function is
sgn(wTφ(x)+b)=sgn[&#;i=1MyiαiK(xi,x)+b].
(6)
SVM has been proved to perform well for classifying hyperspectral data.137 In the processing of medical hyperspectral data, SVM has also been explored for various classification tasks. Melgani and Bruzzone137 investigated the effectiveness of SVMs in the classification of hyperspectral remote sensing data. It was found that SVMs were much more effective than radial-basis function (RBF) neural networks and the K-nearest neighbor classifier in terms of classification accuracy, computational time, and stability to parameter settings. Kong et al.108 chose Gaussian RBF kernel as the kernel function for SVM and learned the SVM parameters from 100 training samples chosen randomly from each of the normal and tumor classes. For testing, (normal) and 517 (tumor) samples were used. Experimental results showed that the spatial filtering enhanced the performance, which resulted in an overall accuracy of 86%, while the use of the original data had an accuracy of 83%.
In our group, we used SVMs for various tissue classification tasks. In Ref. 52, Akbari et al. extracted and evaluated the spectral signatures of both cancerous and normal tissue and used least squares SVMs to classify prostate cancer tissue in tumor-bearing mice and on pathology slides. In Ref. 140, they created a library of spectral signatures for different tissues and discriminated between cancerous and noncancerous tissues in lymph nodes and lung tissues with SVMs. In Ref. 95, Akbari et al. constructed a library of spectral signatures from hyperspectral images of abdominal organs, arteries, and veins, and then differentiated between them using SVMs. In Ref. 48, they utilized least squares kernel SVMs to classify normal tissues and tumors based on their standard deviation and normalized difference index of spectral signature.
Neural network is another supervised classification method that has been adopted by many researchers,92,96,100,136 due to its nonparametric nature, arbitrary decision boundary, etc. Multilayer perceptron (MLP) is the most popular type of neural network in image classification.136 It is a feed-forward network trained by the backpropagation algorithm. Monteiro et al.93 implemented both single-layer perceptron (SLP) and MLP as supervised classifiers. The MLP notably generated the clearest visualization of the calendar&#;s number under the blood. Although the SLP was also able to learn a good visualization, the output presented more noise.
SID models the spectrum of a hyperspectral image pixel as a probability distribution in order to measure the discrepancy of probable behaviors between two spectra. Guan et al.58 used the SID technique to segment pathological white blood cells (WBCs) into four components: nucleus, cytoplasm, erythrocytes, and background. The SID method could not only distinguish different parts with similar gray values, e.g., in the case of cytoplasm and erythrocyte, but also segment WBCs accurately in spite of their irregular shapes and sizes.
SAM determines the spectral similarity by calculating the angle between the spectra and treating them as vectors in a space with dimensionality equal to the number of wavelengths. Martin et al.53 employed SAM algorithm to map the spectral similarity between image spectra and cluster spectra in order to perform supervised classification, and they found that SAM disregarded specific surface irregularities of the vocal cords that naturally led to inhomogeneous reflections in every patient. Li et al.59 used the SAM algorithm to identify the nerve fibers from the molecular hyperspectral images of nerve sections according to the difference of the spectral signatures of different parts.
One of the confounding factors in analyzing hyperspectral images is that the spectra at many pixels are actually mixtures of the spectra of the pure constituents. Spectral unmixing is a subpixel analysis method, which decomposes a mixed pixel into a collection of distinct spectra or endmembers, and a set of fractional abundances that indicate the proportion of each endmember.148 Spectral unmixing algorithms can be supervised or unsupervised. Supervised spectral unmixing relies on the prior knowledge about the reflectance patterns of candidate surface materials, while unsupervised unmixing aims to identify the endmembers and mixtures directly from the data without any user interaction.149 Many unmixing algorithms that were commonly used in the remote sensing area have been explored in medical HSI. Berman et al.71 implemented an unmixing method, i.e., iterated constrained endmembers, for hyperspectral data of cervical tissue. They identified cellular and morphological features as a prelude to construct a library of biologically interpretable endmembers. In another study, Constantinou et al.124 applied linear unmixing to hyperspectral images in order to remove autofluorescent signal contribution. It was considered that hyperspectral spatial spectrum is a combination of autofluorescence spectrum and other fluorescence spectrum of object tissues such as tumors. By decompositing the acquired spectra into different ones, autofluorescent signals can be removed or reduced. Sorg et al.38 performed spectral mixture analysis by utilizing a spectral angle-mapping technique in order to classify pixels as expressing green fluorescent protein (GFP) or red fluorescent protein (RFP).
Over the past 25 years, various studies have shown the exciting potential of HSI techniques in medical applications. Based on the technology by NASA for space exploration and Earth observation, HSI acquires datasets that consist of 2-D images of spatial information and one spectral dimension at each pixel. HSI images offer more wavelength channels than RGB images taken by the ordinary color camera; therefore they may carry more useful information than RGB images. Differences that appear subtle to the human eye could be significant when looking at the detailed spectra. In addition, wavelengths such as UV and SWIR, which are invisible to the human eye, can be captured and analyzed by HSI, and can potentially reveal information that cannot be seen by the naked eye.
MHSI is a noninvasive, and nonionizing technology, which provides a quantitative way of solving medical problems, and it may change the medical world in many ways. With the application of MHSI in the exploration of anatomy, physiology, and pathology, human vision has been extended into IR and near-IR wavelength regions. Due to the noninvasive nature, MHSI can be used for optical biopsy, which involves in vivo diagnosis of tissue without the need for sample excision and processing.201 Blood volume is generally considered to increase during angiogenesis, and changes in blood oxygenation can be correlated with tumor metabolic activities.42 Therefore, MHSI can be employed to map the spatial and temporal relationship of the data and fully grasp the significance of blood oxygen delivery and hypoxia at microvascular levels during tumor growth and angiogenesis.76 MHSI is also able to visualize chemical contents of vessels and organs, and monitor tissue blood volume and oxygenation during surgery. The use of MHSI does not require introduction of agents, which is advantageous compared to imaging techniques that require contrast agents. Moreover, MHSI is able to provide us with real-time data interactively,189 which enables its usage during surgical procedures.
However, the application of MHSI can be limited because it examines only areas of tissue near the surface. The optical penetration depth is defined as the tissue thickness that reduces the light intensity to 37% of the intensity at the surface. For a typical person, the optical penetration depth is 3.57 mm at 850 nm and 0.48 mm at 550 nm. While spectral signatures have little dependence on skin temperature over the NIR region, measured radiance in the thermal infrared (8 to 12 μm) has a strong dependence on skin temperature.202 MHSI can also be limited by the cost of HSI imaging systems and by the ability to extract relevant information from large datasets.
HSI combines spectroscopy with imaging, capturing both the spectral and spatial information of biological samples under investigation and providing spatial mapping of parameters of interest in a noninvasive manner. Spectroscopy is a point-measurement method that measures only a limited number of points, so that the derived optical properties may be biased by local tissue inhomogeneities and important diagnostic information could be missed. Pressure caused by the contact probe may also affect the optical properties due to the altered local blood content, etc.203 Although spectroscopy has been explored extensively for probing molecular, cellular, and tissue properties204&#;206 and characterizing correlation of tissue parameters with disease state,207 such fundamental research has not been investigated vigorously in HSI. Therefore, fundamental research about the biological rationale of MHSI is necessary, and spectroscopy can be used to validate HSI systems. It was argued that cross-talk between spatial locations could occur when extending to HSI, and the information extracted from one location might be influenced by neighboring locations.208 Martin et al.83 compared the average hyperspectral fluorescence over an area with a value obtained for one point on the tissue surface obtained by spectroscopy. They found that the major peaks were consistent between the HSI data and spectroscopic data.
HSI technology is an indirect strategy to extract a spatial map of optical properties within the tissue since it deduces the interaction coefficients from measurements of reflectance and transmittance.209 This is an ill-posed inverse problem with no unique solution.210 It is possible for two media of substantially different optical properties to yield very similar optical measurements, such as the diffuse reflectance and transmittance.209 In practice, it is difficult to eliminate the ambiguities of matching spectral profiles with biological samples, and therefore, the presence of the fundamental nonuniqueness is another limitation of HSI.
HSI can measure significant amounts of spectral information from a large area of tissue. Most literature reported the feasibility of a certain MHSI application without in-depth analysis of the image data obtained. Some results may suffer from a lack of generality because the image datasets are usually constrained to a specific instrument. Therefore, accessible, accurate, and up-to-date spectral databases of tissues, cells, and molecules for various diseases are needed in order to offer a valuable tool for disease diagnosis and treatment. For example, each subtype of renal tumors, such as clear cell, chromophobe, oncocytoma, papillary, and angiomyolipoma, can have different morphological and molecular characteristics and thus lead to the differences in spectra signature. Therefore, a spectral library for renal tumors may be able to provide the reference spectra in order to aid the interpretation of hyperspectral images. Furthermore, advanced data-mining methods are to be investigated in order to fully utilize the abundant spectral and spatial information provided by MHSI.
With the increasing integration with other techniques, such as microscope, colposcope, laparoscope, and fundus camera, MSHI is becoming an essential part of medical imaging techniques, which provides important information at the molecular, cellular, tissue, and organ levels for potential clinical use.
HSI technology acquires a 3-D image cube with two spatial dimensions and one spectral dimension in a noninvasive manner and in real time. Each pixel in the hypercube can be characterized by a spectral curve, which can range from UV to IR region. Spatially resolved spectra obtained by HSI provide diagnostic information about the tissue physiology, morphology, and composition. Furthermore, HSI can be easily adapted to other conventional techniques, such as microscopy, fundus camera, colposcopy, etc. As an emerging imaging technology, MHSI has been explored in a variety of laboratory experiments and clinical trials, which strongly suggested that HSI has a great potential for improving accuracy and reliability in disease detection, diagnosis, monitoring, and image-guided surgeries.
Three major challenges confront the development and applications of HSI technology. The first challenge is the acquisition of high-resolution HSI datasets in video rates. Real-time acquisition will facilitate intraoperative imaging of the organs, tissues, cells, and molecular biomarkers of interest. Higher spectral and spatial resolution and a larger database of tissue spectra will provide more spatial and spectral information and may potentially capture more subtle spectral and spatial variations of different tissue types.
The second challenge involves the fast processing of the vast amount of datasets acquired by HSI, including the extraction of the high-quality diagnostic information, and generation of a quantitative map of different tissue types as well as disease-specific endogenous substances. Advanced classification algorithms will enable better differentiation between healthy, premalignant, and malignant tissue, and more precise delineation of cancer margins for image-guided biopsy and surgery. Advanced spectral unmixing algorithms offer insight into the correlation between intrinsic biomarkers and disease states, and facilitate the identification of biomarkers for early cancer detection by recovering subpixel compositional information.
The third challenge lies in the establishment of a large spectra database for important molecular biomarkers and all types of tissue, including skin and subcutaneous tissue, ocular tissue, head/brain tissue, epithelial/mucous tissue, breast tissue, cartilage, liver, muscle, aorta, lung, myocardium, etc. Such a database will make it possible to distinguish not only between oxygenated and deoxygenated blood, but also between different tissue types, such as bile duct and the fatty tissue surrounding it.196 The identification of the molecular biomarkers can also benefit early cancer detection.
During the past two decades, HSI technology has undergone fast development in terms of hardware and systems, and has found numerous applications in medical domain. However, most MHSI only explores the UV, VIS, and NIR regions of light. Exploration of HSI on disease detection, diagnosis, and monitoring in the mid-IR region may bring new insights into the medical field. Moreover, combination with other imaging modalities, such as preoperative positron emission tomography and intraoperative ultrasound, can leverage the key benefits of each technique individually, overcome the penetration limitation of HSI into biological tissue,211 and broaden the application fields of HSI. In clinical settings, HSI can be easily adapted to conventional diagnostic tools, such as endoscope, colposcope, etc., to meet demanding requirements by various medical applications. Multimodal imaging combining reflectance and fluorescence has the potential of revealing more information about tissue under investigation. The clinical applicability of MHSI is clearly still in its adolescence and requires much more validation before it can be used safely and effectively in clinics. With the advancement of hardware technologies, image analysis methods, and computational power, we expect that HSI will play an important role for noninvasive disease diagnosis and monitoring, identification and quantitative analysis of cancer biomarkers, image-guided minimum invasive surgery, targeted drug delivery and tracking, and pharmaceutical drug dosage assessment.
This research is supported in part by National Institute of Health grants (R01CA and R21CA), Georgia Research Alliance Distinguished Scientists Award, Emory SPORE in Head and Neck Cancer (NIH P50CA), and Emory Molecular and Translational Imaging Center (NIH P50CA).
&#;
Guolan Lu is a PhD student in the Department of Biomedical Engineering at Emory University and Georgia Institute of Technology, Atlanta, Georgia.
&#;
Baowei Fei is associate professor in the Department of Radiology and Imaging Sciences at Emory University and in the Department of Biomedical Engineering at Emory University and Georgia Institute of Technology. He received his MS and PhD degrees from Case Western Reserve University, Cleveland, Ohio. He is a Georgia Cancer Coalition Distinguished Scholar and Director of the Quantitative BioImaging Laboratory (www.feilab.org) at Emory University School of Medicine.
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