Optical spectroscopy detects histological hallmarks of pancreatic cancer

Endoscopic Hyperspectral Imaging System to Discriminate Tissue Characteristics in Tissue Phantom and Orthotopic Mouse Pancreatic Tumor Model

In this study, we developed an endoscopic hyperspectral imaging (eHSI) system and evaluated its performance in analyzing tissues within tissue phantoms and orthotopic mouse pancreatic tumor models. Our custom-built eHSI system incorporated a liquid crystal tunable filter. To assess its tissue discrimination capabilities, we acquired images of tissue phantoms, distinguishing between fat and muscle regions. The system underwent supervised training using labeled samples, and this classification model was then applied to other tissue phantom images for evaluation. In the tissue phantom experiment, the eHSI effectively differentiated muscle from fat and background tissues. The precision scores regarding fat tissue classification were 98.3% for the support vector machine, 97.7% for the neural network, and 96.0% with a light gradient-boosting machine algorithm, respectively. Furthermore, we applied the eHSI system to identify tumors within an orthotopic mouse pancreatic tumor model. The F-score of each pancreatic tumor-bearing model reached 73.1% for the KPC tumor model and 63.1% for the Pan02 tumor models. The refined imaging conditions and optimization of the fine-tuning of classification algorithms enhance the versatility and diagnostic efficacy of eHSI in biomedical applications.

Ultrasound-guided Photoacoustic image Annotation Toolkit in MATLAB (PHANTOM) for preclinical applications

Depth-dependent fluence-compensation in photoacoustic (PA) imaging is paramount for accurate quantification of chromophores from deep tissues. Here we present a user-friendly toolkit named PHANTOM (PHotoacoustic ANnotation TOolkit for MATLAB) that includes a graphical interface and assists in the segmentation of ultrasound-guided PA images. We modelled the light source configuration with Monte Carlo eXtreme and utilized 3D segmented tissues from ultrasound to generate fluence maps to depth compensate PA images. The methodology was used to analyze PA images of phantoms with varying blood oxygenation and results were validated with oxygen electrode measurements. Two preclinical models, a subcutaneous tumor and a calcified placenta, were imaged and fluence-compensated using the PHANTOM toolkit and the results were verified with immunohistochemistry. The PHANTOM toolkit provides scripts and auxiliary functions to enable biomedical researchers not specialized in optical imaging to apply fluence correction to PA images, enhancing accessibility of quantitative PAI for researchers in various fields.

Müller matrix polarimetry for pancreatic tissue characterization

Polarimetry is an optical characterization technique capable of analyzing the polarization state of light reflected by materials and biological samples. In this study, we investigate the potential of Müller matrix polarimetry (MMP) to analyze fresh pancreatic tissue samples. Due to its highly heterogeneous appearance, pancreatic tissue type differentiation is a complex task. Furthermore, its challenging location in the body makes creating direct imaging difficult. However, accurate and reliable methods for diagnosing pancreatic diseases are critical for improving patient outcomes. To this end, we measured the Müller matrices of ex-vivo unfixed human pancreatic tissue and leverage the feature-learning capabilities of a machine-learning model to derive an optimized data representation that minimizes normal-abnormal classification error. We show experimentally that our approach accurately differentiates between normal and abnormal pancreatic tissue. This is, to our knowledge, the first study to use ex-vivo unfixed human pancreatic tissue combined with feature-learning from raw Müller matrix readings for this purpose.

Gold Nanoparticles-Mediated Photothermal Therapy of Pancreas Using GATE: A New Simulation Platform

This work presents the first investigation of gold nanorods (GNRs)-based photothermal therapy of the pancreas tumor using the Monte Carlo-based code implemented with Geant4 Application for Emission Tomography (GATE). The model of a human pancreas was obtained by segmenting an abdominal computed tomography (CT) scan, and its physical and chemical properties, were obtained from experimental and theoretical data. In GATE, GNRs-mediated hyperthermal therapy, simple heat diffusion as well as interstitial laser ablation were then modeled in the pancreas tumor by defining the optical parameters of this tissue when it is loaded with GNRs. Two different experimental setups on ex vivo pancreas tissue and GNRs-embedded water were devised to benchmark the developed Monte Carlo-based model for the hyperthermia in the pancreas alone and with GNRs, respectively. The influence of GNRs on heat distribution and temperature increase within the pancreas tumor was compared for two different power values (1.2 W and 2.1 W) when the tumor was exposed to 808 nm laser irradiation and with two different laser applicator diameters. Benchmark tests demonstrated the possibility of the accurate simulating of NPs-assisted thermal therapy and reproducing the experimental data with GATE software. Then, the output of the simulated GNR-mediated hyperthermia emphasized the importance of the precise evaluation of all of the parameters for optimizing the preplanning of cancer thermal therapy. Simulation results on temperature distribution in the pancreas tumor showed that the temperature enhancement caused by raising the power was increased with time in both the tumor with and without GNRs, but it was higher for the GNR-load tumor compared to tumor alone.

Estimation of porcine pancreas optical properties in the 600-1100 nm wavelength range for light-based therapies

This work reports the optical properties of porcine pancreatic tissue in the broad wavelength range of 600–1100 nm. Absorption and reduced scattering coefficients (µ_a and µ_s′) of the ex vivo pancreas were obtained by means of Time-domain Diffuse Optical Spectroscopy. We have investigated different experimental conditions—including compression, repositioning, spatial sampling, temporal stability—the effect of the freezing procedure (fresh vs frozen-thawed pancreas), and finally inter-sample variability. Good repeatability under different experimental conditions was obtained (median coefficient of variation less than 8% and ~ 16% for µ_a and µ_s′, respectively). Freezing–thawing the samples caused an irreversible threefold reduction of µ_s′ and no effect on µ_a. The absorption and reduced scattering spectra averaged over different samples were in the range of 0.12–0.74 cm⁻¹ and 12–21 cm⁻¹ with an inter-sample variation of ~ 10% and ~ 40% for µ_a and µ_s′, respectively. The calculated effective transport coefficient (µ_eff) for fresh pancreatic tissue shows that regions between 800–900 nm and 1050–1100 nm are similar and offer the lowest tissue attenuation in the considered range (i.e., µ_eff ranging from 2.4 to 2.7 cm⁻¹). These data, describing specific light-pancreas interactions in the therapeutic optical window for the first time, provide pivotal information for planning of light-based thermotherapies (e.g., laser ablation) and instruction of light transport models for biophotonic applications involving this organ.

Needle-compatible miniaturized optoelectronic sensor for pancreatic cancer detection

Pancreatic cancer is one of the deadliest cancers, with a 5-year survival rate of <10%. The current approach to confirming a tissue diagnosis, endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA), requires a time-consuming, qualitative cytology analysis and may be limited because of sampling error. We designed and engineered a miniaturized optoelectronic sensor to assist in situ, real-time, and objective evaluation of human pancreatic tissues during EUS-FNA. A proof-of-concept prototype sensor, compatible with a 19-gauge hollow-needle commercially available for EUS-FNA, was constructed using microsized optoelectronic chips and microfabrication techniques to perform multisite tissue optical sensing. In our bench-top verification and pilot validation during surgery on freshly excised human pancreatic tissues (four patients), the fabricated sensors showed a comparable performance to our previous fiber-based system. The flexibility in source-detector configuration using microsized chips potentially allows for various light-based sensing techniques inside a confined channel such as a hollow needle or endoscopy.

Wavelength weightings in machine learning for ovine joint tissue differentiation using diffuse reflectance spectroscopy (DRS)

Objective: To investigate the DRS of ovine joint tissue to determine the optimal optical wavelengths for tissue differentiation and relate these wavelengths to the biomolecular composition of tissues. In this study, we combine machine learning with DRS for tissue classification and then look further at the weighting matrix of the classifier to further understand the key differentiating features. Methods: Supervised machine learning was used to analyse DRS data. After normalising the data, dimension reduction was achieved through multiclass Fisher's linear discriminant analysis (Multiclass FLDA) and classified with linear discriminant analysis (LDA). The classifier was first run with all the tissue types and the wavelength range 190 nm - 1081 nm. We analysed the weighting matrix of the classifier and then ran the classifier again, the first time using the ten highest weighted wavelengths and the second using only the single highest. Our method was applied to a dataset containing ovine joint tissue including cartilage, cortical and subchondral bone, fat, ligament, meniscus, and muscle. Results: It achieved a classification accuracy of 100% using the wavelength 190 nm - 1081 nm (2048 attributes) with an accuracy of 90% being present for 10 attributes with the exception of those with comparable compositions such as ligament and meniscus. An accuracy greater than 70% was achieved using a single wavelength, with the same exceptions. Conclusion: Multiclass FLDA combined with LDA is a viable technique for tissue identification from DRS data. The majority of differentiating features existed within the wavelength ranges 370-470 and 800-1010 nm. Focusing on key spectral regions means that a spectrometer with a narrower range can potentially be used, with less computational power needed for subsequent analysis.

Optical Metric Assessed Engineered Tissues Over a Range of Viability States

Many conventional methods to assess engineered tissue morphology and viability are destructive techniques with limited utility for tissue constructs intended for implantation in patients. Sterile label-free optical molecular imaging methods analyzed tissue endogenous fluorophores without staining, noninvasively and quantitatively assessing engineered tissue, in lieu of destructive assessment methods. The objective of this study is to further investigate label-free optical metrics and their correlation with destructive methods. Tissue-engineered constructs (n = 33 constructs) fabricated with primary human oral keratinocytes (n = 10 patients) under control, thermal stress, and rapamycin treatment manufacturing conditions exhibited a range of tissue viability states, as evaluated by quantitative histology scoring, WST-1 assay, Ki-67 immunostaining imaging, and label-free optical molecular imaging methods. Both histology sections of fixed tissues and cross-sectioned label-free optical images of living tissues provided quantitative spatially selective information on local tissue morphology, but optical methods noninvasively characterized both local tissue morphology and cellular viability at the same living tissue site. Furthermore, optical metrics noninvasively assessed living tissue viability with a statistical significance consistent with the destructive tissue assays WST-1 and histology. Over the range of cell viability states created experimentally, optical metrics noninvasively and quantitatively characterized living tissue viability and correlated with the destructive WST-1 tissue assay. By providing, under sterile conditions, noninvasive metrics that were comparable with conventional destructive tissue assays, label-free optical molecular imaging has the potential to monitor and assess engineered tissue construct viability before surgical implantation.

Surgical spectral imaging

Recent technological developments have resulted in the availability of miniaturised spectral imaging sensors capable of operating in the multi- (MSI) and hyperspectral imaging (HSI) regimes. Simultaneous advances in image-processing techniques and artificial intelligence (AI), especially in machine learning and deep learning, have made these data-rich modalities highly attractive as a means of extracting biological information non-destructively. Surgery in particular is poised to benefit from this, as spectrally-resolved tissue optical properties can offer enhanced contrast as well as diagnostic and guidance information during interventions. This is particularly relevant for procedures where inherent contrast is low under standard white light visualisation. This review summarises recent work in surgical spectral imaging (SSI) techniques, taken from Pubmed, Google Scholar and arXiv searches spanning the period 2013-2019. New hardware, optimised for use in both open and minimally-invasive surgery (MIS), is described, and recent commercial activity is summarised. Computational approaches to extract spectral information from conventional colour images are reviewed, as tip-mounted cameras become more commonplace in MIS. Model-based and machine learning methods of data analysis are discussed in addition to simulation, phantom and clinical validation experiments. A wide variety of surgical pilot studies are reported but it is apparent that further work is needed to quantify the clinical value of MSI/HSI. The current trend toward data-driven analysis emphasises the importance of widely-available, standardised spectral imaging datasets, which will aid understanding of variability across organs and patients, and drive clinical translation.

Machine learning classification of human joint tissue from diffuse reflectance spectroscopy data

Objective: To assess if incorporation of DRS sensing into real-time robotic surgery systems has merit. DRS as a technology is relatively simple, cost-effective and provides a non-contact approach to tissue differentiation. Methods: Supervised machine learning analysis of diffuse reflectance spectra was performed to classify human joint tissue that was collected from surgical procedures. Results: We have used supervised machine learning in the classification of a DRS human joint tissue data set and achieved classification accuracy in excess of 99%. Sensitivity for the various classes were; cartilage 99.7%, subchondral 99.2%, meniscus 100% and cancellous 100%. Full wavelength range is required for maximum classification accuracy. The wavelength resolution must be larger than 8nm. A SNR better than 10:1 was required to achieve a classification accuracy greater than 50%. The 800-900nm wavelength range gave the greatest accuracy amongst those investigated Conclusion: DRS is a viable method for differentiating human joint tissue and has the potential to be incorporated into robotic orthopaedic surgery.

Tissue Classification Using Optical Spectroscopy Accurately Differentiates Cancer and Chronic Pancreatitis

OBJECTIVES

Current pancreatic cancer diagnostics cannot reliably detect early disease or distinguish it from chronic pancreatitis. We test the hypothesis that optical spectroscopy can accurately differentiate cancer from chronic pancreatitis and normal pancreas. We developed and tested clinically compatible multimodal optical spectroscopy technology to measure reflectance and endogenous fluorescence from human pancreatic tissues.

METHODS

Freshly excised pancreatic tissue specimens (39 normal, 34 chronic pancreatitis, 32 adenocarcinoma) from 18 patients were optically interrogated, with site-specific histopathology representing the criterion standard. A multinomial logistic model using principal component analysis and generalized estimating equations provided statistically rigorous tissue classification.

RESULTS

Optical spectroscopy distinguished pancreatic cancer from normal pancreas and chronic pancreatitis (sensitivity, 91%; specificity, 82%; positive predictive value, 69%; negative predictive value, 95%; area under receiver operating characteristic curve, 0.89). Reflectance alone provided essentially the same classification accuracy as reflectance and fluorescence combined, suggesting that a rapid, low-cost, reduced-footprint, reflectance-based device could be deployed without notable loss of diagnostic power.

CONCLUSIONS

Our novel, clinically compatible, label-free optical diagnostic technology accurately characterizes pancreatic tissues. These data provide the scientific foundation demonstrating that optical spectroscopy can potentially improve diagnosis of pancreatic cancer and chronic pancreatitis.

Limitations of the commonly used simplified laterally uniform optical fiber probe-tissue interface in Monte Carlo simulations of diffuse reflectance

Light propagation models often simplify the interface between the optical fiber probe tip and tissue to a laterally uniform boundary with mismatched refractive indices. Such simplification neglects the precise optical properties of the commonly used probe tip materials, e.g. stainless steel or black epoxy. In this paper, we investigate the limitations of the laterally uniform probe-tissue interface in Monte Carlo simulations of diffuse reflectance. In comparison to a realistic probe-tissue interface that accounts for the layout and properties of the probe tip materials, the simplified laterally uniform interface is shown to introduce significant errors into the simulated diffuse reflectance.

Correction for tissue optical properties enables quantitative skin fluorescence measurements using multi-diameter single fiber reflectance spectroscopy

BACKGROUND AND OBJECTIVE

Fluorescence measurements in the skin are very much affected by absorption and scattering but existing methods to correct for this are not applicable to superficial skin measurements.

STUDY DESIGN/MATERIALS AND METHODS

The first use of multiple-diameter single fiber reflectance (MDSFR) and single fiber fluorescence (SFF) spectroscopy in human skin was investigated. MDSFR spectroscopy allows a quantification of the full optical properties in superficial skin (μa, μs' and γ), which can next be used to retrieve the corrected - intrinsic - fluorescence of a fluorophore Qμa,x(f). Our goal was to investigate the importance of such correction for individual patients. We studied this in 22 patients undergoing photodynamic therapy (PDT) for actinic keratosis.

RESULTS

The magnitude of correction of fluorescence was around 4 (for both autofluorescence and protoporphyrin IX). Moreover, it was variable between patients, but also within patients over the course of fractionated aminolevulinic acid PDT (range 2.7-7.5). Patients also varied in the amount of protoporphyrin IX synthesis, photobleaching percentages and resynthesis (>100× difference between the lowest and highest PpIX synthesis). The autofluorescence was lower in actinic keratosis than contralateral normal skin (0.0032 versus 0.0052; P<0.0005).

CONCLUSIONS

Our results clearly demonstrate the importance of correcting the measured fluorescence for optical properties, because these vary considerably between individual patients and also during PDT. Protoporphyrin IX synthesis and photobleaching kinetics allow monitoring clinical PDT which facilitates individual-based PDT dosing and improvement of clinical treatment protocols. Furthermore, the skin autofluorescence can be relevant for diagnostic use in the skin, but it may also be interesting because of its association with several internal diseases.

Extraction of intrinsic fluorescence from single fiber fluorescence measurements on a turbid medium: experimental validation

The detailed mechanisms associated with the influence of scattering and absorption properties on the fluorescence intensity sampled by a single optical fiber have recently been elucidated based on Monte Carlo simulated data. Here we develop an experimental single fiber fluorescence (SFF) spectroscopy setup and validate the Monte Carlo data and semi-empirical model equation that describes the SFF signal as a function of scattering. We present a calibration procedure that corrects the SFF signal for all system-related, wavelength dependent transmission efficiencies to yield an absolute value of intrinsic fluorescence. The validity of the Monte Carlo data and semi-empirical model is demonstrated using a set of fluorescent phantoms with varying concentrations of Intralipid to vary the scattering properties, yielding a wide range of reduced scattering coefficients (μ's = 0-7 mm (-1)). We also introduce a small modification to the model to account for the case of μ's = 0 mm (-1) and show its relation to the experimental, simulated and theoretically calculated value of SFF intensity in the absence of scattering. Finally, we show that our method is also accurate in the presence of absorbers by performing measurements on phantoms containing red blood cells and correcting for their absorption properties.

CT contrast predicts pancreatic cancer treatment response to verteporfin-based photodynamic therapy

The goal of this study was to determine dominant factors affecting treatment response in pancreatic cancer photodynamic therapy (PDT), based on clinically available information in the VERTPAC-01 trial. This trial investigated the safety and efficacy of verteporfin PDT in 15 patients with locally advanced pancreatic adenocarcinoma. CT scans before and after contrast enhancement from the 15 patients in the VERTPAC-01 trial were used to determine venous-phase blood contrast enhancement and this was correlated with necrotic volume determined from post-treatment CT scans, along with estimation of optical absorption in the pancreas for use in light modeling of the PDT treatment. Energy threshold contours yielded estimates for necrotic volume based on this light modeling. Both contrast-derived venous blood content and necrotic volume from light modeling yielded strong correlations with observed necrotic volume (R² = 0.85 and 0.91, respectively). These correlations were much stronger than those obtained by correlating energy delivered versus necrotic volume in the VERTPAC-01 study and in retrospective analysis from a prior clinical study. This demonstrates that contrast CT can provide key surrogate dosimetry information to assess treatment response. It also implies that light attenuation is likely the dominant factor in the VERTPAC treatment response, as opposed to other factors such as drug distribution. This study is the first to show that contrast CT provides needed surrogate dosimetry information to predict treatment response in a manner which uses standard-of-care clinical images, rather than invasive dosimetry methods.

Influence of the phase function in generalized diffuse reflectance models: review of current formalisms and novel observations

Diffuse reflectance spectroscopy, which has been demonstrated as a noninvasive diagnostic technique, relies on quantitative models for extracting optical property values from turbid media, such as biological tissues. We review and compare reflectance models that have been published, and we test similar models over a much wider range of measurement parameters than previously published, with specific focus on the effects of the scattering phase function and the source-detector distance. It has previously been shown that the dependence of a forward reflectance model on the scattering phase function can be described more accurately using a variable, γ, which is a more predictive variable for reflectance than the traditional anisotropy factor, g. We show that variations in the reflectance model due to the phase function are strongly dependent on the source-detector separation, and we identify a dimensionless scattering distance at which reflectance is insensitive to the phase function. Further, we evaluate how variations in the phase function and source-detector separation affect the accuracy of inverse property extraction. By simultaneously fitting two or more reflectance spectra, measured at different source-detector separations, we also demonstrate that an estimate of γ can be extracted, in addition to the reduced scattering and absorption coefficients.

In vivo optical spectroscopy for improved detection of pancreatic adenocarcinoma: a feasibility study

Pancreatic adenocarcinoma has a five-year survival rate of less than 6%. This low survival rate is attributed to the lack of accurate detection methods, which limits diagnosis to late-stage disease. Here, an in vivo pilot study assesses the feasibility of optical spectroscopy to improve clinical detection of pancreatic adenocarcinoma. During surgery on 6 patients, we collected spectrally-resolved reflectance and fluorescence in vivo. Site-matched in vivo and ex vivo data agreed qualitatively and quantitatively. Quantified differences between adenocarcinoma and normal tissues in vivo were consistent with previous results from a large ex vivo data set. Thus, optical spectroscopy is a promising method for the improved diagnosis of pancreatic cancer in vivo.

Toward transduodenal diffuse optical tomography of proximal pancreas

We demonstrate the feasibility of diffuse optical tomography (DOT) of the proximal pancreas by using optical applicator channels deployed longitudinally along the exterior surface of a duodenoscope. As the duodenum that nearly encircles the proximal pancreas forms a natural "C-loop" that is approximately three-quarters of a circle of 5-6 cm in diameter, a multichannel optical applicator attached to a duodenoscope has the potential to perform transduodenal DOT sampling of the bulk proximal pancreas wherein most cancers and many cystic lesions occur. The feasibility of transduodenal DOT is demonstrated on normal porcine pancreas tissues containing an introduced gelatinous inclusion of approximately 3 cm in diameter, by using nine source channels and six detector channels attached to a duodenoscope. Concurrent ultrasonography of the gelatinous inclusion in the porcine pancreas parenchyma provided a coarse, albeit indispensable, anatomic prior to transduodenal DOT in reconstructing a contrast of optical properties in the pancreas.

Characterizing human pancreatic cancer precursor using quantitative tissue optical spectroscopy

In a pilot study, multimodal optical spectroscopy coupled with quantitative tissue-optics models distinguished intraductal papillary mucinous neoplasm (IPMN), a common precursor to pancreatic cancer, from normal tissues in freshly excised human pancreas. A photon-tissue interaction (PTI) model extracted parameters associated with cellular nuclear size and refractive index (from reflectance spectra) and extracellular collagen content (from fluorescence spectra). The results suggest that tissue optical spectroscopy has the potential to characterize pre-cancerous neoplasms in human pancreatic tissues.

Nonlinear optical microscopy for histology of fresh normal and cancerous pancreatic tissues

BACKGROUND

Pancreatic cancer is a lethal disease with a 5-year survival rate of only 1-5%. The acceleration of intraoperative histological examination would be beneficial for better management of pancreatic cancer, suggesting an improved survival. Nonlinear optical methods based on two-photon excited fluorescence (TPEF) and second harmonic generation (SHG) of intrinsic optical biomarkers show the ability to visualize the morphology of fresh tissues associated with histology, which is promising for real-time intraoperative evaluation of pancreatic cancer.

METHODOLOGY/PRINCIPAL FINDINGS

In order to investigate whether the nonlinear optical imaging methods have the ability to characterize pancreatic histology at cellular resolution, we studied different types of pancreatic tissues by using label-free TPEF and SHG. Compared with other routine methods for the preparation of specimens, fresh tissues without processing were found to be most suitable for nonlinear optical imaging of pancreatic tissues. The detailed morphology of the normal rat pancreas was observed and related with the standard histological images. Comparatively speaking, the preliminary images of a small number of chemical-induced pancreatic cancer tissues showed visible neoplastic differences in the morphology of cells and extracellular matrix. The subcutaneous pancreatic tumor xenografts were further observed using the nonlinear optical microscopy, showing that most cells are leucocytes at 5 days after implantation, the tumor cells begin to proliferate at 10 days after implantation, and the extracellular collagen fibers become disordered as the xenografts grow.

CONCLUSIONS/SIGNIFICANCE

In this study, nonlinear optical imaging was used to characterize the morphological details of fresh pancreatic tissues for the first time. We demonstrate that it is possible to provide real-time histological evaluation of pancreatic cancer by the nonlinear optical methods, which present an opportunity for the characterization of the progress of spontaneous pancreatic cancer and further application in a non-invasive manner.

Semi-empirical model of the effect of scattering on single fiber fluorescence intensity measured on a turbid medium

Quantitative determination of fluorophore content from fluorescence measurements in turbid media, such as tissue, is complicated by the influence of scattering properties on the collected signal. This study utilizes a Monte Carlo model to characterize the relationship between the fluorescence intensity collected by a single fiber optic probe (F(SF)) and the scattering properties. Simulations investigate a wide range of biologically relevant scattering properties specified independently at excitation (λ(x)) and emission (λ(m)) wavelengths, including reduced scattering coefficients in the range μ'(s)(λ(x)) ∈ [0.1 - 8]mm(-1) and μ'(s)(λ(m)) ∈ [0.25 - 1] × μ'(s)(λ(x)). Investigated scattering phase functions (P(θ)) include both Henyey-Greenstein and Modified Henyey-Greenstein forms, and a wide range of fiber diameters (d(f) ∈ [0.2 - 1.0] mm) was simulated. A semi-empirical model is developed to estimate the collected F(SF) as the product of an effective sampling volume, and the effective excitation fluence and the effective escape probability within the effective sampling volume. The model accurately estimates F(SF) intensities (r=0.999) over the investigated range of μ'(s)(λ(x)) and μ'(s)(λ(m)), is insensitive to the form of the P(θ), and provides novel insight into a dimensionless relationship linking F(SF) measured by different d(f).

Temporal variations of skin pigmentation in C57BL/6 mice affect optical bioluminescence quantitation

PURPOSE

Depilation-induced skin pigmentation in C57Bl/6 mice is a known occurrence, and presents a unique problem for quantitative optical imaging of small animals, especially for bioluminescence. The work reported here quantitatively investigated the optical attenuation of bioluminescent light due to melanin pigmentation in the skin of transgenic C57Bl/6 mice, modified such that luciferase expression is under the transcription control of a physiologically and pharmacologically inducible gene.

PROCEDURE

Both in vivo and ex vivo experiments were performed to track bioluminescence signal attenuation through different stages of the mouse hair growth cycle. Simultaneous reflectance measurements were collected in vivo to estimate melanin levels.

RESULTS

Biological variability of skin pigmentation was found to dramatically affect collected bioluminescent signal emerging through the skin of the mice. When compared to signal through skin with no pigmentation, the signal through highly pigmented skin was attenuated an average of 90%. Positive correlation was found between reflectance measurements and bioluminescence signal loss. A correction scheme is proposed based on this correlation, but signal variation due to non-melanin scattering and absorption sources introduce significant errors. Advanced spectral reflectance analysis will be necessary to develop a more reliable correction method in the future.

CONCLUSION

Skin pigmentation is a significant variable in bioluminescent imaging, and should be considered in experimental design and implementation for longitudinal studies, and especially when sensitivity to small signal changes, or differences among animals, is required.

Measurement of tissue scattering properties using multi-diameter single fiber reflectance spectroscopy: in silico sensitivity analysis

Multiple diameter single fiber reflectance (MDSFR) measurements of turbid media can be used to determine the reduced scattering coefficient (μ'(s)) and a parameter that characterizes the phase function (γ). The MDSFR method utilizes a semi-empirical model that expresses the collected single fiber reflectance intensity as a function of fiber diameter (d(fiber)), μ'(s), and γ. This study investigated the sensitivity of the MDSFR estimates of μ'(s) and γ to the choice of fiber diameters and spectral information incorporated into the fitting procedure. The fit algorithm was tested using Monte Carlo simulations of single fiber reflectance intensities that investigated biologically relevant ranges of scattering properties (μ'(s) ∈ [0.4 - 4]mm(-1)) and phase functions (γ ∈ [1.4 - 1.9]) and for multiple fiber diameters (d(fiber) ∈ [0.2 - 1.5] mm). MDSFR analysis yielded accurate estimates of μ'(s) and γ over the wide range of scattering combinations; parameter accuracy was shown to be sensitive to the range of fiber diameters included in the analysis, but not to the number of intermediate fibers. Moreover, accurate parameter estimates were obtained without a priori knowledge about the spectral shape of γ. Observations were used to develop heuristic guidelines for the design of clinically applicable MDSFR probes.

Models of light propagation in human tissue applied to cancer diagnostics

Optical methods such as reflectance and fluorescence spectroscopy are being investigated for their potential to aid cancer detection in a quantitative, minimally invasive manner. Mathematical models of reflectance and fluorescence provide an important link between measured optical data and biomedically-relevant tissue parameters that can be extracted from these data to characterize the presence or absence of disease. The most commonly-used mathematical models in biomedical optics are the diffusion approximation (DA) to the radiative transfer equation, Monte Carlo (MC) computational models of light transport, and semi-empirical models. This paper presents a review of the applications of these models to reflectance and endogenous fluorescence sensing for cancer diagnostics in human tissues. Specific examples are given for cervical, breast, and pancreatic tissues. A comparison of the DA and MC methods in two biologically-relevant regimes of optical parameter space will also be discussed.

Dynamic tissue analysis using time- and wavelength-resolved fluorescence spectroscopy for atherosclerosis diagnosis

Simultaneous time- and wavelength-resolved fluorescence spectroscopy (STWRFS) was developed and tested for the dynamic characterization of atherosclerotic tissue ex vivo and arterial vessels in vivo. Autofluorescence, induced by a 337 nm, 700 ps pulsed laser, was split to three wavelength sub-bands using dichroic filters, with each sub-band coupled into a different length of optical fiber for temporal separation. STWRFS allows for fast recording/analysis (few microseconds) of time-resolved fluorescence emission in these sub-bands and rapid scanning. Distinct compositions of excised human atherosclerotic aorta were clearly discriminated over scanning lengths of several centimeters based on fluorescence lifetime and the intensity ratio between 390 and 452 nm. Operation of STWRFS blood flow was further validated in pig femoral arteries in vivo using a single-fiber probe integrated with an ultrasound imaging catheter. Current results demonstrate the potential of STWRFS as a tool for real-time optical characterization of arterial tissue composition and for atherosclerosis research and diagnosis.

Probe pressure effects on human skin diffuse reflectance and fluorescence spectroscopy measurements

Diffuse reflectance and fluorescence spectroscopy are popular research techniques for noninvasive disease diagnostics. Most systems include an optical fiber probe that transmits and collects optical spectra in contact with the suspected lesion. The purpose of this study is to investigate probe pressure effects on human skin spectroscopic measurements. We conduct an in-vivo experiment on human skin tissue to study the short-term (<2 s) and long-term (>30 s) effects of probe pressure on diffuse reflectance and fluorescence measurements. Short-term light probe pressure (P0<9 mN∕mm2) effects are within 0 ± 10% on all physiological properties extracted from diffuse reflectance and fluorescence measurements, and less than 0±5% for diagnostically significant physiological properties. Absorption decreases with site-specific variations due to blood being compressed out of the sampled volume. Reduced scattering coefficient variation is site specific. Intrinsic fluorescence shows a large standard error, although no specific pressure-related trend is observed. Differences in tissue structure and morphology contribute to site-specific probe pressure effects. Therefore, the effects of pressure can be minimized when the pressure is small and applied for a short amount of time; however, long-term and large pressures induce significant distortions in measured spectra.

Photon-tissue interaction model enables quantitative optical analysis of human pancreatic tissues

A photon-tissue interaction (PTI) model was developed and employed to analyze 96 pairs of reflectance and fluorescence spectra from freshly excised human pancreatic tissues. For each pair of spectra, the PTI model extracted a cellular nuclear size parameter from the measured reflectance, and the relative contributions of extracellular and intracellular fluorophores to the intrinsic fluorescence. The results suggest that reflectance and fluorescence spectroscopies have the potential to quantitatively distinguish among pancreatic tissue types, including normal pancreatic tissue, pancreatitis, and pancreatic adenocarcinoma.

Instrumentation to rapidly acquire fluorescence wavelength-time matrices of biological tissues

A fiber-optic system was developed to rapidly acquire tissue fluorescence wavelength-time matrices (WTMs) with high signal-to-noise ratio (SNR). The essential system components (473 nm microchip laser operating at 3 kHz repetition frequency, fiber-probe assemblies, emission monochromator, photomultiplier tube, and digitizer) were assembled into a compact and clinically-compatible unit. Data were acquired from fluorescence standards and tissue-simulating phantoms to test system performance. Fluorescence decay waveforms with SNR > 100 at the decay curve peak were obtained in less than 30 ms. With optimized data transfer and monochromator stepping functions, it should be feasible to acquire a full WTM at 5 nm emission wavelength intervals over a 200 nm range in under 2 seconds.

Spectral areas and ratios classifier algorithm for pancreatic tissue classification using optical spectroscopy

Pancreatic adenocarcinoma is one of the leading causes of cancer death, in part because of the inability of current diagnostic methods to reliably detect early-stage disease. We present the first assessment of the diagnostic accuracy of algorithms developed for pancreatic tissue classification using data from fiber optic probe-based bimodal optical spectroscopy, a real-time approach that would be compatible with minimally invasive diagnostic procedures for early cancer detection in the pancreas. A total of 96 fluorescence and 96 reflectance spectra are considered from 50 freshly excised tissue sites-including human pancreatic adenocarcinoma, chronic pancreatitis (inflammation), and normal tissues-on nine patients. Classification algorithms using linear discriminant analysis are developed to distinguish among tissues, and leave-one-out cross-validation is employed to assess the classifiers' performance. The spectral areas and ratios classifier (SpARC) algorithm employs a combination of reflectance and fluorescence data and has the best performance, with sensitivity, specificity, negative predictive value, and positive predictive value for correctly identifying adenocarcinoma being 85, 89, 92, and 80%, respectively.