Quantifying the effects of anesthesia on intracellular oxygen via low-cost portable microscopy using dual-emissive nanoparticles.

Intracellular oxygenation is an important parameter for numerous biological studies. While there are a variety of methods available for acquiring in vivo measurements of oxygenation in animal models, most are dependent on indirect oxygen measurements, restraints, or anesthetization. A portable microscope system using a Raspberry Pi computer and Pi Camera was developed for attaching to murine dorsal window chambers. Dual-emissive boron nanoparticles were used as an oxygen-sensing probe while mice were imaged in awake and anesthetized states. The portable microscope system avoids altered in vivo measurements due to anesthesia or restraints while enabling increased continual acquisition durations.

Development and Preliminary Evaluation of a Murine Model of Chronic Radiation-Induced Proctitis.

PURPOSE: Radiotherapy (RT) is commonly used to treat most pelvic malignancies. While treatment is often effective, curative radiation doses to the rectum can result in chronic radiation-induced proctitis, which is characterized by diarrhea, tenesmus, and/or rectal bleeding, recently termed pelvic radiation disease. An animal model of chronic radiation-induced proctitis would be useful to test both preventative and therapeutic strategies to limit this morbidity but has been elusive because of the high rodent mortality associated with acute bowel RT injury. The objective of this research was to develop a novel mouse model of chronic radiation-induced proctitis using advanced technology. METHODS AND MATERIALS: Using an X-RAD 225-Cx (Precision X-Ray) small animal irradiator, multiple plan configurations were evaluated for planning treatment volume and organ-at-risk avoidance to deliver a 15 Gy 3D conformal treatment plan. The final plan was verified by high resolution 3D dosimetry (PRESAGE/optical-CT), and delivered using a single arc. Mice were monitored for mortality for 250 days, followed by histopathological correlates including mucicarmine, Masson's trichrome, and fecal pellet length. RESULTS: Six beam arrangements were considered: single and parallel-opposed fields with whole-pelvis coverage, and collimated fields in parallel-opposed, 3-field, 4-field, and arc geometries. A collimated arc plan offered superior planning treatment volume coverage and organ-at-risk avoidance compared to whole-pelvis irradiation. Treatment verification with PRESAGE 3D dosimetry (Heuris Inc) showed >99% of voxels passing gamma analysis with 2%/2 mm criteria. Our treatment resulted in no acute mortality and 40% mortality at 250 days. Histopathological analysis showed increased mucous production and fibrosis of the irradiated colon, but no change in fecal pellet length. CONCLUSIONS: Our model was able to target successfully lower colon and rectum with lower mortality than other published models. This permitted measurement of late effects that recapitulate some features of rectal damage in humans.

Miniature spectral imaging device for wide-field quantitative functional imaging of the morphological landscape of breast tumor margins.

We have developed a portable, breast margin assessment probe leveraging diffuse optical spectroscopy to quantify the morphological landscape of breast tumor margins during breast conserving surgery. The approach presented here leverages a custom-made 16-channel annular photodiode imaging array (arranged in a 4 × 4 grid), a raster-scanning imaging platform with precision pressure control, and compressive sensing with an optimized set of eight wavelengths in the visible spectral range. A scalable Monte-Carlo-based inverse model is used to generate optical property [ ? s ? ( ? ) and ? a ( ? ) ] measures for each of the 16 simultaneously captured diffuse reflectance spectra. Subpixel sampling (0.75 mm) is achieved through incremental x , y raster scanning of the imaging probe, providing detailed optical parameter maps of breast margins over a 2 × 2 ?? cm 2 area in ? 9 ?? min . The morphological landscape of a tumor margin is characterized using optical surrogates for the fat to fibroglandular content ratio, which has demonstrated diagnostic utility in delineating tissue subtypes in the breast.

Wavelength optimization for quantitative spectral imaging of breast tumor margins.

A wavelength selection method that combines an inverse Monte Carlo model of reflectance and a genetic algorithm for global optimization was developed for the application of spectral imaging of breast tumor margins. The selection of wavelengths impacts system design in cost, size, and accuracy of tissue quantitation. The minimum number of wavelengths required for the accurate quantitation of tissue optical properties is 8, with diminishing gains for additional wavelengths. The resulting wavelength choices for the specific probe geometry used for the breast tumor margin spectral imaging application were tested in an independent pathology-confirmed ex vivo breast tissue data set and in tissue-mimicking phantoms. In breast tissue, the optical endpoints (hemoglobin, ß-carotene, and scattering) that provide the contrast between normal and malignant tissue specimens are extracted with the optimized 8-wavelength set with <9% error compared to the full spectrum (450-600 nm). A multi-absorber liquid phantom study was also performed to show the improved extraction accuracy with optimization and without optimization. This technique for selecting wavelengths can be used for designing spectral imaging systems for other clinical applications.

A diffuse reflectance spectral imaging system for tumor margin assessment using custom annular photodiode arrays.

Diffuse reflectance spectroscopy (DRS) is a well-established method to quantitatively distinguish between benign and cancerous tissue for tumor margin assessment. Current multipixel DRS margin assessment tools are bulky fiber-based probes that have limited scalability. Reported herein is a new approach to multipixel DRS probe design, which utilizes direct detection of the DRS signal by using optimized custom photodetectors in direct contact with the tissue. This first fiberless DRS imaging system for tumor margin assessment consists of a 4 × 4 array of annular silicon photodetectors and a constrained free-space light delivery tube optimized to deliver light across a 256 mm(2) imaging area. This system has 4.5 mm spatial resolution. The signal-to-noise ratio measured for normal and malignant breast tissue-mimicking phantoms was 35 dB to 45 dB for λ = 470 nm to 600 nm.

Experimental validation of an inverse fluorescence Monte Carlo model to extract concentrations of metabolically relevant fluorophores from turbid phantoms and a murine tumor model.

An inverse Monte Carlo based model has been developed to extract intrinsic fluorescence from turbid media. The goal of this work was to experimentally validate the model to extract intrinsic fluorescence of three biologically meaningful fluorophores related to metabolism from turbid media containing absorbers and scatterers. Experimental studies were first carried out on tissue-mimicking phantoms that contained individual fluorophores and their combinations, across multiple absorption, scattering, and fluorophore concentrations. The model was then tested in a murine tumor model to determine both the kinetics of fluorophore uptake as well as overall tissue fluorophore concentration through extraction of the intrinsic fluorescence of an exogenous contrast agent that reports on glucose uptake. Results show the model can be used to recover the true intrinsic fluorescence spectrum with high accuracy (R(2)=0.988) as well as accurately compute fluorophore concentration in both single and multiple fluorophores phantoms when appropriate calibration standards are available. In the murine tumor, the model-corrected intrinsic fluorescence could be used to differentiate drug dose injections between different groups. A strong linear correlation was observed between the extracted intrinsic fluorescence intensity and injected drug dose, compared with the distorted turbid tissue fluorescence.

Non-invasive monitoring of intra-tumor drug concentration and therapeutic response using optical spectroscopy.

Optical spectroscopy was used to monitor changes in tumor physiology with therapy, and its influence on drug delivery and treatment efficacy for hyperthermia treatment combined with free doxorubicin or a low-temperature sensitive liposomal formulation. Monte Carlo-based modeling techniques were used to characterize the intrinsic absorption, scattering, and fluorescence properties of tissue. Fluorescence assessment of drug concentration was validated against HPLC and found to be significantly linearly correlated (r=0.88). Cluster analysis on the physiologic data obtained by optical spectroscopy revealed two physiologic phenotypes prior to treatment. One of these was relatively hypoxic, with relatively low total hemoglobin content. This hypoxic group was found to have a significantly shorter time to reach 3 times pre-treatment volume, indicating a more treatment resistant phenotype (p=0.003). Influence of tumor physiology was assessed in more detail for the liposomal doxorubicin+hyperthermia group, which demonstrated a highly significant correlation between pre-treatment hemoglobin saturation and tumor growth delay, and also between post-hyperthermia total hemoglobin content and tumor drug delivery. Finally, it was found that the doxorubicin concentration, measured in vivo using fluorescence techniques significantly predicted for chemoresponse (hazard ratio: 0.34, p=0.0007). The ability to characterize drug delivery and tumor physiology in vivo makes this a potentially useful tool for evaluating the efficacy of targeted delivery systems in preclinical studies, and may be translatable for monitoring and predicting individual treatment responses in the clinic.

A robust Monte Carlo model for the extraction of biological absorption and scattering in vivo.

We have a toolbox to quantify tissue optical properties that is composed of specialized fiberoptic probes for UV-visible diffuse reflectance spectroscopy and a fast, scalable inverse Monte Carlo (MC) model. In this paper, we assess the robustness of the toolbox for quantifying physiologically relevant parameters from turbid tissue-like media. In particular, we consider the effects of using different instruments, fiberoptic probes, and instrument-specific settings for a wide range of optical properties. Additionally, we test the quantitative accuracy of the inverse MC model for extracting the biologically relevant parameters of hemoglobin saturation and total hemoglobin concentration. We also test the effect of double-absorber phantoms (hemoglobin and crocin to model the absorption of hemoglobin and beta carotene, respectively, in the breast) for a range of absorption and scattering properties. We include an assessment on which reference phantom serves as the best calibration standard to enable accurate extraction of the absorption and scattering properties of the target sample. We found the best reference-target phantom combinations to be ones with similar scattering levels. The results from these phantom studies provide a set of guidelines for extracting optical parameters from clinical studies.

A strategy for quantitative spectral imaging of tissue absorption and scattering using light emitting diodes and photodiodes.

A diffuse reflectance spectroscopy system was modified as a step towards miniaturization and spectral imaging of tissue absorption and scattering. The modified system uses a tunable source and an optical fiber for illumination and a photodiode in contact with tissue for detection. Compared to the previous system, it is smaller, less costly, and has comparable performance in extracting optical properties in tissue phantoms. Wavelength reduction simulations show the feasibility of replacing the source with LEDs to further decrease system size and cost. Simulated crosstalk analysis indicates that this evolving system can be multiplexed for spectral imaging in the future.

Diagnosis of breast cancer using fluorescence and diffuse reflectance spectroscopy: a Monte-Carlo-model-based approach.

We explore the use of Monte-Carlo-model-based approaches for the analysis of fluorescence and diffuse reflectance spectra measured ex vivo from breast tissues. These models are used to extract the absorption, scattering, and fluorescence properties of malignant and nonmalignant tissues and to diagnose breast cancer based on these intrinsic tissue properties. Absorption and scattering properties, including beta-carotene concentration, total hemoglobin concentration, hemoglobin saturation, and the mean reduced scattering coefficient are derived from diffuse reflectance spectra using a previously developed Monte Carlo model of diffuse reflectance. A Monte Carlo model of fluorescence described in an earlier manuscript was employed to retrieve the intrinsic fluorescence spectra. The intrinsic fluorescence spectra were decomposed into several contributing components, which we attribute to endogenous fluorophores that may present in breast tissues including collagen, NADH, and retinol/vitamin A. The model-based approaches removes any dependency on the instrument and probe geometry. The relative fluorescence contributions of individual fluorescing components, as well as beta-carotene concentration, hemoglobin saturation, and the mean reduced scattering coefficient display statistically significant differences between malignant and adipose breast tissues. The hemoglobin saturation and the reduced scattering coefficient display statistically significant differences between malignant and fibrous/benign breast tissues. A linear support vector machine classification using (1) fluorescence properties alone, (2) absorption and scattering properties alone, and (3) the combination of all tissue properties achieves comparable classification accuracies of 81 to 84% in sensitivity and 75 to 89% in specificity for discriminating malignant from nonmalignant breast tissues, suggesting each set of tissue properties are diagnostically useful for the discrimination of breast malignancy.

Monte-Carlo-based model for the extraction of intrinsic fluorescence from turbid media.

A Monte-Carlo-based model of fluorescence is developed that is capable of extracting the intrinsic fluorescence properties of tissue, which are independent of the absorption and scattering properties of tissue. This model is flexible in its applicability to different illumination-collection geometries and is also valid for a wide range of optical properties, representative of tissue in the UV-visible spectrum. This is potentially useful in a variety of biomedical applications, including cancer diagnostics and monitoring the physiological response to therapy. The model is validated using phantoms composed of hemoglobin (absorber), polystyrene spheres (scatterer), and furan-2 (fluorophore). It is found that this model is able to retrieve the intrinsic fluorescence spectra of the tissue phantoms and recover the intrinsic fluorescence intensity of furan within the phantoms to within a mean error of less than 10%.

Cost-effective diffuse reflectance spectroscopy device for quantifying tissue absorption and scattering in vivo.

A hybrid optical device that uses a multimode fiber coupled to a tunable light source for illumination and a 2.4-mm photodiode for detection in contact with the tissue surface is developed as a first step toward our goal of developing a cost-effective, miniature spectral imaging device to map tissue optical properties in vivo. This device coupled with an inverse Monte Carlo model of reflectance is demonstrated to accurately quantify tissue absorption and scattering in tissue-like turbid synthetic phantoms with a wide range of optical properties. The overall errors for quantifying the absorption and scattering coefficients are 6.0+/-5.6 and 6.1+/-4.7%, respectively. Compared with fiber-based detection, having the detector right at the tissue surface can significantly improve light collection efficiency, thus reducing the requirement for sophisticated detectors with high sensitivity, and this design can be easily expanded into a quantitative spectral imaging system for mapping tissue optical properties in vivo.

Effect of optical clearing agents on the in vivo optical properties of squamous epithelial tissue.

BACKGROUND AND OBJECTIVES: Optical clearing agents (OCAs) have previously been shown to increase depth penetration within turbid tissue ex vivo. This paper quantifies tissue optical properties of the hamster cheek pouch model in order to provide a means to assess the effect of OCAs quantitatively in vivo. STUDY DESIGN/MATERIALS AND METHODS: Diffuse reflectance spectra were obtained from both cheeks of 12 hamsters before and after immersion in dimethyl sulfoxide (DMSO), glycerol or a phosphate buffer saline (PBS) control for 20 minutes. A Monte Carlo model was then utilized to derive the wavelength dependent reduced scattering and absorption coefficients. RESULTS: DMSO caused a statistically significant decrease in the absorption and reduced scattering coefficients derived by the model. Glycerol caused a statistically significant increase in the wavelength dependent absorption coefficient, but no statistically significant changes in the reduced scattering coefficient. CONCLUSIONS: DMSO and glycerol act upon tissues differently as reflected by the tissue optical properties, implying that not all OCAs are equally effective in optically clearing tissues.

Diagnosis of breast cancer using diffuse reflectance spectroscopy: Comparison of a Monte Carlo versus partial least squares analysis based feature extraction technique.

BACKGROUND AND OBJECTIVE: We explored the use of diffuse reflectance spectroscopy in the ultraviolet-visible (UV-VIS) spectrum for the diagnosis of breast cancer. A physical model (Monte Carlo inverse model) and an empirical model (partial least squares analysis) based approach, were compared for extracting diagnostic features from the diffuse reflectance spectra. STUDY DESIGN/METHODS: The physical model and the empirical model were employed to extract features from diffuse reflectance spectra measured from freshly excised breast tissues. A subset of extracted features obtained using each method showed statistically significant differences between malignant and non-malignant breast tissues. These features were separately input to a support vector machine (SVM) algorithm to classify each tissue sample as malignant or non-malignant. RESULTS AND CONCLUSIONS: The features extracted from the Monte Carlo based analysis were hemoglobin saturation, total hemoglobin concentration, beta-carotene concentration and the mean (wavelength averaged) reduced scattering coefficient. Beta-carotene concentration was positively correlated and the mean reduced scattering coefficient was negatively correlated with percent adipose tissue content in normal breast tissues. In addition, there was a statistically significant decrease in the beta-carotene concentration and hemoglobin saturation, and a statistically significant increase in the mean reduced scattering coefficient in malignant tissues compared to non-malignant tissues. The features extracted from the partial least squares analysis were a set of principal components. A subset of principal components showed that the diffuse reflectance spectra of malignant breast tissues displayed an increased intensity over wavelength range of 440-510 nm and a decreased intensity over wavelength range of 510-600 nm, relative to that of non-malignant breast tissues. The diagnostic performance of the classification algorithms based on both feature extraction techniques yielded similar sensitivities and specificities of approximately 80% for discriminating between malignant and non-malignant breast tissues. While both methods yielded similar classification accuracies, the model based approach provided insight into the physiological and structural features that discriminate between malignant and non-malignant breast tissues.

Monte Carlo-based inverse model for calculating tissue optical properties. Part I: Theory and validation on synthetic phantoms.

A flexible and fast Monte Carlo-based model of diffuse reflectance has been developed for the extraction of the absorption and scattering properties of turbid media, such as human tissues. This method is valid for a wide range of optical properties and is easily adaptable to existing probe geometries, provided a single phantom calibration measurement is made. A condensed Monte Carlo method was used to speed up the forward simulations. This model was validated by use of two sets of liquid-tissue phantoms containing Nigrosin or hemoglobin as absorbers and polystyrene spheres as scatterers. The phantoms had a wide range of absorption (0-20 cm(-1)) and reduced scattering coefficients (7-33 cm(-1)). Mie theory and a spectrophotometer were used to determine the absorption and reduced scattering coefficients of the phantoms. The diffuse reflectance spectra of the phantoms were measured over a wavelength range of 350-850 nm. It was found that optical properties could be extracted from the experimentally measured diffuse reflectance spectra with an average error of 3% or less for phantoms containing hemoglobin and 12% or less for phantoms containing Nigrosin.

Monte Carlo-based inverse model for calculating tissue optical properties. Part II: Application to breast cancer diagnosis.

The Monte Carlo-based inverse model of diffuse reflectance described in part I of this pair of companion papers was applied to the diffuse reflectance spectra of a set of 17 malignant and 24 normal-benign ex vivo human breast tissue samples. This model allows extraction of physically meaningful tissue parameters, which include the concentration of absorbers and the size and density of scatterers present in tissue. It was assumed that intrinsic absorption could be attributed to oxygenated and deoxygenated hemoglobin and beta-carotene, that scattering could be modeled by spheres of a uniform size distribution, and that the refractive indices of the spheres and the surrounding medium are known. The tissue diffuse reflectance spectra were evaluated over a wavelength range of 400-600 nm. The extracted parameters that showed the statistically most significant differences between malignant and nonmalignant breast tissues were hemoglobin saturation and the mean reduced scattering coefficient. Malignant tissues showed decreased hemoglobin saturation and an increased mean reduced scattering coefficient compared with nonmalignant tissues. A support vector machine classification algorithm was then used to classify a sample as malignant or nonmalignant based on these two extracted parameters and produced a cross-validated sensitivity and specificity of 82% and 92%, respectively.