Polarized light is commonly used to detect optical anisotropies, such as birefringence, in tissues. This optical anisotropy is often attributed to underlying structural anisotropy in tissue, which may originate from regularly aligned collagen fibers. In these cases, the optical anisotropy, such as birefringence, is interpreted as a relative measure of the structural anisotropy of the collagen fibers. However, the relative amplitude of optical anisotropy depends on factors other than fiber orientation, and few models allow quantitative interpretation of absolute measures of true fiber orientation distribution from the optical signal. Our model uses the Mie solution to scattering of linearly polarized light from infinite cylindrical scatterers. The model is expanded to include populations of scatterers with physiologically relevant size and orientation distributions. We investigated the influences of fiber diameter, orientation distribution, and wavelength on the back-scattering signal with our computational model, and used these results to extract structural information from experimental fiber phantoms and bovine tendon. Our results demonstrated that by fitting our model to the experimental data using limited assumptions, we could extract fiber orientation distributions and diameters that were comparable to those found in scanning electron microscope images of the same fiber sample. We found a higher alignment of fibers in the bovine tendon sample, and the extracted fiber diameter was within the expected physiological range. Our model showed that the amplitude of optical anisotropy can vary widely due to factors other than the orientation distribution of fiber structures, including index of refraction, and therefore should not be taken as a sole indicator of structural anisotropy. This work highlights that the accuracy of model assumptions plays a crucial role in extracting quantitative structural information from optical anisotropy.

Mohs surgery is the current gold standard to treat large, aggressive or high-risk non-melanoma skin cancer (NMSC) cases. While Mohs surgery is an effective treatment, the procedure is time-consuming and expensive for physicians as well as burdensome for patients as they wait for frozen section histology. Our group has recently demonstrated high diagnostic accuracy using a noninvasive “spectral biopsy” (combination of diffuse reflectance (DRS), fluorescence (FS) and Raman spectroscopy (RS)) to classify NMSC vs. normal lesion in a screening setting of intact tissue. Here, we examine the sensitivity of spectral biopsy to pathology in excised Mohs sections. The system is designed with three modalities integrated into one fiber probe, which is utilized to measure DRS, FS, and RS of freshly excised skin from patients with various NMSC pathologies including basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), where each measurement location is correlated to histopathology. The spectral biopsy provides complimentary physiological information including the reduced scattering coefficient, hemoglobin content and oxygen saturation from DRS, NADH and collagen contribution from FS and information regarding multiple proteins and lipids from RS. We then apply logistic regression model to the extracted physiological parameters to classify NMSC vs. normal tissue. The results on the excised tissue are generally consistent with in vivo measurements showing decreased scattering within the tumor and reduced fluorescence. Due to the high sensitivity of RS to lipids, subcutaneous fat often dominates the RS signal. This pilot study demonstrates the potential for a spectral biopsy to classify NMSC vs. normal tissue, indicating the opportunity to guide Mohs excisions.

Skin cancer is the most common form of cancer in the United States and is a recognized public health issue. Diagnosis of skin cancer involves biopsy of the suspicious lesion followed by histopathology. Biopsies, which involve excision of the lesion, are invasive, at times unnecessary, and are costly procedures ( $2.8B/year in the US). An unmet critical need exists to develop a non-invasive and inexpensive screening method that can eliminate the need for unnecessary biopsies. To address this need, our group has reported on the continued development of a noninvasive method that utilizes multimodal spectroscopy towards the goal of a “spectral biopsy” of skin. Our approach combines Raman spectroscopy, fluorescence spectroscopy, and diffuse reflectance spectroscopy to collect comprehensive optical property information from suspicious skin lesions. We previously described an updated spectral biopsy system that allows acquisition of all three forms of spectroscopy through a single fiber optic probe and is composed of off-the-shelf OEM components that are smaller, cheaper, and enable a more clinic-friendly system. We present initial patient data acquired with the spectral biopsy system, the first from an extensive clinical study (n = 250) to characterize its performance in identifying skin cancers (basal cell carcinoma, squamous cell carcinoma, and melanoma). We also present our first attempts at analyzing this initial set of clinical data using statistical-based models, and with models currently being developed to extract biophysical information from the collected spectra, all towards the goal of noninvasive skin cancer diagnosis.

Diffuse reflectance spectroscopy offers a noninvasive, fast, and low-cost alternative to visual screening and biopsy for skin cancer diagnosis. We have previously acquired reflectance spectra from 137 lesions in 76 patients and determined the capability of spectral diagnosis using principal component analysis (PCA). However, it is not well elucidated why spectral analysis enables tissue classification. To provide the physiological basis, we used the Monte Carlo look-up table (MCLUT) model to extract physiological parameters from those clinical data. The MCLUT model results in the following physiological parameters: oxygen saturation, hemoglobin concentration, melanin concentration, vessel radius, and scattering parameters. Physiological parameters show that cancerous skin tissue has lower scattering and larger vessel radii, compared to normal tissue. These results demonstrate the potential of diffuse reflectance spectroscopy for detection of early precancerous changes in tissue. In the future, a diagnostic algorithm that combines these physiological parameters could be enable non-invasive diagnosis of skin cancer.

Skin cancer is the most common form of cancer in the United States and is a recognized public health issue. Diagnosis of skin cancer involves biopsy of the suspicious lesion followed by histopathology. Biopsies, which involve excision of the lesion, are invasive, at times unnecessary, and are costly procedures ( $2.8B/year in the US). An unmet critical need exists to develop a non-invasive and inexpensive screening method that can eliminate the need for unnecessary biopsies. To address this need, our group has reported on the continued development of a multimodal spectroscopy (MMS) system towards the goal of a spectral biopsy of skin. Our approach combines Raman spectroscopy, fluorescence spectroscopy, and diffuse reflectance spectroscopy to collect comprehensive optical property information from suspicious skin lesions. We describe our present efforts to develop an updated MMS system composed of OEM components that will be smaller, less expensive, and more clinic-friendly than the previous system. Key system design choices include the selection of miniature spectrometers, a fiber-coupled broadband light source, a fiber coupled diode laser, and a revised optical probe. Selection of these components results in a  50% reduction in system footprint, resulting in a more clinic-friendly system. We also present preliminary characterization data from the updated MMS system, showing similar performance with our revised optical probe design. Finally, we present in vivo skin measurements taken with the updated MMS system. Future work includes the initiation of a clinical study (n = 250) of the MMS system to characterize its performance in identifying skin cancers.

Raman spectroscopy (RS) is proving to be a valuable tool for real time noninvasive skin cancer detection via optical fiber probe. However, current methods utilizing RS for skin cancer diagnosis rely on statistically based algorithms to provide tissue classification and do not elucidate the underlying biophysical changes of skin tissue. Therefore, we aim to use RS to explore skin biochemical and structural characteristics and then correlate the Raman spectrum of skin tissue with its disease state. We have built a custom confocal micro-Raman spectrometer system with an 830nm laser light. The high resolution capability of the system allows us to measure spectroscopic features from individual tissue components in situ. Raman images were collected from human skin samples from Mohs surgical biopsy, which were then compared with confocal laser scanning, two-photon fluorescence and hematoxylin and eosin-stained images to develop a linear model of skin tissue Raman spectra. In this model, macroscopic tissue spectra obtained from RS fiber probe were fit into a linear combination of individual basis spectra of primary skin constituents. The fit coefficient of the model explains the biophysical changes spanning a range of normal and various disease states. The model allows for determining parameters similar to that a pathologist is familiar reading and will be a significant guidance in developing RS diagnostic decision schemes.

Our group previously introduced Polarized Spatial Frequency Domain Imaging (PSFDI), a wide-field, reflectance imaging technique which we used to empirically map fiber direction in porcine pulmonary heart valve leaflets (PHVL) without optical clearing or physical sectioning of the sample. Presented is an extended analysis of our PSFDI results using an inverse Mueller matrix model of polarized light scattering that allows additional maps of fiber orientation distribution, along with instrumentation permitting increased imaging speed for dynamic PHVL fiber measurements. We imaged electrospun fiber phantoms with PSFDI, and then compared these measurements to SEM data collected for the same phantoms. PHVL was then imaged and compared to results of the same leaflets optically cleared and imaged with small angle light scattering (SALS). The static PHVL images showed distinct regional variance of fiber orientation distribution, matching our SALS results. We used our improved imaging speed to observe bovine tendon subjected to dynamic loading using a biaxial stretching device. Our dynamic imaging experiment showed trackable changes in the fiber microstructure of biological tissue under loading. Our new PSFDI analysis model and instrumentation allows characterization of fiber structure within heart valve tissues (as validated with SALS measurements), along with imaging of dynamic fiber remodeling. The experimental data will be used as inputs to our constitutive models of PHVL tissue to fully characterize these tissues' elastic behavior, and has immediate application in determining the mechanisms of structural and functional failure in PHVLs used as bio-prosthetic implants.

In this paper, we present a novel approach to retrieve attenuation corrected fluorescence (ACF) in the image field. This approach can be applied to improve tumor identification for both diagnosis and treatment purpose. Furthermore, this approach will facilitate the development of fluorescence image-guided surgery.