This PDF file contains the front matter associated with SPIE Proceedings Volume 9333, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and Conference Committee listing.

Laser Speckle Contrast Imaging (LSCI) is a flexible, non-invasive, label-free technique to measure relative blood flow speeds in-vivo. Near IR illumination allows deep tissue penetration due to low tissue absorption in that wavelength range. However, the low absorption leads to a reduced observed image contrast between tissue and blood vessels. This leads to a challenge in determining and automatically adjusting the best focus location invivo. Traditional autofocus algorithms that are based on either intensity contrast or frequency domain analysis do not work well during flow imaging with the LSCI technique, due to increased speckle and low contrast in the image. Using the LSCI-derived contrast ratio K directly, over a vessel of interest, provides a better metric for determining the location of imaging system focal plane, but the method is not robust as it is possesses low signal-to-noise ratio (SNR) within a single frame. In this work we use a different metric, kurtosis of the flow profile cross-section, to estimate the degree of misfocus (axial deviation of imaging system focal plane from the imaged blood vessel) and provide a feedback mechanism for robust autofocusing during blood flow imaging in a rats brain. We demonstrate via flow imaging simulations, imaging of flow in microfluidic capillaries, and in-vivo imaging of blood flow in brains of anaesthetized rats that this metric allows for the determination of the location of best focus and assessing the degree of misfocus.

In epilepsy it has been challenging to detect early changes in brain activity that occurs prior to seizure onset and to map their origin and evolution for possible intervention. Besides, preclinical seizure experiments need to be conducted in awake animals with images reconstructed and displayed in real-time. We demonstrate using a rat model of generalized epilepsy that diffuse optical tomography (DOT) provides a unique functional neuroimaging modality for noninvasively and continuously tracking brain activities with high spatiotemporal resolution. We developed methods to conduct seizure experiments in fully awake rats using a subject-specific helmet and a restraining mechanism. For the first time, we detected early hemodynamic responses with heterogeneous patterns several minutes preceding the electroencephalographic seizure onset, supporting the presence of a “pre-seizure” state both in anesthetized and awake rats. Using a novel time-series analysis of scattering images, we show that the analysis of scattered diffuse light is a sensitive and reliable modality for detecting changes in neural activity associated with generalized seizure. We found widespread hemodynamic changes evolving from local regions of the bilateral cortex and thalamus to the entire brain, indicating that the onset of generalized seizures may originate locally rather than diffusely. Together, these findings suggest DOT represents a powerful tool for mapping early seizure onset and propagation pathways.

Diffuse correlation spectroscopy (DCS) is a non-invasive optical technique capable of monitoring tissue perfusion changes, particularly in the brain. The normalized temporal intensity autocorrelation function generated by DCS is typically characterized by assuming that the movement of erythrocytes can be modeled as a Brownian diffusion-like process instead of the expected random flow model. Carp et al. [Biomedical Optics Express, 2011] proposed a hybrid model, referred to as the hydrodynamic diffusion model, to capture both the random ballistic and diffusive nature of erythrocyte motion. The purpose of this study was to compare how well the Brownian diffusion and the hydrodynamic diffusion models characterized DCS data acquired directly on the brain, avoiding the confounding effects of scalp and skull. Data were acquired from seven pigs during normocapnia (39.9 ± 0.7 mmHg) and hypocapnia (22.1 ± 1.6 mmHg) with the DCS fibers placed 7 mm apart, directly on the cerebral cortex. The hydrodynamic diffusion model was found to provide a consistently better fit to the autocorrelation functions compared to the Brownian diffusion model and was less sensitive to the chosen start and end time points used in the fitting. However, the decrease in cerebral blood flow from normocapnia to hypocapnia determined was similar for the two models (-42.6 ± 8.6 % for the Brownian model and -42.2 ± 10.2 % for the hydrodynamic model), suggesting that the latter is reasonable for monitoring flow changes.

The choice of scattering phase function is critically important in the modeling of photon propagation in turbid media, particularly when the scattering path within the material is on the order of several mean free path lengths. For tissue applications, the single parameter Henyey-Greenstein (HG) phase function is known to underestimate the contribution of backscattering, while phase functions based on Mie theory can be more complex than necessary due to the multitude of parameter inputs. In this work, the two term Gegenbauer phase function is highlighted as an effective compromise between HG and Mie, as demonstrated when fitting the various phase function to measured data from phantom materials. Further comparison against the Modified Henyey-Greenstein (MHG) phase function, another two term function, demonstrates that the Gegenbauer function provides better control of the higher order phase function moments, and hence allows for a wider range of values for the similarity parameter, γ. Wavelength dependence of the Gegenbauer parameters is also investigated using a range of theoretical particle distributions. Finally, extraction of the scattering properties of solid turbid samples from angularly resolved transmission measurements is performed using an iterative Monte Carlo optimization technique. Fitting results using Gegenbauer, HG, MHG, and Mie phase functions are compared.

Cancerous and precancerous growths often exhibit changes in scattering, and therefore depolarization, as well as collagen breakdown, causing changes in birefringent effects. Simple difference of linear polarization imaging is unable to represent anisotropic effects like birefringence, and Mueller polarimetry is time-consuming and difficult to implement clinically. This work presents a dual-polarization endoscope to collect co- and cross-polarized images for each of two polarization states, and further incorporates narrow band detection to increase vascular contrast, particularly vascular remodeling present in diseased tissue, and provide depth sensitivity. The endoscope was shown to be sensitive to both isotropic and anisotropic materials in phantom and in vivo experiments.

The propagation of light in turbid media is an active area of research with relevance to numerous investigational fields, e.g., biomedical diagnostics and therapeutics. The statistical random-walk nature of photon propagation through turbid media is ideal for computational based modeling and simulation. Ready access to super computing resources provide a means for attaining brute force solutions to stochastic light-matter interactions entailing scattering by facilitating timely propagation of sufficient (>10⁷) photons while tracking characteristic parameters based on the incorporated physics of the problem. One such model that works well for isotropic but fails for anisotropic scatter, which is the case for many biomedical sample scattering problems, is the diffusion approximation. In this report, we address this by utilizing Berry phase (BP) evolution as a means for capturing anisotropic scattering characteristics of samples in the preceding depth where the diffusion approximation fails. We extend the polarization sensitive Monte Carlo method of Ramella-Roman, et al., to include the computationally intensive tracking of photon trajectory in addition to polarization state at every scattering event. To speed-up the computations, which entail the appropriate rotations of reference frames, the code was parallelized using OpenMP. The results presented reveal that BP is strongly correlated to the photon penetration depth, thus potentiating the possibility of polarimetric depth resolved characterization of highly scattering samples, e.g., biological tissues.

Spectroscopic optical coherence tomography (SOCT) is an extension of a standard OCT technique, which allows to obtain depth-resolved, spectroscopic information on the examined sample. It can be used as a source of additional contrast in OCT images e.g. by encoding certain features of the light spectrum into the hue of the image pixels. However, SOCT require computation of time-frequency distributions of each OCT A-scan, what is a very time consuming procedure. This is particularly important in a real-time OCT imaging. Here, we present a new approach to SOCT signal processing that allows for nearly tenfold reduction of a required computation time. The presented approach is based on a recursive analysis of OCT scan in time-domain without necessity of computing neither short-time Fourier transform or any other time-frequency distribution.

We present new near-infrared (NIR) imaging technique for analyzing the moisturizer drop dynamics on the human skin surface. From the measurement experiment for the vertical water content in the skin tissue and light transport simulation, it was clarified that imaging the skin tissue using 1950 nm band effectively visualizes the water distribution. We demonstrate the NIR imaging experiment using originally developed NIR microscopic water imaging system. The relationship between the moisturizer drop dynamics and the water condition of the skin tissue is also discussed.

We take advantage of human hair specific geometry to visualize sparse submicron cuticle peelings with highly oblique tip-side illumination. We show that the statistics of these features can directly estimate hair quality in much lower magnifications (down to 20^x) with less powerful objectives when the features themselves are below the system resolution. Our technique has strong potential for lower cost, portable, and autonomous hair diagnostic apparatuses.

Diffuse reflectance spectroscopy is a popular approach for non-invasive assessment of optical properties in turbid media. The acquired spectra are analyzed by various light propagation models or by purely empirical methods. In this study, we quantitatively asses the experimental data and Monte Carlo lookup table-based inverse models by extracting the optical properties from the diffuse reflectance spectra of two carefully prepared turbid phantom sets with exactly defined optical properties. The first turbid phantom set was used for the creation of the experimental data-based lookup table model and calibration of the Monte Carlo lookup table-based inverse model. The second phantom set was used for the evaluation and comparative assessment of the two lookup table-based inverse models. In addition, we investigate the possible sources of errors introduced by the inverse models and show that the lookup table-based models disregard important information regarding the medium scattering phase function.

Laser speckle analysis is a very powerful method with various existing applications, including biomedical diagnostics. The majority of the speckle applications are based on analysis of dependence of scattered light intensity distribution from sizes of the scattereres. We propose a numerical model of speckle formation in reflected light in one-dimension which shows that properties of the scattered light are strongly dependent on the form of the scatterers. In particular, the dependence of number of speckles from the size of the scatterers was investigated for the light reflected from the surface with varying roughness; the single roughness on the surface was assumed to have the form of one-dimensional ‘pyramid’ with the sides having either linear or parabolic descent from the top of the ‘pyramid’ to the bottom. It was found that for the linear roughness, number of speckles decreased with increase of the roughness size, whereas for the parabolic roughness the number of speckles increased. Results of numerical simulation were compared with experiment investigations of roughness samples (0.5-2.5 μm) made of glass and copper. Due to different production processes, the glass samples are likely to have the parabolic roughness and copper samples are likely to have the linear roughness. Experiments show that the dependences of number of speckles also have different slopes, the same as in numerical simulation. These findings can lead to new analytical methods capable of determining not only the size distribution of roughness (or scatterers) but also the shape.

Accurate modeling and efficient calculation of photon migration in biological tissues is requested for determination of the optical properties of living tissues by in vivo experiments. This study develops a calculation scheme of photon migration for determination of the optical properties of the rat cerebral cortex (ca 0.2 cm thick) based on the three-dimensional time-dependent radiative transport equation assuming a homogeneous object. It is shown that the time-resolved profiles calculated by the developed scheme agree with the profiles measured by in vivo experiments using near infrared light. Also, an efficient calculation method is tested using the delta-Eddington approximation of the scattering phase function.

Simulations of the optical coherence tomography (OCT) systems using the Monte Carlo method is a widely explored research area. However, there are several difficulties that need to be overcome in order to properly model the OCT imaging with the Monte Carlo algorithm. First of all, the temporal and the spatial coherence of the scattered light need to be considered, since OCT is based on the interference phenomenon. For the same reason, the polarization state of the scattered light need to be calculated. Moreover, the OCT systems use light beams that can be described by the Gaussian beam model. However, such beams cannot be directly simulated using the standard Monte Carlo algorithm. Different research groups have developed simulators dealing with some of these problems but the Monte Carlo simulator which considers all of them has not been published yet. Here we present the Monte Carlo program allowing to simulate OCT images of heterogeneous light scattering structures. The presented program considers all of the listed problems and allows to model complex sample geometries with layer boundaries described by a set of polygons.

We present here the numeric study of the propagation of polarized coherent complex light in turbid media with Electric field Monte Carlo (EMC) approach. EMC is one unique Monte Carlo method suitable for simulating coherent phenomenon of multiple scattering light. EMC has been extended to explicitly incorporate the complex incident wave front of coherent complex light and used to investigate the interaction of coherent complex light with highly scattering turbid media such as biological tissue. We will report the dependence of the decay of the beam intensity and the loss of the polarization over the penetration depth on the orbital angular momentum of the complex light and the scattering properties of a turbid medium. The potential application of complex light in imaging turbid media will be discussed.