This PDF file contains the front matter associated with SPIE Proceedings Volume 12363, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Fiber endoscopes capable of making two-photon (2P) autofluorescence measurements, time-resolved fluorescence decay measurements, and collagen second harmonic generation (SHG) measurements have been applied to animal models. Clinical translation of such devices to internal human organs has the potential to overhaul conventional methods of disease diagnosis and monitoring. Previous work by our lab has established the potential to diagnose high-grade cervical precancers using 2P autofluorescence measurements. Other groups have demonstrated that 2P-based fluorescence lifetime imaging microscopy (FLIM) measurements of nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and collagen SHG measurements have the potential to discriminate between cancerous and benign tissues. In this work, we demonstrate the potential to discern high-grade cervical precancerous lesions (HSILs) from benign tissues using fluorescence intensity measurements of NAD(P)H and oxidized flavoproteins, FLIM NAD(P)H measurements, and collagen SHG measurements. Consistent with previous results, benign tissues demonstrated increased depth-dependent heterogeneity in mitochondrial clustering, and increased overall and intrafield heterogeneity of oxido-reductive state relative to HSILs. FLIM phasor analysis demonstrated a relative decrease in NAD(P)H short and long lifetime, and a relative increase in NAD(P)H bound fraction for benign tissues compared to HSILs. Collagen SHG intensity in benign tissues was greater than that of HSIL tissues, along with overall intrafield variations in collagen fiber orientation. This work motivates the functionalization of a clinical 2P fiber endoscope capable of making SHG, autofluorescence intensity and lifetime measurements of metabolic coenzymes in the human cervix.

Compromised social interactions are a clear sign of several pathological conditions such as autism, schizophrenia, and social anxiety disorder. Understanding the neural mechanisms underlying social behavior plays a key role in improving treatments.

In the past, studies of social behavior in rodents have been conducted using different behavioral paradigms but have rarely been combined with the study of brain activity, this is because the available optical imaging techniques require head-fixed conditions.

Miniaturized optical systems have recently been developed to perform calcium imaging in freely moving rodents, however, current devices have a reduced field of view limiting the brain area to be investigated.

The development of a miniaturized wide-field optical system "Miniscope" has enabled the recording of neuronal dynamics in both hemispheres in free-moving mice. The Miniscope made it possible to perform social interaction studies by simultaneously monitoring neuronal activity, opening a new frontier in the field of social neuroscience.

Intracellular activity occurs at a wide range of time scales, requiring a multiscale approach for data acquisition and analysis. Current optical techniques used for imaging intracellular dynamics suffer from limited imaging speeds, low biomolecular specificity, or require fluorescent labels which can perturb cellular environments. Deep-ultraviolet (UV) microscopy enables fast, label-free, and quantitative molecular imaging with subcellular resolution. Previously, multispectral deep-UV microscopy has been demonstrated for robust hematology analysis and prostate tissue characterization. In this work, we present deep-UV microscopy as a tool to capture multiscale intracellular dynamics. We discuss our microscope setup and analytical framework using phasor analysis. We show that deep-UV microscopy can characterize multiscale intracellular dynamics in prostate cancer cells and reveals unique activity in cell lines of varying malignancy.

Functional near-infrared spectroscopy (fNIRS) is a noninvasive optical technique for assessing spatial brain activation by determining the relative changes in the concentrations of oxy- and deoxyhemoglobin (HbO and HbR) in different regions of the brain. The modified Beer-Lambert Law (mBLL) is essential for calculating the relative concentrations of HbO and HbR, and the differential pathlength factor (DPF) is a key component of the mBLL. In this paper, we investigate how error in DPF estimates translates to relative concentration calculations for HbO and HbR. To do this, we generate various amounts of error in DPF values and calculated the error in the resulting concentration data. We then use a two-regression fit to generate a model of concentration error as a function of DPF error. We also compare different reported DPF values to assess their impact on concentration error and to verify our findings with the concentration-error model. We find that age appears to be a greater contributing factor to concentration error than same-subject anatomical differences, and we also find that DPF over- and underestimation in equal amounts will produce unequal amounts of concentration error.

Hyperspectral microscopy recovers both 2D spatial and 1D spectral information, with applications in bioassays, cell biology, and tissue diagnostics. Traditional hyperspectral systems are bulky, expensive, and slow (require scanning). We designed a compact snapshot hyperspectral imager which uses a single acquisition and can achieve high spatial, spectral, and temporal resolution. The imager consists of a diffuser (random phase mask) placed in the Fourier plane followed by an image sensor with a 64-channel spectral filter array. The diffuser’s point spread function (PSF) enables each spatial point from the object to map onto all the spectral filter channels at once and generates a spatially varying caustic pattern, which encodes the position of the point. Hence, we can computationally reconstruct the object’s 2D spatial information and the full spectral information for each pixel by solving a sparsity-constrained inverse problem.

In this work, we redesign the Spectral DiffuserScope to improve the fabrication and calibration methods. The prior architecture required a custom camera without the cover glass and sacrificed field of view (FOV) to enable PSF calibration. In our design, we use an off-the-shelf camera and demonstrate a simple calibration procedure. We show initial reconstruction experimental results and discuss computational modifications to obtain more accurate reconstructions. These improvements will enable others to easily replicate our hyperspectral imager.

With epidemics that have frequently occurred in the 21st century, various diagnostic techniques are being actively researched to replace the public diagnosis tool, DNA amplification technique (Poly-chain reaction, PCR). Among antigen readout techniques, SERS (Surface Enhanced Raman spectroscopy), which can find out the structure of a single molecule level, has received considerable attention as an alternative technique for replacing PCR because this can detect strong signals even with low concentration samples – inducing rapid diagnosis. Despite this advantage, it is still challenging to utilize as a public diagnostic tool due to the inconvenience of continuously replacing samples for measuring a large number of samples. In this study, we developed a SERS-based massive testing system that combined an optical switch and Raman spectroscopy, with simplifying the system to improve portability. In the system, 1xN optical switch, the mechanical displacement of input fiber is moved in a bidirectional way, which makes the input fiber shift into the location corresponding to each output fiber – inducing transmission for the light source and Raman signal. Thus, Raman data of testing samples would be automatically collectible without manual labor like changing specimens for testing another specimen, result ing in rapid diagnostic results for massive samples. We demonstrated the validation of our system by measuring Raman signals for SERS tags.

The linear frequency modulated (LFM) waveforms for medical imaging have been explored previously. Although the chaotic waveforms are used for radar applications, their benefits for medical imaging applications are not adequately analyzed. In this work, we propose using chaos for microwave medical imaging. Firstly, we consider waveforms generated from two chaotic systems: the Lang-Kobayashi and the Lorenz. Through auto-correlation analysis, we show that these waveforms possess good medical imaging properties. Then, we model the received signal from a prototype of the body tissue consisting of multiple layers (media). This received signal incorporates the transmission and reflection coefficients which are a function of the intrinsic impedance of the media. Lastly, the received signal is cross-correlated with the transmitted signal, i.e., the matched filtering operation. The resultant sharp correlations peaks serve as input to the inversion algorithm that estimates the media's intrinsic impedance, which can further be used to assess the healthy/unhealthy nature of the body part.

Osteoarthritis is the most common disease in articular cartilage. Raman spectroscopy is a promising tool for early detection of degenerative changes in cartilage matrix. In this study, surgically resected humeral heads of 14 patients were subjected to pathological analysis using Raman spectroscopy with principal component analysis and hierarchical clustering analysis. In the result, Raman spectral data of each specimen were divided into three major cluster reflecting the alteration in molecular composition of cartilage matrix. We also found histological characteristics in the cluster, suggesting that Raman spectrum is a biomarker to determine the condition of the cartilage tissue.

Studying the concentrations of water and lipids in human tissue can give insights into biological processes and diseases. This study shows that shortwave-infrared (SWIR) light from light-emitting diodes (LEDs) can be used in spatial frequency domain imaging (SFDI) to quantify water and lipid concentrations in tissue. In contrast to near-infrared (NIR) wavelengths, the SWIR wavelength range offers deeper tissue penetration and coincides with strong absorption bands of water and lipids. The system developed in this work uses 970 nm, 1050 nm, and 1200 nm LEDs with a digital micromirror device for DC and AC illumination. An InGaAs camera and optics image the diffusely reflected light. A 10% Intralipid phantom was used to calibrate the system, allowing conversion of demodulated pixel values to diffuse reflectance. Measurement of sample lipid and water concentrations was performed for several different known dilutions of Intralipid. Water content in biological tissue was measured using SWIR-SFDI in ex vivo porcine skin tissue samples and validated by measuring the change in mass due to water during desiccation, showing a mean error of 0.9% in prediction of initial water content. SWIR-SFDI measurements were taken in human subjects before and after light exercise, showing distinct changes in tissue absorption and reduced scattering. These results show the potential of a LED-based SWIR-SFDI system for noninvasive quantification and mapping of important tissue chromophores.

Modern intraoral scanners are handheld devices that can produce point cloud-based representations of the human jaw. These scanners achieve 3-dimensional spatial resolution on the order of tens of micrometers by measuring light reflected from hard and soft intraoral tissue and applying advanced depth estimation techniques. In this work, a series of deep learning-based segmentation and registration methods for 3D intraoral data was developed for longitudinal monitoring of plaque accumulation and gingival inflammation. An intraoral scanner was used to acquire point cloud data from the upper and lower jaws of human subjects after an initial professional cleaning and then after multiple days abstaining from some oral hygiene. Individual teeth and gum regions within longitudinal datasets were identified using a deep learning algorithm for 3D instance segmentation. Next, automated spatial alignment of teeth and gum regions acquired over multi-day studies was achieved using a multiway registration method. The minimum distances between closest-correlated points were then calculated, allowing changes in tissue and plaque volume to be quantified. Differences in these measured quantities were found to correlate with the extent of plaque and inflammation assessed visually by a trained clinician. These methods provided precise measurements of morphological differences in patient tissue over longitudinal studies, allowing quantification of plaque accumulation and gingival inflammation. Integration of deep learning algorithms with commercial intraoral 3D scanning systems may provide a new approach for expanded screening of intraoral diseases.