This PDF file contains the front matter associated with SPIE Proceedings Volume 11951 including the Title Page, Copyright information, and Table of Contents.

Tissue-mimicking optical phantoms are used for a wide variety of purposes, especially the development and routine quality control of bio-optical measurement techniques. We present here the fabrication and evaluation of a multilayer human skin phantom with a pulsating vascular network. The skin-mimicking phantom comprises the three upper skin layers (dermis, epidermis and hypodermis) and the corresponding blood vessels. It is based on polydimethylsiloxane (PDMS) in which absorption and scattering are induced by adding ink as an absorber and titanium dioxide particles as scattering agent. The vessels are fabricated by inserting wires of different diameter at different heights into a mold frame before adding the uncured PDMS and removing them after curing. A circulatory system is constructed using micro-displacement pumps that are connected to the artificial blood vessels. The expansion of the microvasculature in pure PDMS is monitored using a microscope.

Pulse oximetry is a common tool to perform a non-invasive optical estimate (SpO2) of arterial blood oxygen saturation level (SaO2). Although the principle of pulse oximetry has been established for a long time Recent clinical studies reported oximeter over-estimation bias in black patients. Measurement accuracy is an important factor, as over-estimation could impact clinical decision-making. Prior Monte-Carlo (MC) simulation-based studies showed increased melanin could reduce the oximeter signal intensity. These studies didn’t show the impact of pigmentation on calibration equation development in a population cohort. Extending MC simulations to study the influence of bias in calibration model enrollment, along with the corresponding optical estimation errors would offer insight into the basis of important clinical observations. Here, an MC simulation platform was developed to assess how pigmentation distribution in the racial demographics could impact calibration model development. MC simulations of oximeter measurements from <1200 simulated patient finger models were generated using a stochastic sampling-based technique, where patient optical properties (including pigmentation) were statistically assigned to generate a variation of measurements across different population cohorts. MC simulations of oximeter calibration studies representative of prior FDA 510(k) guidelines e.g.- minimum 20% darkly pigmented population) in comparison with alternative enrollment distributions. Performance of oximeter calibration equations was evaluated with unique population distributions of test subjects. Results showed that even if the calibration equations were developed from a representative population cohort, the predicted SpO2 show overestimation in high pigmentation cohorts. This over-estimation minimizes when the calibration is generated from distributions with an increased pigmented subject enrollment. The sensitivity to detect hypoxia in the highly pigmented cohort (sensitivity=0.95) is lower than the low pigmented cohort(sensitivity=0.98) when the representative population distribution was used to develop the calibration equation.

Beyond the optical and analytical performance of the sensor itself, the development of an optical detection tool in response to a pressing research or diagnostic need requires consideration of a host of additional factors. This talk will provide an overview of two photonic sensor systems developed for profiling the human immune response to COVID-19 infection and/or vaccination. One, focused on the design goal of high multiplexing (many targets per sensor), was built on the Arrayed Imaging Reflectometry (AIR) platform. AIR is a free-space optics technique that relies on the creation and target molecule binding-induced disruption of an antireflective coating on the surface of a silicon chip. The second method, focused on low cost and high speed, uses a small (1 x 4 mm) ring resonator photonic chip embedded in a plastic card able to provide passive transport of human samples. This “disposable photonics” platform is able to detect and quantify anti-COVID antibodies in a human sample in a minute, making it attractive for high-throughput testing applications.

Extended-wavelength diffuse reflectance spectroscopy (EWDRS) combines two spectrometers extending the wavelength range up to 1500 nm, which provides more information than visible or near-infrared DRS alone and indicates the improved ability to differentiate biological tissues. Identification of Neurovascular bundles (NVB) is a main challenging during surgery, while current EWDRS studies have been prove the ability in open surgeries. However, theoretical simulations for complex multilayer structure in EWDRS range have yet to be reported. Monte Carlo (MC) model has been developed to accurately simulate the random propagations in complex and multilayered structures in various optical applications. We report the design and development of an EWDRS system with a fiber optic EWDRS probe. In addition, 2-layer tissue simulating phantoms with different top-layer thickness are developed and MC simulations of EWDRS spectra based on the optical properties of phantoms are compared against empirical measurements, demonstrating the accuracy of MC model in simulating multilayers structures and a superficial bias in probe sampling volume. Finally, measurements taken during dissection of the NVB in an ex vivo chicken thigh animal model are reported and confirm the ability of EWDRS to identify peripheral NVB from adjacent tissues. The results showed the developed phantoms had the ability to mimic blood content and lipid absorption features in visible and near-infrared region and simulated spectra had same tendency as measured spectra. Additionally, the classification results from the animal model displayed the overall accuracy was over 92%, which indicated the feasibility of identification of NVB from adjacent tissues.

We demonstrate three-wavelength spatial frequency domain imaging (SFDI) of a moving arm under a room light with suppressing motion artifact and biased reflectance based on an 8-tap CMOS image sensor developed in our research group. The images for two projected patters for three wavelengths and that for only ambient light are captured. Three LEDs with the wavelengths of 660nm, 780nm, and 850nm were utilized to decompose the oxy-/ deoxyhemoglobin and melanin concentrations. The exposure time for each frame was 70ms. The total hemoglobin, tissue oxygen saturation, and scattering coefficient maps were obtained without any significant motion artifact.

We demonstrate simultaneous multi-band spatial frequency domain imaging (SFDI) and blood flow mapping by multi-exposure laser speckle contrast imaging (MELSCI) with a laboratory-designed 2x2-aperture 4-tap CMOS image sensor. The proposed imaging device is composed of an array of sub-image sensors. This multi-aperture configuration realizes multi-wavelength imaging to estimate chromophore concentrations and wavelength-division-multiplexed imaging for multi-band SFDI and MELSCI. For SFDI, 450, 550, 660nm LEDs were used as the light sources of a DMD. For MELSCI, a 785nm LD was used for flood illumination. Reflectance and K2 maps of a human arm before and after an exercise was measured.

Reproducibility between different replicas of the same device is an important aspect in the biomedical research field. Multiple replicas of a dual-wavelength, single-channel “NIRSBOX” device have been assembled and characterized. In this work, we present their full performance assessment. Characterization is focused on measurement accuracy, reproducibility, and reliability, following well-defined and widely adopted procedures to assess the quality of diffuseoptics instruments. The results of the performance assessment procedures are promising, demonstrating highly reproducible performances over different time-domain near-infrared spectroscopy devices, a feature of paramount importance when it comes to comparing results from different instruments, e.g. in multicenter studies.