Significance: Fiber-optic extended-wavelength diffuse reflectance spectroscopy (EWDRS) using both visible/near-infrared and shortwave-infrared detectors enables improved detection of spectral absorbances arising from lipids, water, and collagen and has demonstrated promise in a variety of applications, including detection of nerves and neurovascular bundles (NVB). Development of future applications of EWDRS for nerve detection could benefit from the use of model-based analyses including Monte Carlo (MC) simulations and evaluation of agreement between model systems and empirical measurements.Aim: The aim of this work is to characterize agreement between EWDRS measurements and simulations and inform future applications of model-based studies of nerve-detecting applications.Approach: A model-based platform consisting of an ex vivo microsurgical nerve dissection model, unique two-layer optical phantoms, and MC model simulations of fiber-optic EWDRS spectroscopic measurements were used to characterize EWDRS and compare agreement across models. In addition, MC simulations of an EWDRS measurement scenario are performed to provide a representative example of future analyses.Results: EWDRS studies performed in the common chicken thigh femoral nerve microsurgical dissection model indicate similar spectral features for classification of NVB versus adjacent tissues as reported in porcine models and human subjects. A comparison of measurements from unique EWDRS issue mimicking optical phantoms and MC simulations indicates high agreement between the two in homogeneous and two-layer optical phantoms, as well as in dissected tissues. Finally, MC simulations of measurement over a simulated NVB indicate the potential of future applications for measurement of nerve plexus.Conclusions: Characterization of agreement between fiber-optic EWDRS measurements and MC simulations demonstrates strong agreement across a variety of tissues and optical phantoms, offering promise for further use to guide the continued development of EWDRS for translational applications.
Modern mobile phone imaging sensors wide availability and high quality have enabled development of low-cost imaging and sensing approaches that utilize the camera, including those which detect diffuse optical interactions and produce quantitative transcutaneous measures analogous to clinical techniques for bilirubin and oxygenation sensing. Concurrently, recent clinical studies report overestimation bias from dark skinned patients in transcutaneous bilirubinometery (TcB) and pulse oximetry. Here, Monte Carlo simulations of TcB and oximetery were used to investigate the source of possible racial biases in clinical measurements. Simulations of device calibration studies with dark, mixed, and light skinned cohorts were tested against groups with similar and different racial distributions. Results implicate a combination of tissue optics and biased enrollment in calibration studies for systematic overestimation in both TcB and oximetry. Next, identical Monte Carlo simulations were performed with a 2D image sensor capable of detecting spatially resolved diffuse reflectance. Quantification models were developed from simulated calibration studies where reflectance was extracted from 1 to 5 unique sensor regions of interest (ROI), followed by evaluation against test cohorts with different racial distributions. The results indicated overestimation bias in darkly pigmented subjects could be reduced through incorporation of an increasing number of sensor ROI’s. Models for quantification of bilirubin were then developed using clinical data from our mobile phone based TcB study, and increasing number of sensor ROI’s improved model performance (r2) . These results suggest promise for the development of mobile image-sensor based spatially resolved diffuse reflectance for improving accuracy and reducing racial bias in transcutaneous measurements.
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.
Extreme or prolonged neonatal jaundice (hyperbilirubinemia) can result in permanent neurological impairment and even death. In developing countries, risk factors that increase the risk of neurodevelopmental impairment, such as sepsis, malnutrition, and certain genetic conditions are common. Administering treatments can be simple but identification of at-risk infants through visual screening is unreliable. Infants in the US are routinely screened prior to hospital discharge using transcutaneous bilirubinometry (TcB), a non-invasive technique based on diffuse reflectance. In low-resource settings such as rural sub-Saharan Africa, TcB devices are not available to traditional birth attendants and doctors; however, it is increasingly common for these personnel to carry mobile phones equipped with a camera and flash. We have previously reported initial feasibility of TcB utilizing the built-in camera and flash of the mobile phone, a Monte Carlo model driven design of a snap-on optical assembly. Here, we report the experience and results from clinical studies in newborns which compare mobile-phone based measurements of TcB with corresponding serum bilirubin levels. These results will lead to a discussion of feasibility and limitations for mobile-phone based TcB.
Monte Carlo (MC) modeling of photon propagation in turbid media is an essential tool for understanding optical interactions between light and tissue. Insight gathered from outputs of MC models assists in mapping between detected optical signals and bulk tissue optical properties, and as such, has proven useful for inverse calculations of tissue composition and optimization of the design of optical probes. MC models of Raman scattering have previously been implemented without consideration to background autofluorescence, despite its presence in raw measurements. Modeling both Raman and fluorescence profiles at high spectral resolution requires a significant increase in computation, but is more appropriate for investigating issues such as detection limits. We present a new Raman Fluorescence MC model developed atop an existing GPU parallelized MC framework that can run more than 300x times faster than CPU methods. The robust acceleration allows for the efficient production of both Raman and fluorescence outputs from the MC model. In addition, this model can handle arbitrary sample morphologies of excitation and collection geometries to more appropriately mimic experimental settings. We will present the model framework and initial results.
Kidney cancer affects 65,000 new patients every. As computerized tomography became ubiquitous, the number of small, incidentally detected renal masses increased. About 6,000 benign cases are misclassified radiographically as malignant and removed surgically. Raman spectroscopy (RS) has been widely demonstrated for disease discrimination, however intense near-infrared auto-fluorescence of certain tissues (e.g kidney) can present serious challenges to bulk tissue diagnosis. A 1064nm excitation dispersive detection RS system demonstrated the ability to collect spectra with superior quality in tissues with strong auto-fluorescence. Our objective is to develop a 1064 nm dispersive detection RS system capable of differentiating normal and malignant renal tissue. We will report on the design and development of a clinical system for use in nephron sparing surgeries. We will present pilot data that has been collected from normal and malignant ex vivo kidney specimens using a benchtop RS system. A total of 93 measurements were collected from 12 specimens (6 Renal Cell Carcinoma, 6 Normal ). Spectral classification was performed using sparse multinomial logistic regression (SMLR). Correct classification by SMLR was obtained in 78% of the trials with sensitivity and specificity of 82% and 75% respectively. We will present the association of spectral features with biological indicators of healthy and diseased kidney tissue. Our findings indicate that 1064nm RS is a promising technique for differentiation of normal and malignant renal tissue. This indicates the potential for accurately separating healthy and cancerous tissues and suggests implications for utilizing RS for optical biopsy and surgical guidance in nephron sparing surgery.
Infants in the US are routinely screened for risk of neurodevelopmental impairment due to neonatal jaundice using transcutaneous bilirubinometry (TcB). In low-resource settings, such as sub-Saharan Africa, TcB devices are not common, however, mobile camera-phones are now widespread. We provide an update on the development of TcB using the built-in camera and flash of a mobile phone, along with a snap-on adapter containing optical filters. We will present Monte Carlo Extreme modeling of diffuse reflectance in neonatal skin, implications in design, and refined analysis methods.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.