SignificanceMultiparameter spectrophotometry (MPS) provides a powerful tool for accurate characterization of turbid materials in applications such as analysis of material compositions, assay of biological tissues for clinical diagnosis and food safety monitoring.AimThis work is aimed at development and validation of a rapid inverse solver based on a particle swarm optimization (PSO) algorithm to retrieve the radiative transfer (RT) parameters of absorption coefficient, scattering coefficient and anisotropy factor of a turbid sample.ApproachMonte Carlo (MC) simulations were performed to obtain calculated signals for comparison to the measured ones of diffuse reflectance, diffuse transmittance and forward transmittance. An objective function has been derived and combined with the PSO algorithm to iterate MC simulations for MPS.ResultsWe have shown that the objective function can significantly reduce the variance in calculated signals by local averaging of an inverse squared error sum function between measured and calculated signals in RT parameter space. For validation of the new objective function for PSO based inverse solver, the RT parameters of 20% Intralipid solutions have been determined from 520 to 1000 nm which took about 2.7 minutes on average to complete signal measurement and inverse calculation per wavelength.ConclusionThe rapid solver enables MPS to be translated into easy-to-use and cost-effective instruments without integrating sphere for material characterization by separating and revealing compositional profiles at the molecular and particulate scales.
We present the results of a validation study on the uniqueness of a new method of multiparameter spectrophotometry (MPS) without integrating spheres to determine radiation transfer (RT) parameters by measurement of 20% Intralipid samples. The new MPS method is based on a robust stochastic optimization algorithm combined with Monte Carlo simulation to model light matter interaction. Our results prove the uniqueness of the inverse solutions for the of MPS method, which can be further developed in easy-to-use instrument for determination of RT parameters of turbid samples.
SignificanceAs a noncontact method, imaging photoplethysmography (iPPG) may provide a powerful tool to measure pulsatile pressure wave (PPW) in superficial arteries and extract biomarkers for monitoring of artery wall stiffness.AimWe intend to develop a approach for extraction of the very weak cardiac component from iPPG data by identifying locations of strong PPW signals with optimized illumination wavelength and determining pulse wave velocity (PWV).ApproachMonochromatic in vivo iPPG datasets have been acquired from left hands to investigate various algorithms for retrieval of PPW signals, distribution maps and waveforms, and their dependence on arterial location and wavelength.ResultsA robust algorithm of pixelated independent component analysis (pICA) has been developed and combined with spatiotemporal filtering to retrieve PPW signals. Spatial distributions of PPW signals have been mapped in 10 wavelength bands from 445 to 940 nm and waveforms were analyzed at multiple locations near the palmar artery tree. At the wavelength of 850 nm selected for timing analysis, we determined PWV values from 12 healthy volunteers in a range of 0.5 to 5.8 m/s across the hand region from wrist to midpalm and fingertip.ConclusionsThese results demonstrate the potentials of the iPPG method based on pICA algorithm for translation into a monitoring tool to characterize wall stiffness of superficial artery by rapid and noncontact measurement of PWV and other biomarkers within 10 s.
Rapid and label-free cell assay presents a challenging and significant problem that have wide applications in life science and clinics. We report here a method that combines polarization diffraction imaging flow cytometry (p-DIFC) with deep convolutional neural network (CNN) based image analysis for solving the above problem. Cross-polarized diffraction image (p-DI) pairs were acquired from 6185 cells in 5 types to investigate their uses for accurate classification. Different CNN architects have been studied to develop a compact architect named DINet which has relatively small set of network parameter for fast training and test. The averaged accuracy among the 5 groups of p-DI data ranges from 98.7% to 99.2%. With the DINet, the strong potentials of the p-DIFC method for morphology based and label-free cell assay have been demonstrated.
Development of cost-effective methods and instruments to accurately determine optical parameters of turbid materials can have wide applications in life science research and clinical applications such as blood test and assay of cell suspensions. We report a fast and photodiode based method without integrating spheres to measure three light scattering signals and determine absorption coefficient, scattering coefficient and anisotropy factor of the sample using only one optically thick sample. Results of optical parameters for microsphere suspension samples have been obtained from 460 to 1000nm and validated against the results predicted by Mie theory. Using a GPU executed Monte Carlo code and gradient decent based inverse algorithm, the inverse solutions for determination of optical parameters and their spectra can be achieved in real-time. The uniqueness of the inverse solutions has been proved.
Diffuse reflectance standards of known hemispherical reflectance Rh are widely used in optical and imaging studies. We have developed a stochastic surface model to investigate light reflection and roughness dependence. Through Monte Carlo simulations, the angle-resolved distributions of reflected light have been modeled as the results of local surface reflection with a constant reflectance Rs representing the overall ability of a reflectance standard. The surface was modeled by an ensemble of random Gaussian surface profiles parameterized by a mean surface height δ and transverse correlation length a. By decreasing δ / a, the calculated reflected light distributions were found to transit from Lambertian to specular reflection regime. Reflected light distributions were measured with three standards by nominal reflectance Rh valued at 10%, 80%, and 99%. The calculated results agree well with the measured data in their angular distributions at different incident angles by setting Rs = Rh and δ = a = 3.5 μm.
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.