Kenneth M. Jacobs

Determination of complex refractive index of polystyrene microspheres from 370 to 1610 nm

We introduce an inverse method for determining simultaneously the real and imaginary refractive indices of microspheres based on integrating sphere measurements of diffuse reflectance and transmittance, and Monte Carlo modelling in conjunction with the Mie theory. The results for polystyrene microspheres suspended in water are presented.

Determination of refractive indices of porcine skin tissues and Intralipid at eight wavelengths between 325 and 1557 nm

We constructed an automated reflectometry system for accurate measurement of coherent reflectance curves of turbid samples and analyzed the presence of coherent and diffuse reflection near the specular reflection angle. An existing method has been validated to determine the complex refractive indices of turbid samples on the basis of nonlinear regression of the coherent reflectance curves by Fresnels equations. The complex refractive indices of fresh porcine skin epidermis and dermis tissues and Intralipid solutions were determined at eight wavelengths: 325, 442, 532, 633, 850, 1064, 1310, and 1557 nm.

A primary method for determination of optical parameters of turbid samples and application to intralipid between 550 and 1630nm

The lack of a primary method for determination of optical parameters remains a significant barrier in optical study of turbid media. We present a complete system of experimental setups and Monte Carlo modeling tools for fast and accurate solution of the inverse problem from the measured signals of homogeneous turbid samples. The calibration of the instrument and validation of the Monte Carlo modeling have been carried out to ensure the accuracy of the inverse solution. We applied this method to determine the optical parameters of turbid media of 10% intralipid between 550 and 940 nm and 20% intralipid between 550 and 1630 nm.

Development of a diffraction imaging flow cytometer

Diffraction images record angle-resolved distribution of scattered light from a particle excited by coherent light and can correlate highly with the 3D morphology of a particle. We present a jet-in-fluid design of flow chamber for acquisition of clear diffraction images in a laminar flow. Diffraction images of polystyrene spheres of different diameters were acquired and found to correlate highly with the calculated ones based on the Mie theory. Fast Fourier transform analysis indicated that the measured images can be used to extract sphere diameter values. These results demonstrate the significant potentials of high-throughput diffraction imaging flow cytometry for extracting 3D morphological features of cells.

Angle-resolved Mueller matrix study of light scattering by B-cells at three wavelengths of 442, 633, and 850 nm

Angle-resolved signals of polarized light scattered by biological cells provide rich information on cell morphology. Quantitative study of these signals can lead to new methods to develop and improve high-throughput instrumentation for cell probing such as scattering-based flow cytometry. We employ a goniometer system with a photoelastic modulation scheme to determine selected Mueller matrix elements of B-cell hydrosol samples. The angular dependence of S(11), S(12), and S(34) is determined from the scattered light signals between 10 and 160 deg at the three wavelengths 442, 633, and 850 nm. A finite-difference, time-domain (FDTD) method and coated-sphere model are used to investigate the effect of nuclear refractive index on the angle-resolved Mueller elements at different wavelengths using the 3-D structures of selected B cells reconstructed from confocal images. With these results, we demonstrate the value of the light-scattering method in obtaining the cell morphology information.

Label-free classification of cultured cells through diffraction imaging

Automated classification of biological cells according to their 3D morphology is highly desired in a flow cytometer setting. We have investigated this possibility experimentally and numerically using a diffraction imaging approach. A fast image analysis software based on the gray level co-occurrence matrix (GLCM) algorithm has been developed to extract feature parameters from measured diffraction images. The results of GLCM analysis and subsequent classification demonstrate the potential for rapid classification among six types of cultured cells. Combined with numerical results we show that the method of diffraction imaging flow cytometry has the capacity as a platform for high-throughput and label-free classification of biological cells.

Diffraction imaging of spheres and melanoma cells with a microscope objective

Diffraction imaging of polystyrene spheres and B16F10 mouse melanoma cells embedded in gel has been investigated with a microscope objective. The diffraction images acquired with the objective from a sphere have been shown to be comparable to the Mie theory based projection images of the scattered light if the objective is translated to defocused positions towards the sphere. Using a confocal imaging based method to reconstruct and analyze the 3D structure, we demonstrated that genetic modifications in these cells can induce morphological changes and the modified cells can be used as an experimental model for study of the correlation between 3D morphology features and diffraction image data.

Polarization Imaging and Classification of Jurkat T and Ramos B Cells Using a Flow Cytometer

Label‐free and rapid classification of cells can have awide range of applications in biology. We report a robust method of polarization diffraction imaging flow cytometry (p‐DIFC) for achieving this goal. Coherently scattered light signals are acquired from single cells excited by a polarized laser beam in the form of two cross‐polarized diffraction images. Image texture and intensity parameters are extracted with a gray level co‐occurrence matrix (GLCM) algorithm to obtain an optimized set of feature parameters as the morphological “fingerprints” for automated cell classification. We selected the Jurkat T cells and Ramos B cells to test the p‐DIFC methods capacity for cell classification. After detailed statistical analysis, we found that the optimized feature vectors yield accuracies of classification between the Jurkat and Ramos ranging from 97.8% to 100% among different cell data sets. Confocal imaging and three‐dimensional reconstruction were applied to gain insights on the ability of p‐DIFC method for classifying the two cell lines of highly similar morphology. Based on these results we conclude that the p‐DIFC method has the capacity to discriminate cells of high similarity in their morphology with “fingerprints” features extracted from the diffraction images, which may be attributed to subtle but statistically significant differences in the nucleus‐to‐cell volume ratio in the case of Jurkat and Ramos cells.

Analysis of diffraction imaging in non-conjugate configurations

Diffraction imaging of scattered light allows extraction of information on scatterers morphology. We present a method for accurate simulation of diffraction imaging of single particles by combining rigorous light scattering model with ray-tracing software. The new method has been validated by comparison to measured images of single microspheres. Dependence of fringe patterns on translation of an objective based imager to off-focus positions has been analyzed to clearly understand diffraction imaging with multiple optical elements. The calculated and measured results establish unambiguously that diffraction imaging should be pursued in non-conjugate configurations to ensure accurate sampling of coherent light distribution from the scatterer.

Multivariate analysis of Monte Carlo generated images for diagnosis of dysplastic lesions

Early detection of malignant melanoma is critical to improve the survival rates of patients with this aggressive malignancy. We constructed an imaging system employing two liquid-crystal tunable filters to acquire in vivo spectral images of dysplastic lesions from patients at 31 wavelengths from 500 to 950nm. These reflectance images were analyzed in search of optical signatures for quantitative characterization of dysplastic nevi and malignant melanoma. A principal component analysis (PCA) algorithm was developed to examine the spectral imaging data in the component space and an index of spreading of clustering pixels (SCP) was defined to measure the degree of clustering in the distribution of image pixel scores in a component space. We found that SCP of differential polarimetric images correlate strongly with the degree of dysplasia for 4 lesions. However, many questions remain unanswered on the relations between PCA results and the spatial and spectral characteristics of the image data because of limited spectral image data from the patients. To fully improve our understanding on the multivariate analysis of spectral imaging data, we have developed a parallel Monte Carlo code to efficiently generate reflectance images from given distribution of optical parameters in a skin lesion phantom. With this tool, we have investigated numerically the dependence of score distribution and SCP in the component sub-spaces on lesion size and position. These numerical results provide a foundation for our future study to identify optical signature of dysplastic lesion and melanoma in the skin.