Comparative study of 3D morphology and functions on genetically engineered mouse melanoma cells.

Quantification of 3D morphology and measurement of cellular functions were performed on the mouse melanoma cell lines of B16F10 to investigate the intriguing problem of structure-function relations in the genetically engineered cells with GPR4 overexpression. Results of 3D analysis of cells in suspension and phase contrast imaging of adherent cells yield consistent evidence that stimulation of the proton-sensing GPR4 receptor in these cells may modify significantly their morphology with diminishing ability to produce membrane protrusions and to migrate. Examination of the 3D parameters of mitochondria provide further insights on the measured variation of the maximal capacity of oxygen consumption rate among the genetically modified cells, indicating that the proton-sensing receptor may regulate cancer cell metabolism with increased mitochondrial surface area. Our study demonstrates clearly the significant benefits of quantitative 3D morphological study in illuminating cellular functions and development of novel morphology based cell assay methods.

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.

Reconstruction of a cone-beam CT image via forward iterative projection matching.

PURPOSE: To demonstrate the feasibility of reconstructing a cone-beam CT (CBCT) image by deformably altering a prior fan-beam CT (FBCT) image such that it matches the anatomy portrayed in the CBCT projection data set. METHODS: A prior FBCT image of the patient is assumed to be available as a source image. A CBCT projection data set is obtained and used as a target image set. A parametrized deformation model is applied to the source FBCT image, digitally reconstructed radiographs (DRRs) that emulate the CBCT projection image geometry are calculated and compared to the target CBCT projection data, and the deformation model parameters are adjusted iteratively until the DRRs optimally match the CBCT projection data set. The resulting deformed FBCT image is hypothesized to be an accurate representation of the patient's anatomy imaged by the CBCT system. The process is demonstrated via numerical simulation. A known deformation is applied to a prior FBCT image and used to create a synthetic set of CBCT target projections. The iterative projection matching process is then applied to reconstruct the deformation represented in the synthetic target projections; the reconstructed deformation is then compared to the known deformation. The sensitivity of the process to the number of projections and the DRR/CBCT projection mismatch is explored by systematically adding noise to and perturbing the contrast of the target projections relative to the iterated source DRRs and by reducing the number of projections. RESULTS: When there is no noise or contrast mismatch in the CBCT projection images, a set of 64 projections allows the known deformed CT image to be reconstructed to within a nRMS error of 1% and the known deformation to within a nRMS error of 7%. A CT image nRMS error of less than 4% is maintained at noise levels up to 3% of the mean projection intensity, at which the deformation error is 13%. At 1% noise level, the number of projections can be reduced to 8 while maintaining CT image and deformation errors of less than 4% and 13%, respectively. The method is sensitive to contrast mismatch between the simulated projections and the target projections when the soft-tissue contrast in the projections is low. CONCLUSIONS: By using prior knowledge available in a FBCT image, the authors show that a CBCT image can be iteratively reconstructed from a comparatively small number of projection images, thus saving acquisition time and reducing imaging dose. This will enable more frequent daily imaging during radiation therapy. Because the process preserves the CT numbers of the FBCT image, the resulting 3D image intensities will be more accurate than a CBCT image reconstructed via conventional backprojection methods. Reconstruction errors are insensitive to noise at levels beyond what would typically be found in CBCT projection data, but are sensitive to contrast mismatch errors between the CBCT projection data and the DRRs.

Validity of a closed-form diffusion solution in P1 approximation for reflectance imaging with an oblique beam of arbitrary profile.

Determination of optical parameters of turbid media from reflectance image data is an important class of inverse problems due to its potential for noninvasive characterization of materials and biological tissues, which demands rapid modeling tools to generate calculated images. We treat the problem of reflectance imaging with homogeneous semi-infinite turbid media as a boundary-value problem of diffusion type in the P1 approximation to the radiative transfer equation. A closed-form solution has been obtained for an oblique incident beam of arbitrary profile and its accuracy has been examined against a Monte Carlo method and measured data. We find that the diffusion solution provides a sufficiently accurate tool to rapidly calculate reflectance images for samples of large or moderate scattering albedo illuminated by a beam of arbitrary profile as long as the anisotropy factor remains less than 0.7 and single scattering albedo larger than 0.8. The closed-form solution can thus be used as a part of a forward modeling toolbox to determine optical parameters from reflectance image data in combination with other method such as the Monte Carlo simulation.

Numerical study of reflectance imaging using a parallel Monte Carlo method.

Reflectance imaging of biological tissues with visible and near-infrared light has the significant potential to provide a noninvasive and safe imaging modality for diagnosis of dysplastic and malignant lesions in the superficial tissue layers. The difficulty in the extraction of optical and structural parameters lies in the lack of efficient methods for accurate modeling of light scattering in biological tissues of turbid nature. We present a parallel Monte Carlo method for accurate and efficient modeling of reflectance images from turbid tissue phantoms. A parallel Monte Carlo code has been developed with the message passing interface and evaluated on a computing cluster with 16 processing elements. The code was validated against the solutions of the radiative transfer equation on the bidirectional reflection and transmission functions. With this code we investigated numerically the dependence of reflectance image on the imaging system and phantom parameters. The contrasts of reflectance images were found to be nearly independent of the numerical aperture (NA) of the imaging camera despite the fact that reflectance depends on the NA. This enables efficient simulations of the reflectance images using an NA at 1.00. Using heterogeneous tissue phantoms with an embedded region simulating a lesion, we investigated the correlation between the reflectance image profile or contrast and the phantom parameters. It has been shown that the image contrast approaches 0 when the single-scattering albedos of the two regions in the heterogeneous phantoms become matched. Furthermore, a zone of detection has been demonstrated for determination of the thickness of the embedded region and optical parameters from the reflectance image profile and contrast. Therefore, the utility of the reflectance imaging method with visible and near-infrared light has been firmly established. We conclude from these results that the optical parameters of the embedded region can be determined inversely from reflectance images acquired with full-field illumination at multiple incident angles or multiple wavelengths.

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.

Systematic comparison of the discrete dipole approximation and the finite difference time domain method for large dielectric scatterers.

We compare the discrete dipole approximation (DDA) and the finite difference time domain (FDTD) method for simulating light scattering of spheres in a range of size parameters x up to 80 and refractive indices m up to 2. Using parallel implementations of both methods, we require them to reach a certain accuracy goal for scattering quantities and then compare their performance. We show that relative performance sharply depends on m. The DDA is faster for smaller m, while the FDTD for larger values of m. The break-even point lies at m = 1.4. We also compare the performance of both methods for a few particular biological cells, resulting in the same conclusions as for optically soft spheres.

Evaluation of a parallel FDTD code and application to modeling of light scattering by deformed red blood cells.

A parallel Finite-Difference-Time-Domain (FDTD) code has been developed to numerically model the elastic light scattering by biological cells. Extensive validation and evaluation on various computing clusters demonstrated the high performance of the parallel code and its significant potential of reducing the computational cost of the FDTD method with low cost computer clusters. The parallel FDTD code has been used to study the problem of light scattering by a human red blood cell (RBC) of a deformed shape in terms of the angular distributions of the Mueller matrix elements. The dependence of the Mueller matrix elements on the shape and orientation of the deformed RBC has been investigated. Analysis of these data provides valuable insight on determination of the RBC shapes using the method of elastic light scattering measurements.

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.