Single cell nuclei were investigated using two-dimensional angularly and spectrally resolved scattering microscopy. We show that even for a qualitative comparison of experimental and theoretical data, the standard Mie model of a homogeneous sphere proves to be insufficient. Hence, an accelerated finite-difference time-domain method using a graphics processor unit and domain decomposition was implemented to analyze the experimental scattering patterns. The measured cell nuclei were modeled as single spheres with randomly distributed spherical inclusions of different size and refractive index representing the nucleoli and clumps of chromatin. Taking into account the nuclear heterogeneity of a large number of inclusions yields a qualitative agreement between experimental and theoretical spectra and illustrates the impact of the nuclear micro- and nanostructure on the scattering patterns.
In computed tomography (CT), scattering causes server quality degradation of the reconstructed CT images by introducing streaks and cupping artifacts which reduce the detectability of low contrast objects. Monte Carlo (MC) simulation is considered the most accurate approach for scatter estimation. However, the existing MC estimators are computationally expensive, especially for high-resolution flat-panel CT. In this paper, we propose a fast and accurate MC photon transport model which describes the physics within the 1 keV to 1 MeV range using multiple controllable key parameters. Based on this model, scatter computation for a single projection can be completed within a range of a few seconds under well-defined model parameters. Smoothing and interpolation are performed on the estimated scatter to accelerate the scatter calculation without compromising accuracy too much compared to measured near scatter-free projection images. Combining the fast scatter estimation with the filtered backprojection (FBP), scatter correction is performed effectively in an iterative manner. To evaluate the proposed MC model, we have conducted extensive experiments on the simulated data and real-world high-resolution flat-panel CT. Compared to the state-of-the-art MC simulators, the proposed MC model achieved a 15$$\times$$ × acceleration on a single-GPU compared to the GPU implementation of the Penelope simulator (MCGPU) utilizing several acceleration techniques, and a 202 $$\times$$ × speed-up on a multi-GPU system compared to the multi-threaded state-of-the-art EGSnrc MC simulator. Furthermore, it is shown that for high-resolution images, scatter correction with sufficient accuracy is accomplished within one to three iterations using a FBP and the proposed fast MC photon transport model.
Simulation results depend not only on the precision of the floating point arithmetic with respect to the numerical accuracy of the results. They are also sensitive to diff erences of floating point arithmetic implementations of diff erent hybrid and parallel computing systems such as CPUs, GPUs, dedicated processors like the Cell processor or the GRAPE sp ecial-purpose computer with the same precision. As floating point op erations may not maintain basic properties like associative or distributive properties of the underlying mathematical op erations, the numerical values computed by simulations may become dependent on the hardware platform and the sp ecific run of the program. Numerical accuracy control of the simulation would identifY significant variations of the simulation results due to these numerical effects. For this purpose, the numerical accuracy is controlled in this paper by a method for rounding error estimation based on the discrete stochastic arithmetic (DSA). This method, which is investigated on both CPUs and GPUs here, is generally applicable independent of the algorithm and can provide a tight estimation of the rounding errors while increasing the computational time only by a factor of approximately 3 in the ideal case. It is shown that the method can be applied automatically without modifYing source code. Furthermore, performance improvements compared to the numerical accuracy control based on higher precision arithmetic can be obtained.
Fast and nondestructive laser light scattering measurements characterize the quality of surfaces with sub-100 nm structures during the production process. The aim is to detect defective surface structures. The measurement procedure is based on a primary comparison of measured speckle patterns with a multitude of numerically calculated speckle patterns caused by defect-free and defective surface structures. The comparison shows characteristic light scattering effects, which have to be detected by the in-process measurement setup. An efficient rigorous algorithm, implemented on a graphics processing unit, calculates the scattered light intensity distributions within reasonable computing times. The measurements are performed with an angle resolved light scattering measurement setup. The measuring procedure is applied to zinc oxide nanograss surfaces.
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