Optimization of the Monte Carlo code for modeling of photon migration in tissue

The Monte Carlo method is frequently used to simulate light transport in turbid media because of its simplicity and flexibility, allowing to analyze complicated geometrical structures. Monte Carlo simulations are, however, time consuming because of the necessity to track the paths of individual photons. The time consuming computation is mainly associated with the calculation of the logarithmic and trigonometric functions as well as the generation of pseudo-random numbers. In this paper, the Monte Carlo algorithm was developed and optimized, by approximation of the logarithmic and trigonometric functions. The approximations were based on polynomial and rational functions, and the errors of these approximations are less than 1% of the values of the original functions. The proposed algorithm was verified by simulations of the time-resolved reflectance at several source-detector separations. The results of the calculation using the approximated algorithm were compared with those of the Monte Carlo simulations obtained with an exact computation of the logarithm and trigonometric functions as well as with the solution of the diffusion equation. The errors of the moments of the simulated distributions of times of flight of photons (total number of photons, mean time of flight and variance) are less than 2% for a range of optical properties, typical of living tissues. The proposed approximated algorithm allows to speed up the Monte Carlo simulations by a factor of 4. The developed code can be used on parallel machines, allowing for further acceleration.     

Novel Geometrical Voxelization Approach with Application to Streamlines

This paper presents a novel geometrical voxelization algorithm for polygonal models. First, distance computation is performed slice by slice on graphics processing units (GPUs) between geometrical primitives and voxels for line/surface voxelization. A novel solid filling process is then proposed to assist surface voxelization and achieve solid voxelization. Furthermore, using the proposed transfer functions, both binary and anti-aliasing voxelizations are achievable. Finally, the proposed approach can be applied to voxelize streamlines for 3D vector fields using line voxelization. The proposed approach obtains desired experimental results.     

基于Monte Carlo在体生物光学成像的光子传输模型

随着分子标记技术和光学成像技术的发展,在体生物光学成像倍受关注,并广泛应用于对生物组织的生理或病理过程的无损实时动态成像.研究生物组织中的光子传输模型和光子传输规律,是开展在体生物光学成像研究的两个关键环节.提出了一种基于Monte Carlo方法的在体生物光学成像中的光子传输模型.已知荧光光源参数、生物组织参数和探测器参数,建立荧光光源发射光子、光子在生物组织中传输的数学模型,并利用Monte Carlo方法实现这些模型.最后做了对比实验,实验结果表明了该算法的正确性和有效性.     

基于拟蒙特卡罗方法的三维医学重建模型体积测量方法研究

测量医学图像三维重建病灶组织与器官的体积, 为临床诊疗与医学研究提供更可靠的数据。通过先对系列二维医学图像进行预处理, 并对处理后的图像进行三维重建, 可以获得较好的只有表面三角网格的三维重建模型; 然后使用拟蒙特卡罗方法在构造的包围盒内生成低差异分布的随机点, 通过计算模型内的点数量与全部随机点数量的比例进行体积测量。分别对四组重建的三维模型进行体积测量并与蒙特卡罗方法相比, 拟蒙特卡罗方法在测量体积方面具有较好的效果。拟蒙特卡罗方法对三维医学重建模型体积测量可以得到较准确的体积数据, 具有一定的实际应用与理论研究价值。     

MC3D-3D continuum radiative transfer, Version 2

A revised and greatly improved version of the 3D continuum radiative transfer code MC3D is presented. It is based on the Monte Carlo method and solves the radiative transfer problem self-consistently. It is designed for the simulation of dust temperatures in arbitrary geometric configurations and the resulting observables: spectral energy distributions, wavelength-dependent images, and polarization maps. The main objective is the investigation of “dust-dominated” astrophysical systems such as young stellar objects surrounded by an optically thick circumstellar disk and an optically thin(ner) envelope, debris disks around more evolved stars, asymptotic giant branch stars, the dust component of the interstellar medium, and active galactic nuclei.     

Realistic Ultrasound Simulation of Complex Surface Models Using Interactive Monte‐Carlo Path Tracing

Ray‐based simulations have been shown to generate impressively realistic ultrasound images in interactive frame rates. Recent efforts used GPU‐based surface raytracing to simulate complex ultrasound interactions such as multiple reflections and refractions. These methods are restricted to perfectly specular reflections (i.e. following only a single reflective/refractive ray), whereas real tissue exhibits roughness of varying degree at tissue interfaces, causing partly diffuse reflections and refractions. Such surface interactions are significantly more complex and can in general not be handled by conventional deterministic raytracing approaches. However, these can be efficiently computed by Monte‐Carlo sampling techniques, where many ray paths are generated with respect to a probability distribution. In this paper, we introduce Monte‐Carlo raytracing for ultrasound simulation. This enables the realistic simulation of ultrasound‐tissue interactions such as soft shadows and fuzzy reflections. We discuss how to properly weight the contribution of each ray path in order to simulate the behaviour of a beamformed ultrasound signal. Tracing many individual rays per transducer element is easily parallelizable on modern GPUs, as opposed to previous approaches based on recursive binary raytracing. We further propose a significant performance optimization based on adaptive sampling.     

Mixed Monte Carlo/Molecular Dynamics simulations of the prion protein

In this paper we present the results of mixed Monte Carlo/Molecular Dynamics (MC/MD) simulations of the D178N mutant of the human prion protein. We have used the MC moves for polypeptide sampling known as Concerted Rotations with Angles (CRA) to selectively sample the region of the prion protein comprising the β-sheet and one of the α-helices. The results indicate that the MC/MD simulations sample the phase space substantially faster than regular Molecular Dynamics simulations starting with the same initial conditions. This work further indicates the MC/MD technique as a potentially powerful simulation tool, allowing the selective sampling of a region of a physical system that is deemed important.     

A parallel Monte Carlo code for planar and SPECT imaging: implementation, verification and applications in (131)I SPECT.

This paper reports the implementation of the SIMIND Monte Carlo code on an IBM SP2 distributed memory parallel computer. Basic aspects of running Monte Carlo particle transport calculations on parallel architectures are described. Our parallelization is based on equally partitioning photons among the processors and uses the Message Passing Interface (MPI) library for interprocessor communication and the Scalable Parallel Random Number Generator (SPRNG) to generate uncorrelated random number streams. These parallelization techniques are also applicable to other distributed memory architectures. A linear increase in computing speed with the number of processors is demonstrated for up to 32 processors. This speed-up is especially significant in Single Photon Emission Computed Tomography (SPECT) simulations involving higher energy photon emitters, where explicit modeling of the phantom and collimator is required. For (131)I, the accuracy of the parallel code is demonstrated by comparing simulated and experimental SPECT images from a heart/thorax phantom. Clinically realistic SPECT simulations using the voxel-man phantom are carried out to assess scatter and attenuation correction.     

Performance predictions for high altitude self-contained satellite navigation systems

This paper summarizes a study of self-contained navigation system accuracies for high altitude orbital missions projected through 1985. It is found that root-sum-square (RSS) satellite position errors as low as a few hundred feet are obtainable with range measuring systems. Totally autonomous, nonradiating systems are operationally more attractive, and some promising candidates in this category give RSS position errors from about one-half to a few miles, depending on the orbit. The extended Kalman filter and extensive Monte Carlo simulations are used as the basic analysis tools. Favorable comparisons between covariance analysis and Monte Carlo results are reported. Time-average statistics are also found to provide useful figures of merit.     

Camera motion estimation by tracking contour deformation: Precision analysis

An algorithm to estimate camera motion from the progressive deformation of a tracked contour in the acquired video stream has been previously proposed. It relies on the fact that two views of a plane are related by an affinity, whose six parameters can be used to derive the six degrees-of-freedom of camera motion between the two views. In this paper we evaluate the accuracy of the algorithm. Monte Carlo simulations show that translations parallel to the image plane and rotations about the optical axis are better recovered than translations along this axis, which in turn are more accurate than rotations out of the plane. Concerning covariances, only the three less precise degrees-of-freedom appear to be correlated. In order to obtain means and covariances of 3D motions quickly on a working robot system, we resort to the Unscented Transformation (UT) requiring only 13 samples per view, after validating its usage through the previous Monte Carlo simulations. Two sets of experiments have been performed: short-range motion recovery has been tested using a Staübli robot arm in a controlled lab setting, while the precision of the algorithm when facing long translations has been assessed by means of a vehicle-mounted camera in a factory floor. In the latter more unfavourable case, the obtained errors are around 3%, which seems accurate enough for transferring operations.     

Research of wall roughness effects based on Q criterion

The wall roughness shows a great impact on flow field in a micro-channel. This paper investigates wall roughness effects based on Q criterion. The direct simulation Monte Carlo method is employed to simulate the flow field. It reveals the physical mechanism behind the phenomenon that the Poiseuille number (\(fRe\)) increases more significantly for smaller roughness element spacing. Besides, it demonstrates the phenomenon of flow fluctuation and the physical mechanism behind it. Furthermore, it indicates that the Poiseuille number of roughness element combination is determined by large roughness elements. It also provides suggestions on the simplification of real wall roughness profile.     

基于集成众核的3D蒙特卡罗半导体器件模拟器

3D蒙特卡罗器件模拟计算量大,计算量随网格与粒子数增加而急剧增加。通过分析3D蒙卡模拟加速热点和进一步可并行性,研究有效电势方法的集成众核并行方案;研究粒子自由飞行、统计模拟信息、计算表面粗糙散射等热点并行方案,最终实现基于CPU/MIC的三级并行3D蒙特卡罗器件模拟软件。实验结果显示,三级并行比单级并行获得更好的性能;当提高模拟精度时,相比单级并行,三级并行蒙特卡罗模拟加速比增加。     

Alias-free voxelization of geometric objects

Introduces a new concept for alias-free voxelization of geometric objects based on a voxelization model (V-model). The V-model of an object is its representation in 3D continuous space by a trivariate density function. This function is sampled during the voxelization and the resulting values are stored in a volume buffer. This concept enables us to study general issues of sampling and rendering separately from object-specific design issues. It provides us with a possibility to design such V-models, which are correct from the point of view of both the sampling and rendering, thus leading to both alias-free volumetric representation and alias-free rendered images. We performed numerous experiments with different combinations of V-models and reconstruction techniques. We have shown that the V-model with a Gaussian surface density profile combined with tricubic interpolation and Gabor derivative reconstruction outperforms the previously published technique with a linear density profile. This enables higher fidelity of images rendered from volume data due to increased sharpness of edges and thinner surface patches     

A new simulation approach to the freezing transition

The task of accurately locating fluid-crystal phase boundaries by computer simulation is hampered by problems associated with traversing mixed-phase states. We describe a recently introduced Monte Carlo (MC) method that circumvents this problem by implementing a global coordinate transformation (“phase switch”) which takes the system from one pure phase to the other in a single MC step. The method is potentially quite general. We illustrate it by application to the freezing of hard spheres.     

Source of error in calculation of optical diffuse reflectance from turbid media using diffusion theory

Diffusion theory and similarity relations were used to calculate the optical diffuse reflectance of an infinitely narrow laser beam incident upon a semi-infinite turbid medium. The results were analyzed by comparison with the accurate results from Monte Carlo simulations. Because a large number of photon packets were traced, the variance of the results from Monte Carlo simulations was small enough to reveal the detailed defects of the diffusion theory and the similarity relations, which are broadly used in photomedicine. We demonstrated that both diffusion theory and similarity relations provide very accurate results when the photon sources are isotropic and buried more deeply than one transport mean free path in turbid media. We found that the key factor affecting the accuracy of the diffusion theory application was the conversion from the infinitely narrow laser beam to an isotropic point source in turbid media.     

Nonlinear time series forecasting with Bayesian neural networks

The Bayesian learning provides a natural way to model the nonlinear structure as the artificial neural networks due to their capability to cope with the model complexity. In this paper, an evolutionary Monte Carlo (MC) algorithm is proposed to train the Bayesian neural networks (BNNs) for the time series forecasting. This approach called as Genetic MC is based on Gaussian approximation with recursive hyperparameter. Genetic MC integrates MC simulations with the genetic algorithms and the fuzzy membership functions. In the implementations, Genetic MC is compared with the traditional neural networks and time series techniques in terms of their forecasting performances over the weekly sales of a Finance Magazine.     

基于RMC的蒙特卡罗程序性能优化

蒙特卡罗MC方法是核反应堆设计和分析中重要的粒子输运模拟方法。MC方法能够模拟复杂几何形状且计算结果精度高,缺点是需要耗费大量时间进行上亿规模粒子模拟。如何提高蒙特卡罗程序的性能成为大规模蒙特卡罗数值模拟的挑战。基于堆用蒙特卡罗分析程序RMC,先后开展了基于TCMalloc动态内存分配优化、OpenMP线程调度策略优化、vector内存对齐优化和基于HDF5的并行I/O优化等一系列优化手段,对于200万粒子的算例,使其总体性能提高26.45%以上。     

Monte Carlo domain decomposition for robust nuclear reactor analysis

Monte Carlo (MC) neutral particle transport codes are considered the gold-standard for nuclear simulations, but they cannot be robustly applied to high-fidelity nuclear reactor analysis without accommodating several terabytes of materials and tally data. While this is not a large amount of aggregate data for a typical high performance computer, MC methods are only embarrassingly parallel when the key data structures are replicated for each processing element, an approach which is likely infeasible on future machines. The present work explores the use of spatial domain decomposition to make full-scale nuclear reactor simulations tractable with Monte Carlo methods, presenting a simple implementation in a production-scale code. Good performance is achieved for mesh-tallies of up to 2.39 TB distributed across 512 compute nodes while running a full-core reactor benchmark on the Mira Blue Gene/Q supercomputer at the Argonne National Laboratory. In addition, the effects of load imbalances are explored with an updated performance model that is empirically validated against observed timing results. Several load balancing techniques are also implemented to demonstrate that imbalances can be largely mitigated, including a new and efficient way to distribute extra compute resources across finer domain meshes.     

Uncertainty quantification through the Monte Carlo method in a cloud computing setting

The Monte Carlo (MC) method is the most common technique used for uncertainty quantification, due to its simplicity and good statistical results. However, its computational cost is extremely high, and, in many cases, prohibitive. Fortunately, the MC algorithm is easily parallelizable, which allows its use in simulations where the computation of a single realization is very costly. This work presents a methodology for the parallelization of the MC method, in the context of cloud computing. This strategy is based on the MapReduce paradigm, and allows an efficient distribution of tasks in the cloud. This methodology is illustrated on a problem of structural dynamics that is subject to uncertainties. The results show that the technique is capable of producing good results concerning statistical moments of low order. It is shown that even a simple problem may require many realizations for convergence of histograms, which makes the cloud computing strategy very attractive (due to its high scalability capacity and low-cost). Additionally, the results regarding the time of processing and storage space usage allow one to qualify this new methodology as a solution for simulations that require a number of MC realizations beyond the standard.