The adaptation method in the Monte Carlo simulation for computed tomography

Lee, Hyounggun; Yoon, Changyeon; Cho, Seungryong; Park, Sung Ho; Lee, Wonho

doi:10.1016/j.net.2015.01.010

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…The Monte Carlo simulation (MCS) is a reliable sampling method with wide applications in engineering and research [28][29][30][31]. With sufficiently large sampling space, a satisfactory accuracy can be achieved in the numerical simulation.…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo-Based Finite Element Method for the Study of Randomly Distributed Vacancy Defects in Graphene Sheets

Chu

Shi

Cursi

et al. 2018

Journal of Nanomaterials

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This paper proposed an effective stochastic finite element method for the study of randomly distributed vacancy defects in graphene sheets. The honeycomb lattice of graphene is represented by beam finite elements. The simulation results of the pristine graphene are in accordance with literatures. The randomly dispersed vacancies are propagated and performed in graphene by integrating Monte Carlo simulation (MCS) with the beam finite element model (FEM). The results present that the natural frequencies of different vibration modes decrease with the augment of the vacancy defect amount. When the vacancy defect reaches 5%, the regularity and geometrical symmetry of displacement and rotation in vibration behavior are obviously damaged. In addition, with the raise of vacancy defects, the random dispersion position of vacancy defects increases the variance in natural frequencies. The probability density distributions of natural frequencies are close to the Gaussian and Weibull distributions.

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Section: Introductionmentioning

confidence: 99%

Monte Carlo-Based Finite Element Method for the Study of Randomly Distributed Vacancy Defects in Graphene Sheets

Chu

Shi

Cursi

et al. 2018

Journal of Nanomaterials

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“…The spatial distribution of scatter on the imaging detector varies from projection to projection, requiring the MC simulators to provide projection-specific estimation of scatter while minimizing the possibility of observing artifacts associated with statistical noise in the reconstructed images (Tang and Tang 2012). A typical simulation for estimating projection-specific scatter with such levels of statistical noise will require simulating number of photon histories comparable to a clinical scenario (∼10 11 histories/projection/mAs for the whole detector (Taguchi et al 2010, Lee et al 2015 × typical 100-200 mAs for a CT scan=∼10 13 histories per projection), necessitating execution time in the order of thousands of hours for simulating a single exam, an intractable computation even on large-scale clusters.…”

Section: Introductionmentioning

confidence: 99%

A GPU-accelerated framework for rapid estimation of scanner-specific scatter in CT for virtual imaging trials

et al. 2021

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Virtual imaging trials (VITs), defined as the process of conducting clinical imaging trials using computer simulations, offer a time- and cost-effective alternative to traditional imaging trials for CT. The clinical potential of VITs hinges on the realism of simulations modeling the image acquisition process, where the accurate scanner-specific simulation of scatter in a time-feasible manner poses a particular challenge. To meet this need, this study proposes, develops, and validates a rapid scatter estimation framework, based on GPU-accelerated Monte Carlo (MC) simulations and denoising methods, for estimating scatter in single source, dual-source, and photon-counting CT. A CT simulator was developed to incorporate parametric models for an anti-scatter grid and a curved energy integrating detector with an energy-dependent response. The scatter estimates from the simulator were validated using physical measurements acquired on a clinical CT system using the standard single-blocker method. The MC simulator was further extended to incorporate a pre-validated model for a PCD and an additional source-detector pair to model cross scatter in dual-source configurations. To estimate scatter with desirable levels of statistical noise using a manageable computational load, two denoising methods using a (1) convolutional neural network and an (2) optimized Gaussian filter were further deployed. The viability of this framework for clinical VITs was assessed by integrating it with a scanner-specific ray-tracer program to simulate images for an image quality (Mercury) and an anthropomorphic phantom (XCAT). The simulated scatter-to-primary ratios agreed with physical measurements within 4.4% ± 10.8% across all projection angles and kVs. The differences of ∼121 HU between images with and without scatter, signifying the importance of scatter for simulating clinical images. The denoising methods preserved the magnitudes and trends observed in the reference scatter distributions, with an averaged rRMSE value of 0.91 and 0.97 for the two methods, respectively. The execution time of ∼30 s for simulating scatter in a single projection with a desirable level of statistical noise indicates a major improvement in performance, making our tool an eligible candidate for conducting extensive VITs spanning multiple patients and scan protocols.

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“…The Monte Carlo simulation (MCS), a sophisticated sampling method, can be used in program design and has been widely adopted in various fields of research including engineering [ 20 , 21 , 22 , 23 ]. When the sampling space is large enough, the MCS can achieve an acceptable level of accuracy in numerical results.…”

Section: Introductionmentioning

confidence: 99%

Buckling Analysis of Vacancy-Defected Graphene Sheets by the Stochastic Finite Element Method

2018

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Vacancy defects are unavoidable in graphene sheets, and the random distribution of vacancy defects has a significant influence on the mechanical properties of graphene. This leads to a crucial issue in the research on nanomaterials. Previous methods, including the molecular dynamics theory and the continuous medium mechanics, have limitations in solving this problem. In this study, the Monte Carlo-based finite element method, one of the stochastic finite element methods, is proposed and simulated to analyze the buckling behavior of vacancy-defected graphene. The critical buckling stress of vacancy-defected graphene sheets deviated within a certain range. The histogram and regression graphs of the probability density distribution are also presented. Strengthening effects on the mechanical properties by vacancy defects were detected. For high-order buckling modes, the regularity and geometrical symmetry in the displacement of graphene were damaged because of a large amount of randomly dispersed vacancy defects.

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The adaptation method in the Monte Carlo simulation for computed tomography

Cited by 4 publications

References 7 publications

Monte Carlo-Based Finite Element Method for the Study of Randomly Distributed Vacancy Defects in Graphene Sheets

Monte Carlo-Based Finite Element Method for the Study of Randomly Distributed Vacancy Defects in Graphene Sheets

A GPU-accelerated framework for rapid estimation of scanner-specific scatter in CT for virtual imaging trials

Buckling Analysis of Vacancy-Defected Graphene Sheets by the Stochastic Finite Element Method

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