A vectorized Monte Carlo code for radiotherapy treatment planning dose calculation

Weng, X.; Yan, Yulong; Hu, Shu; Wang, Jia-Wang; Jiang, Steve B; Luo, Limin

doi:10.1088/0031-9155/48/7/401

Cited by 10 publications

(8 citation statements)

References 16 publications

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“…Simulations were carried out with nanoparticles ranging in diameter from 2 to 40 nm. To give accurate statistics, the number of random histories required 1 × 10 8 events using a condensed random walk method of particle transport with kerma approximation for all cases. , The energy physics processes are capable of simulating secondary events to energies from low energy (keV) to high energy (MeV) because the production threshold of secondary events is less than the 1 keV cutoff of the standard electromagnetic physics list. For CT attenuation simulations, photon fluence was calculated by FLURZnrc extension code, simulated, and followed by incident X-ray beams with a monoenergetic energy of 120 keV mainly from the point source off axis.…”

Section: Experimental Sectionmentioning

confidence: 99%

Size-Tuning Ionization To Optimize Gold Nanoparticles for Simultaneous Enhanced CT Imaging and Radiotherapy

et al. 2016

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Computed tomography (CT) contrast and radiosensitization usually increase with particle sizes of gold nanoparticles (AuNPs), but there is a huge challenge to improve both by adjusting sizes under the requirements of in vivo application. Here, we report that AuNPs have great size-dependent enhancements on CT imaging as well as radiotherapy (RT) in the size range of 3-50 nm. It is demonstrated that AuNPs with a size of ∼13 nm could simultaneously possess superior CT contrast ability and significant radioactive disruption. The Monte Carlo method is further used to evaluate this phenomenon and indicates that the inhomogeneity of gold atom distributions caused by sizes may influence secondary ionization in whole X-ray interactions. In vivo studies further indicate that this optimally sized AuNP improves real-time CT imaging and radiotherapeutic inhibition of tumors in living mice by effective accumulation at tumors with prolonged in vivo circulation times compared to clinically used small-molecule agents. These results suggest that ∼13 nm AuNPs may serve as multifunctional adjuvants for clinical X-ray theranostic application.

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Section: Experimental Sectionmentioning

confidence: 99%

Size-Tuning Ionization To Optimize Gold Nanoparticles for Simultaneous Enhanced CT Imaging and Radiotherapy

et al. 2016

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“…Some researchers have devoted efforts to further accelerate the code. Thus, for instance, Tyagy and coworkers [17] used the Message Passing Interface (MPI) library to parallelize the algorithm, Weng et al [18] aimed at vectorizing the code and Jia et al [19] adapted it to the graphics processing unit (GPU) architecture.…”

Section: Methodsmentioning

confidence: 99%

DPM as a radiation transport engine for PRIMO

et al. 2018

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BackgroundPRIMO is a dose verification system based on the general-purpose Monte Carlo radiation transport code penelope, which implements an accurate physics model of the interaction cross sections and the radiation transport process but with low computational efficiency as compared with fast Monte Carlo codes. One of these fast Monte Carlo codes is the Dose Planning Method (DPM). The purpose of this work is to describe the adaptation of DPM as an alternative PRIMO computation engine, to validate its performance against penelope and to validate it for some specific cases.MethodsDPM was parallelized and modified to perform radiation transport in quadric geometries, which are used to describe linacs, thus allowing the simulation of dynamic treatments. To benchmark the new code versus penelope, both in terms of accuracy of results and simulation time, several tests were performed, namely, irradiation of a multi-layer phantom, irradiation of a water phantom using a collimating pattern defined by the multileaf collimator (MLC), and four clinical cases. The gamma index, with passing criteria of 1 mm/1%, was used to compare the absorbed dose distributions. Clinical cases were compared using a 3-D gamma analysis.ResultsThe percentage of voxels passing the gamma criteria always exceeded 99% for the phantom cases, with the exception of the transport through air, for which dose differences between DPM and penelope were as large as 24%. The corresponding percentage for the clinical cases was larger than 99%. The speedup factor between DPM and penelope ranged from 2.5 ×, for the simulation of the radiation transport through a MLC and the subsequent dose estimation in a water phantom, up to 11.8 × for a lung treatment. A further increase of the computational speed, up to 25 ×, can be obtained in the clinical cases when a voxel size of (2.5 mm)3 is used.ConclusionsDPM has been incorporated as an efficient and accurate Monte Carlo engine for dose estimation in PRIMO. It allows the concatenated simulation of the patient-dependent part of the linac and the patient geometry in static and dynamic treatments. The discrepancy observed between DPM and penelope, which is due to an artifact of the cross section interpolation algorithm for low energy electrons in air, does not affect the results in other materials.

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“…In the field of treatment planning, there are only a few publications that investigate the full performance of a single CPU. Weng et al (2003) investigated a vectorized implementation of an MC code for radiotherapy treatment planning dose calculation. He could achieve a speedup of 1.5 using an early version of the SSE extension on CPUs compared to using the conventional float point unit.…”

Section: Other Typical Architecturesmentioning

confidence: 99%

GPU-based high-performance computing for radiation therapy

2014

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Recent developments in radiotherapy therapy demand high computation powers to solve challenging problems in a timely fashion in a clinical environment. Graphics processing unit (GPU), as an emerging high-performance computing platform, has been introduced to radiotherapy. It is particularly attractive due to its high computational power, small size, and low cost for facility deployment and maintenance. Over the past a few years, GPU-based high-performance computing in radiotherapy has experienced rapid developments. A tremendous amount of studies have been conducted, in which large acceleration factors compared with the conventional CPU platform have been observed. In this article, we will first give a brief introduction to the GPU hardware structure and programming model. We will then review the current applications of GPU in major imaging-related and therapy-related problems encountered in radiotherapy. A comparison of GPU with other platforms will also be presented.

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A vectorized Monte Carlo code for radiotherapy treatment planning dose calculation

Cited by 10 publications

References 16 publications

Size-Tuning Ionization To Optimize Gold Nanoparticles for Simultaneous Enhanced CT Imaging and Radiotherapy

Size-Tuning Ionization To Optimize Gold Nanoparticles for Simultaneous Enhanced CT Imaging and Radiotherapy

DPM as a radiation transport engine for PRIMO

GPU-based high-performance computing for radiation therapy

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