SU‐GG‐T‐376: Medical Physics Calculations in the Cloud: A New Paradigm for Clinical Computing

Keyes, R; Romano, C; Arnold, Dorian; Luan, Shuang

doi:10.1118/1.3468773

Cited by 3 publications

(2 citation statements)

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“…Cloud computing (Armbrust et al , 2010) has the advantage that computational resources are provided “on-demand”. Keyes et al (2010) first ported dose calculations into a cloud environment and the computation time was explained by a theoretical model, demonstrating the efficiency improvement using multiple nodes. Poole et al (2012) found that the computation time decreases approximately with 1/ n , where n was the number of parallel machines used.…”

Section: Gpu Vs Other Architecturesmentioning

confidence: 99%

“…However, the data communication overhead still remains. The available work (Keyes et al , 2010; Pratx and Xing, 2011b; Poole et al , 2012) showed that cloud computing is feasible for a MC dose calculation. This was possible, because the MC dose calculation is an embarrassingly parallel computing problem, in which inter-CPU data communication is very minimal.…”

Section: Gpu Vs Other Architecturesmentioning

confidence: 99%

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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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Section: Gpu Vs Other Architecturesmentioning

confidence: 99%

Section: Gpu Vs Other Architecturesmentioning

confidence: 99%

GPU-based high-performance computing for radiation therapy

2014

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GATE Monte Carlo simulation of dose distribution using MapReduce in a cloud computing environment

Liu

Tang

Gao

2017

Australas Phys Eng Sci Med

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The GATE Monte Carlo simulation platform has good application prospects of treatment planning and quality assurance. However, accurate dose calculation using GATE is time consuming. The purpose of this study is to implement a novel cloud computing method for accurate GATE Monte Carlo simulation of dose distribution using MapReduce. An Amazon Machine Image installed with Hadoop and GATE is created to set up Hadoop clusters on Amazon Elastic Compute Cloud (EC2). Macros, the input files for GATE, are split into a number of self-contained sub-macros. Through Hadoop Streaming, the sub-macros are executed by GATE in Map tasks and the sub-results are aggregated into final outputs in Reduce tasks. As an evaluation, GATE simulations were performed in a cubical water phantom for X-ray photons of 6 and 18 MeV. The parallel simulation on the cloud computing platform is as accurate as the single-threaded simulation on a local server and the simulation correctness is not affected by the failure of some worker nodes. The cloud-based simulation time is approximately inversely proportional to the number of worker nodes. For the simulation of 10 million photons on a cluster with 64 worker nodes, time decreases of 41× and 32× were achieved compared to the single worker node case and the single-threaded case, respectively. The test of Hadoop's fault tolerance showed that the simulation correctness was not affected by the failure of some worker nodes. The results verify that the proposed method provides a feasible cloud computing solution for GATE.

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Toward a web-based real-time radiation treatment planning system in a cloud computing environment

Na¹,

Suh²,

Kapp³

et al. 2013

Phys. Med. Biol.

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To exploit the potential dosimetric advantages of intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT), an in-depth approach is required to provide efficient computing methods. This needs to incorporate clinically related organ specific constraints, Monte Carlo (MC) dose calculations, and large-scale plan optimization. This paper describes our first steps toward a web-based real-time radiation treatment planning system in a cloud computing environment (CCE). The Amazon Elastic Compute Cloud (EC2) with a master node (named m2.xlarge containing 17.1 GB of memory, two virtual cores with 3.25 EC2 Compute Units each, 420 GB of instance storage, 64-bit platform) is used as the backbone of cloud computing for dose calculation and plan optimization. The master node is able to scale the workers on an 'on-demand' basis. MC dose calculation is employed to generate accurate beamlet dose kernels by parallel tasks. The intensity modulation optimization uses total-variation regularization (TVR) and generates piecewise constant fluence maps for each initial beam direction in a distributed manner over the CCE. The optimized fluence maps are segmented into deliverable apertures. The shape of each aperture is iteratively rectified to be a sequence of arcs using the manufacture's constraints. The output plan file from the EC2 is sent to the simple storage service. Three de-identified clinical cancer treatment plans have been studied for evaluating the performance of the new planning platform with 6 MV flattening filter free beams (40 × 40 cm(2)) from the Varian TrueBeam(TM) STx linear accelerator. A CCE leads to speed-ups of up to 14-fold for both dose kernel calculations and plan optimizations in the head and neck, lung, and prostate cancer cases considered in this study. The proposed system relies on a CCE that is able to provide an infrastructure for parallel and distributed computing. The resultant plans from the cloud computing are identical to PC-based IMRT and VMAT plans, confirming the reliability of the cloud computing platform. This cloud computing infrastructure has been established for a radiation treatment planning. It substantially improves the speed of inverse planning and makes future on-treatment adaptive re-planning possible.

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SU‐GG‐T‐376: Medical Physics Calculations in the Cloud: A New Paradigm for Clinical Computing

Cited by 3 publications

References 0 publications

GPU-based high-performance computing for radiation therapy

GPU-based high-performance computing for radiation therapy

GATE Monte Carlo simulation of dose distribution using MapReduce in a cloud computing environment

Toward a web-based real-time radiation treatment planning system in a cloud computing environment

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