Award Date

8-1-2014

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Health Physics and Diagnostic Sciences

First Committee Member

Yu Kuang

Second Committee Member

Bing Ma

Third Committee Member

Gary Cerefice

Fourth Committee Member

Janet Dufek

Number of Pages

66

Abstract

The GEANT4-based GATE is a unique and powerful Monte Carlo (MC) platform, which provides a single code library allowing the simulation of specific medical physics applications, e.g. PET, SPECT, CT, radiotherapy, and hadron therapy. However, this rigorous yet flexible platform is used only sparingly in the clinic due to its lengthy calculation time. By accessing the powerful computational resources of a cloud computing environment, GATE's runtime can be significantly reduced to clinically feasible levels without the sizable investment of a local high performance cluster. This study investigated a reliable and efficient execution of GATE MC simulations using a commercial cloud computing services. Amazon's Elastic Compute Cloud was used to launch several nodes equipped with GATE. Job data was initially broken up on the local computer, then uploaded to the worker nodes on the cloud. The results were automatically downloaded and aggregated on the local computer for display and analysis. Five simulations were repeated for every cluster size between 1 and 20 nodes. Ultimately, increasing cluster size resulted in a decrease in calculation time that could be expressed with an inverse power model. Comparing the benchmark results to the published values and error margins indicated that the simulation results were not affected by the cluster size and thus that integrity of a calculation is preserved in a cloud computing environment. The runtime of a 53 minute long simulation was decreased to 3.11 minutes when run on a 20-node cluster. The ability to improve the speed of simulation suggests that fast MC simulations are viable for imaging and radiotherapy applications. With high power computing continuing to lower in price and accessibility, implementing Monte Carlo techniques with cloud computing for clinical applications will continue to become more attractive.

Keywords

Amazon; Amazon.com (Firm); Cloud computing; Cluster; Diagnostic imaging – Computer simulations; EC2; Monte Carlo method; Nodes; Radiotherapy; Radiotherapy – Computer simulations; Simulation

Disciplines

Computer Engineering | Computer Sciences | Digital Communications and Networking | Medical Sciences | Medicine and Health Sciences | Oncology | Physics

Language

English


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