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Radiation Planning Assistant - A Web-based Tool to Support High-quality Radiotherapy in Clinics with Limited Resources

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Published on: October 6, 2023

Radiotherapy Monte Carlo simulation using cloud computing technology.

C M Poole¹, I Cornelius, J V Trapp

¹Cancer Care Services, Royal Brisbane and Womens Hospital, Herston, Australia. christopher.poole@qut.edu.au

Australasian Physical & Engineering Sciences in Medicine

|November 29, 2012

Summary

This summary is machine-generated.

Cloud computing offers rapid Monte Carlo radiotherapy dose calculations using GEANT4. Simulation time decreases with more machines, and cost is optimal when machine count divides total simulation hours.

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Area of Science:

Medical Physics
Computational Science
Radiotherapy

Background:

Cloud computing provides scalable computational power on demand.
Monte Carlo methods are crucial for accurate radiotherapy dose calculations.
GEANT4 is a toolkit for simulating particle transport through matter.

Purpose of the Study:

To present a technique for performing Monte Carlo radiotherapy dose calculations in the cloud using GEANT4.
To evaluate the impact of machine count on simulation cost and completion time.
To demonstrate the feasibility of cloud-based Monte Carlo simulations for radiotherapy.

Main Methods:

Utilized cloud computing infrastructure for GEANT4 simulations.
Performed Monte Carlo radiotherapy dose calculations.
Analyzed simulation completion time and relative cost as a function of parallel machine count (n).

Main Results:

Simulation completion time showed a decrease proportional to 1/n with n parallel machines.
Relative simulation cost was found to be optimal when n was a factor of the total simulation time in hours.
Successfully executed Monte Carlo simulations in the cloud.

Conclusions:

Cloud computing is a viable solution for rapid Monte Carlo simulations in radiotherapy.
This technique eliminates the need for dedicated local hardware for complex dose calculations.
The findings support the use of cloud resources for efficient radiotherapy planning.