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Related Concept Videos

Radiation: Applications01:17

Radiation: Applications

The average temperature of Earth is the subject of much current discussion. Earth is in radiative contact with both the Sun and dark space; it receives almost all its energy from the radiation of the Sun and reflects some of it into outer space. Dark space is very cold, about 3 K, so Earth radiates energy into it. For instance, heat transfer occurs from soil and grasses, the rate of which can be so rapid that frost can occur on clear summer evenings, even in warm latitudes.
The average...

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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

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

Yong Hum Na1, Tae-Suk Suh, Daniel S Kapp

  • 1Department of Radiation Oncology, Stanford University, Stanford, CA 94305 USA. yhna@stanford.edu

Physics in Medicine and Biology
|September 5, 2013
PubMed
Summary

This study introduces a cloud computing environment for faster radiation treatment planning, significantly improving intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) optimization. The new system achieves up to 14-fold speed-ups, enabling real-time planning and adaptive re-planning.

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Computational Science

Background:

  • Intensity Modulated Radiation Therapy (IMRT) and Volumetric Modulated Arc Therapy (VMAT) offer dosimetric advantages but require efficient computational methods.
  • Clinically relevant organ-specific constraints, Monte Carlo (MC) dose calculations, and large-scale plan optimization are essential for advanced treatment planning.

Purpose of the Study:

  • To develop and evaluate a web-based, real-time radiation treatment planning system utilizing a cloud computing environment (CCE).
  • To integrate MC dose calculations and large-scale plan optimization within a scalable cloud infrastructure for IMRT and VMAT.

Main Methods:

  • Implementation of a CCE using Amazon Elastic Compute Cloud (EC2) for dose calculation and plan optimization.
  • Utilizing MC dose calculations for beamlet dose kernels and total-variation regularization (TVR) for intensity modulation optimization.
  • Iterative rectification of optimized fluence maps into deliverable apertures compatible with Varian TrueBeam STx linear accelerators.

Main Results:

  • Achieved speed-ups of up to 14-fold for dose kernel calculations and plan optimizations across head and neck, lung, and prostate cancer cases.
  • Cloud-based plans were found to be identical to traditional PC-based IMRT and VMAT plans, confirming system reliability.
  • Demonstrated the feasibility of a CCE for parallel and distributed computing in radiation treatment planning.

Conclusions:

  • The developed CCE substantially improves the speed of inverse planning for IMRT and VMAT.
  • The cloud computing infrastructure enables real-time radiation treatment planning and facilitates future on-treatment adaptive re-planning.
  • This approach enhances the efficiency and potential of advanced radiation therapy techniques.