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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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Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies
08:34

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

Published on: February 6, 2019

Towards real-time radiation therapy: GPU accelerated superposition/convolution.

Robert Jacques1, Russell Taylor, John Wong

  • 1School of Medicine, Johns Hopkins University, Baltimore, MD 21231-2410, USA. sandford@jhu.edu

Computer Methods and Programs in Biomedicine
|August 22, 2009
PubMed
Summary
This summary is machine-generated.

Highly parallel graphics processing units (GPUs) accelerate the superposition/convolution (S/C) algorithm for faster, more accurate dose calculations in medical physics. This GPU acceleration achieved significant speedups over traditional CPU methods.

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Area of Science:

  • Medical Physics
  • Computational Science
  • Radiotherapy Physics

Background:

  • The superposition/convolution (S/C) algorithm is crucial for calculating dose distributions in radiotherapy.
  • Existing CPU implementations are computationally intensive, limiting interactive use and requiring approximations.
  • Accelerating dose calculation is essential for improving treatment planning efficiency and accuracy.

Purpose of the Study:

  • To accelerate the S/C algorithm using graphics processing units (GPUs) to achieve interactive rates.
  • To reduce the number of approximations made in dose calculation.
  • To enhance the accuracy and performance of the TERMA and dose deposition steps.

Main Methods:

  • Implemented the S/C algorithm on highly parallel GPUs.
  • Enhanced the TERMA algorithm with multi-spectral attenuation and an inverse formulation.
  • Utilized a tilted poly-energetic inverse cumulative-cumulative kernel for dose deposition.
  • Incorporated volumetric mip-maps and exact radiological path ray casting for improved accuracy and performance.

Main Results:

  • Achieved interactive rates for the S/C algorithm.
  • Demonstrated a speedup of 34x-98x compared to a highly optimized CPU implementation.
  • Reduced discretization errors through exact radiological path ray casting.
  • Enhanced physical correctness with multi-spectral attenuation.

Conclusions:

  • GPU acceleration significantly enhances the speed and efficiency of the S/C algorithm for dose calculation.
  • The enhanced methods improve accuracy and reduce approximations in radiotherapy dose planning.
  • This approach enables faster and more precise radiotherapy treatment planning.