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

Robert Jacques, Russell Taylor, John Wong, Todd McNutt

Research output: Contribution to journalArticlepeer-review

46 Scopus citations


We demonstrate the use of highly parallel graphics processing units (GPUs) to accelerate the superposition/convolution (S/C) algorithm to interactive rates while reducing the number of approximations. S/C first transports the incident fluence to compute the total energy released per unit mass (TERMA) grid. Dose is then calculated by superimposing the dose deposition kernel at each point in the TERMA grid and summing the contributions to the surrounding voxels. The TERMA algorithm was enhanced with physically correct multi-spectral attenuation and a novel inverse formulation for increased performance, accuracy and simplicity. Dose deposition utilized a tilted poly-energetic inverse cumulative-cumulative kernel, with the novel option of using volumetric mip-maps to approximate solid angle ray casting. Exact radiological path ray casting decreased discretization errors. We achieved a speedup of 34. x-98. x over a highly optimized CPU implementation.

Original languageEnglish (US)
Pages (from-to)285-292
Number of pages8
JournalComputer Methods and Programs in Biomedicine
Issue number3
StatePublished - Jun 2010


  • Adaptive radiotherapy
  • Convolution/superposition
  • Graphics processing unit (GPU)
  • Inverse planning
  • Radiation therapy planning

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Health Informatics


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