GPU programming for biomedical imaging

Luca Caucci, Lars R Furenlid

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Scientific computing is rapidly advancing due to the introduction of powerful new computing hardware, such as graphics processing units (GPUs). Affordable thanks to mass production, GPU processors enable the transition to efficient parallel computing by bringing the performance of a supercomputer to a workstation. We elaborate on some of the capabilities and benefits that GPU technology offers to the field of biomedical imaging. As practical examples, we consider a GPU algorithm for the estimation of position of interaction from photomultiplier (PMT) tube data, as well as a GPU implementation of the MLEM algorithm for iterative image reconstruction.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherSPIE
Volume9594
ISBN (Print)9781628417609
DOIs
StatePublished - 2015
EventMedical Applications of Radiation Detectors V - San Diego, United States
Duration: Aug 12 2015Aug 13 2015

Other

OtherMedical Applications of Radiation Detectors V
CountryUnited States
CitySan Diego
Period8/12/158/13/15

Keywords

  • CUDA
  • GPU
  • medical imaging.
  • parallel computing

ASJC Scopus subject areas

  • Applied Mathematics
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics

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  • Cite this

    Caucci, L., & Furenlid, L. R. (2015). GPU programming for biomedical imaging. In Proceedings of SPIE - The International Society for Optical Engineering (Vol. 9594). [95940G] SPIE. https://doi.org/10.1117/12.2195217