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 language||English (US)|
|Title of host publication||Proceedings of SPIE - The International Society for Optical Engineering|
|State||Published - 2015|
|Event||Medical Applications of Radiation Detectors V - San Diego, United States|
Duration: Aug 12 2015 → Aug 13 2015
|Other||Medical Applications of Radiation Detectors V|
|Period||8/12/15 → 8/13/15|
- 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