@inproceedings{ba4d10e5538147379fca52b13e14aa8c,
title = "GPU programming for biomedical imaging",
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.",
keywords = "CUDA, GPU, medical imaging., parallel computing",
author = "Luca Caucci and Furenlid, {Lars R.}",
note = "Publisher Copyright: {\textcopyright} 2015 SPIE.; Medical Applications of Radiation Detectors V ; Conference date: 12-08-2015 Through 13-08-2015",
year = "2015",
doi = "10.1117/12.2195217",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Furenlid, {Lars R.} and Barber, {H. Bradford} and Roehrig, {Hans N.}",
booktitle = "Medical Applications of Radiation Detectors V",
}