27 August 2015 GPU programming for biomedical imaging
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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.
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Luca Caucci, Luca Caucci, Lars R. Furenlid, Lars R. Furenlid, } "GPU programming for biomedical imaging", Proc. SPIE 9594, Medical Applications of Radiation Detectors V, 95940G (27 August 2015); doi: 10.1117/12.2195217; https://doi.org/10.1117/12.2195217

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