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Maximum likelihood event estimation and list-mode image reconstruction on GPU hardware

Luca Caucci, Lars R. Furenlid, Harrison H. Barrett
College of Optical Sciences, University of Arizona, 1630 E. University Blvd., Tucson, Arizona 85721, USA
IEEE Nuclear Science Symposium Conference Record (NSS/MIC), 2009

@inproceedings{caucci2009maximum,

   title={Maximum likelihood event estimation and list-mode image reconstruction on GPU hardware},

   author={Caucci, L. and Furenlid, L.R. and Barrett, H.H.},

   booktitle={Nuclear Science Symposium Conference Record (NSS/MIC), 2009 IEEE},

   pages={4072–4076},

   year={2009},

   organization={IEEE}

}

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The scintillation detectors commonly used in SPECT and PET imaging and in Compton cameras require estimation of the position and energy of each gamma ray interaction. Ideally, this process would yield images with no spatial distortion and the best possible spatial resolution. In addition, especially for Compton cameras, the computation must yield the best possible estimate of the energy of each interacting gamma ray. These goals can be achieved by use of maximum-likelihood (ML) estimation of the event parameters, but in the past the search for an ML estimate has not been computationally feasible. Now, however, graphics processing units (GPUs) make it possible to produce optimal, real-time estimates of position and energy, even from scintillation cameras with a large number of photodetectors. In addition, the mathematical properties of ML estimates make them very attractive for use as list entries in list-mode ML image reconstruction. This two-step ML process – using ML estimation once to get the list data and again to reconstruct the object – allows accurate modeling of the detector blur and, potentially, considerable improvement in reconstructed spatial resolution.
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