@Article{Aguiar_etal2010,
author="Aguiar, P.
and Rafecas, M.
and Ortu{\~{n}}o, J. E.
and Kontaxakis, G.
and Santos, A.
and Pavia, J.
and Rosetti, M.",
title="Geometrical and Monte Carlo projectors in 3D PET reconstruction",
journal="Medical Physics",
year="2010",
publisher="Amer Assoc Physicists Medicine Amer Inst Physics",
volume="37",
number="11",
pages="5691--5702",
optkeywords="3D PET; iterative reconstruction; list-mode reconstruction; ray-tracing techniques; Monte Carlo simulation; system response matrix",
abstract="Purpose: In the present work, the authors compare geometrical and Monte Carlo projectors in detail. The geometrical projectors considered were the conventional geometrical Siddon ray-tracer (S-RT) and the orthogonal distance-based ray-tracer (OD-RT), based on computing the orthogonal distance from the center of image voxel to the line-of-response. A comparison of these geometrical projectors was performed using different point spread function (PSF) models. The Monte Carlo-based method under consideration involves an extensive model of the system response matrix based on Monte Carlo simulations and is computed off-line and stored on disk. Methods: Comparisons were performed using simulated and experimental data of the commercial small animal PET scanner rPET. Results: The results demonstrate that the orthogonal distance-based ray-tracer and Siddon ray-tracer using PSF image-space convolutions yield better images in terms of contrast and spatial resolution than those obtained after using the conventional method and the multiray-based S-RT. Furthermore, the Monte Carlo-based method yields slight improvements in terms of contrast and spatial resolution with respect to these geometrical projectors. Conclusions: The orthogonal distance-based ray-tracer and Siddon ray-tracer using PSF image-space convolutions represent satisfactory alternatives to factorizing the system matrix or to the conventional on-the-fly ray-tracing methods for list-mode reconstruction, where an extensive modeling based on Monte Carlo simulations is unfeasible.",
optnote="ISI:000283747600015",
optnote="exported from refbase (https://references.ific.uv.es/refbase/show.php?record=338), last updated on Tue, 13 Oct 2020 12:45:34 +0000",
issn="0094-2405",
doi="10.1118/1.3501884",
opturl="https://doi.org/10.1118/1.3501884",
language="English"
}