TY  - JOUR
AU  - Aguiar, P.
AU  - Rafecas, M.
AU  - Ortuño, J. E.
AU  - Kontaxakis, G.
AU  - Santos, A.
AU  - Pavia, J.
AU  - Rosetti, M.
PY  - 2010
DA  - 2010//
TI  - Geometrical and Monte Carlo projectors in 3D PET reconstruction
T2  - Med. Phys.
JO  - Medical Physics
SP  - 5691
EP  - 5702
VL  - 37
IS  - 11
PB  - Amer Assoc Physicists Medicine Amer Inst Physics
KW  - 3D PET
KW  - iterative reconstruction
KW  - list-mode reconstruction
KW  - ray-tracing techniques
KW  - Monte Carlo simulation
KW  - system response matrix
AB  - 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.
SN  - 0094-2405
UR  - https://doi.org/10.1118/1.3501884
DO  - 10.1118/1.3501884
LA  - English
N1  - ISI:000283747600015
ID  - Aguiar_etal2010
ER  -