NDLI: Sensitivity in PET: Neural networks as an alternative to compton photons LOR analysis

Content Provider	IEEE Xplore Digital Library
Author	Michaud, J.-B. Brunet, C.-A. Rafecas, M. Lecomte, R. Fontaine, R.
Copyright Year	2007
Description	Author affiliation: Univ. de Sherbrooke, Quebec (Michaud, J.-B.; Brunet, C.-A.)
Abstract	In high-resolution small-animal positron emission tomography (PET), sensitivity remains an active issue. Sensitivity can be increased by lowering the energy threshold to include more Compton-scattered events, but then computation of the correct annihilation line-of-response (LOR) proves problematic. The complexity of Compton-kinematics analysis, compounded with finite energy resolution and detection position quantization of finite-size detectors, yields unaffordable methods with rather poor success rates. As an alternative, this paper proposes an artificial neural network (ANN) approach, which forfeits all explicit handling of equations at the expense of a priori statistical training, and which has the potential to better handle the previous measurement impairments. The method first consists in a preprocessing step involving geometrical transformations, which simplifies the actual use of the neural network, in the second step. This paper presents the method's proof-of-concept. It focuses on a simple yet prevalent inter-crystal scatter scenario, where a 511-keV annihilation photon is detected coincidently with two inter-crystal-scattered photons whose energy sum accounts for the whole 511 keV annihilation energy. It shows, in preliminary simulations, a promising correct LOR computation rate in the range from 90 to 94%. Finally, it discusses the steps and requirements for the eventual implementation of the method, including further validation, hardware requirements, system- level issues and possible other applications.
Starting Page	3594
Ending Page	3600
File Size	309176
Page Count	7
File Format	PDF
ISBN	9781424409228
ISSN	10957863
DOI	10.1109/NSSMIC.2007.4436902
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Positron emission tomography Neural networks Artificial neural networks Energy resolution Quantization Detectors Equations Electromagnetic scattering Particle scattering Computational modeling
Content Type	Text
Resource Type	Article

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