NDLI: A new scatter compensation method for Ga-67 imaging using artificial neural networks

Content Provider	IEEE Xplore Digital Library
Author	El Fakhri, G. Moore, S.C. Maksud, P.
Copyright Year	1963
Abstract	A new scatter correction method for Ga-67 based on artificial neural networks (ANN) with error back-propagation was designed and evaluated. The ANN consisted of a 37-node input layer (37 energy channels in the range 60-370 keV), an 18-node hidden layer, and a 3-node output layer to estimate the scatter-free distribution in the 93-, 185-, and 300-keV photopeaks. Two separate activity and attenuation distribution sets, based on a segmented realistic anthropomorphic torso phantom, were simulated. The first set was used for ANN learning and the second to evaluate the scatter correction. The authors' Monte Carlo simulation modeled all photon interactions in the patient, collimator, and detector. Interactions simulated in the collimator included Compton and coherent scatter and photoelectric absorption with forced production of lead K-shell X-rays. Ninety very high count projections were simulated and used as a basis for generating 15 Poisson noise realizations for each angle; noise levels were characteristic of 72-h post-injection Ga-67 studies. The energy window images (WIN) used clinically were also generated for comparison. Bias and variance were computed with respect to the primary distributions over reconstructed volumes of interest in the lungs, abdomen, liver, and tumors. ANN overall bias in all structures was less than 16% (8% in the abdomen) as compared to 85% with WIN. The variance of the activity estimates was systematically greater with WIN than ANN. ANN is a promising approach to scatter correction in Ga-67 studies.
Sponsorship	IEEE Nuclear and Plasma Sciences Society Computer Applications in Nuclear and Plasma Sciences (CANPS) Lawrence Berkeley Lab. Lawrence Livermore Nat. Lab. APS College of William and Mary Continuous Electron Beam Accelerator Facility NASA Defence Nuclear Agency Sandia National Laboratories Jet Propulsion Laboratory Brookhaven Nat. Lab. Lawrence Livermore Nat. Lab IEEE/NPPS Radiat. Effects Committee Defence Nuclear Agency/DoD Sandia National Laboratories/DOE Jet Propulsion Laboratory/NASA Phillips Lab./DoD
Starting Page	799
Ending Page	804
Page Count	6
File Size	129789
File Format	PDF
ISSN	00189499
Volume Number	48
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-06-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Electromagnetic scattering Particle scattering X-ray scattering Noise level Abdomen Error correction Attenuation Anthropomorphism Torso
Content Type	Text
Resource Type	Article
Subject	Nuclear and High Energy Physics Electrical and Electronic Engineering Nuclear Energy and Engineering

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