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

Abstract

A new scatter correction method for Ga-67 based on artificial neural networks (ANN) with error backpropagation 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. Our 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-hour 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 interests in the lungs, abdomen and liver. ANN overall bias and precision in the abdomen were 5.8/spl infin/2.6% (93 keV), -0.1/spl plusmn/2.4% (185 keV) and -4.9/spl plusmn/1.8% (300 keV), and the bias in all structures was less than 19% as compared to 85% with WIN. ANN is an accurate and robust scatter correction method for Ga-67 studies.