Development of an accurate separation of two radionuclides with an artificial neural network in dual radioisotope SPECT data acquisition

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This paper presents a new method for estimating primary photons with an artificial neural network in a dual isotope SPECT study. The target isotopes are I-123 and Tc-99 m which are used for myocardial imaging (Tc-99 m MIBI and I-123 BMIPP). These two radionuclides have close photopeak energies. To estimate the primary photons we used a neural network which had three layers : one input layer with ten units, one hidden layer with twenty units and one output layer with two units. As input values to the input units, we used count ratios which were the ratios of the counts acquired with narrow energy windows (6 keV) to the total count acquired with a broad window in an energy range from 120 to 180 keV. The outputs were a primary count ratio of I-123, and a primary count ratio of Tc-99 m and I-123. With these primary count ratios and the total count we calculated the primary count of the pixel directly. The neural network was trained true energy spectra calculated by a Monte Carlo simulation. The simulation showed that an accurate estimation of primary photons was accomplished.