Accurate separation of two radionuclidesin dual isotope SPECT scan for small animal imaging
Multi-isotope SPECT study yields an optimization of therapeutic strategy and accurate evaluation of therapeutic eﬀect. However, it is diﬃcult to estimate primary photons in a multi-isotope SPECT system due to Compton scattered photons. The aim of this study is to estimate three primary counts of 99m Tc (photopeak energy 141keV) and 111 In (171, 245keV)in a simultaneous SPECT data aquisition. The target system is Nano SPECT/CT system and it has four dtectors. Each detector has nine pinholes, thus the system acquires 36 projection data simultaneously. In this paper, an accurate measurement method of 99m Tc and 111 In using an artiﬁcial neural network was proposed. In the acquisition of SPECTdata, we used four energy windows: one for the 99m Tc photopeak (141keV), two for 111 Inphotopeaks (171, 245keV) and the last one for scattered photons. The count data of these four energy windows were input to a feed forward neural network with these layers. Using the output values we estimated two primary counts of 99m Tc and 111 In. The learning of the neural network was performed with the calculated phantom data using an Monte Carlo method.The accuracy of the proposed method was cnﬁrmed with the simulations. The simulationsetting was assumed with the actual data acquisition of Nano SPECT/CT system.