Correction for crosstalk contaminations in dual radionuclide 99mTc and 123I images using artificial neural network

Use of an artificial neural network (ANN) has been previously shown to be an effective tool in compensating scatter and crosstalk from the primary photons in simultaneous dual radionuclide imaging. Generally, a large number of input energy windows are required within the network structure while the commercial cameras have only 3-8 energy windows. It is difficult to use two input windows within the ANN structure for the crosstalk contamination corrections of ^99mTc/¹²³I images acquired using only two photopeak energy windows. In this paper, we designed an ANN network with 24 inputs, 32 nodes in the hidden layer and two nodes in the output layer, to correct for crosstalk contamination in ^99mTc/¹²³I images acquired using two photopeak windows. We trained the network using experimentally acquired ^99mTc and ¹²³I spectrum data using the RSD brain phantom. The neural network package Stuttgart Neural Network Simulator (SNNSv4.2), from the University of Stuttgart, was used for the neural network training and the crosstalk corrections. Two sets of image data were tested. The first was a human activation study and the other used a cylindrical striatal phantom. Our results show a great improvement on both the human activation and the cylindrical striatal phantom images. Further work is to test our new approach on more ^99mTc/¹²³I imaging data and apply it to other radionuclide combinations such as ²⁰¹Tl/^99mTc.