Validation and evaluation of model-based crosstalk compensation method in simultaneous 99mTc stress and 201Tl rest myocardial perfusion SPECT
ABSTRACT Simultaneous acquisition of 99mTc stress and 201Tl rest myocardial perfusion SPECT has several potential advantages, but the image quality is degraded by crosstalk between the Tc and Tl data. We have previously developed a crosstalk model that includes estimates of the downscatter and Pb X-ray for use in crosstalk compensation. In this work, we validated the model by comparing the crosstalk from 99mTc to the Tl window calculated using a combination of the SimSET-MCNP Monte Carlo simulation codes. We also evaluated the model-based crosstalk compensation method using both simulated data from the 3-D MCAT phantom and experimental data from a physical phantom with a myocardial defect. In these studies, the Tl distributions were reconstructed from crosstalk contaminated data without crosstalk compensation, with compensation using the model-based crosstalk estimate, and with compensation using the known true crosstalk, and were compared with the Tl distribution reconstructed from uncontaminated Tl data. Results show that the model gave good estimates of both the downscatter photons and Pb X-rays in the simultaneous dual-isotopes myocardial perfusion SPECT. The model-based compensation method provided image quality that was significantly improved as compared to no compensation and was very close to that from the separate acquisition.
Conference Proceeding: Correction for scatter and cross-talk contaminations in dual radionuclide 99mTc and 123I images using artificial neural network[show abstract] [hide abstract]
ABSTRACT: Artificial neural network (ANN) is shown to be an effective tool in separating scatter and cross-talk from the primary photons in simultaneous dual radionuclide imaging. Generally, a large number of input energy windows are required within the network structure whilst the commercial cameras have only 3-8 energy windows. It is difficult to use two input windows within the ANN structure for the contamination corrections of <sup>99m</sup>Tc/<sup>123</sup>I images acquired using only two photo-peak energy windows. In this work, we designed a new ANN network with 24 inputs, and 32 nodes in the hidden layer and two nodes in the output layer, to correct for scatter and cross-talk contaminations on <sup>99m</sup>Tc/<sup>123</sup>I images acquired using two photo-peak windows. We trained the network using experimentally acquired <sup>99m</sup>Tc and <sup>123</sup>I spectrum data using 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 cross-talk corrections. Two sets of image data were tested: one was a human activation images and the other was 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 <sup>99m</sup>Tc/<sup>123</sup>I imaging data and apply it to other radionuclide combinations such as <sup>201</sup>Tl/<sup>99m</sup>Tc.Nuclear Science Symposium Conference Record, 2003 IEEE; 11/2003