An artificial neural network approach to quantitative single photon emission computed tomographic reconstruction with collimator, attenuation, and scatter compensation.

A spatially variant technique for quantitative single photon emission computed tomographic (SPECT) image reconstruction using an artificial neural network (ANN) is presented. This network was developed to simultaneously compensate for collimator, attenuation, and scatter effects during the reconstruction process. The network was trained using a supervised scheme which implemented the generalized delta rule. Training ended once the mean-squared error (MSE) between the ideal and reconstructed images converged to a minimum. After training, the ANN weights were held constant and could be used to reconstruct source distributions other than those used while training. In the absence of noise when only collimator effects were present, reconstruction of a Hoffman brain phantom had a 89% reduction in MSE compared to standard filtered backprojection. When collimator-and-attenuation and collimator-attenuation-and-scatter trials were tested against filtered backprojection with Chang attenuation compensation, the corresponding ANN reconstructions demonstrated 85% and 86% decreases in MSE, respectively. With noise present, and with standard noise reduction filters implemented prior to reconstruction, the ANN reconstructions displayed up to a 50% decrease in MSE compared to filtered backprojection reconstructions for 200,000 count data. These results demonstrate that an ANN can be used to reconstruct SPECT images with improved quantitative accuracy.