A statistically tailored neural network approach to tomographic image reconstruction

In previous work it has been shown that a standard backpropagation neural network can be trained to reconstruct sections of single photon emission computed tomography (SPECT) images based on the planar image projections as inputs. In this study, it is demonstrated that an artificial neural network (ANN) trained on a series of simulated SPECT images or trained on a set of rudimentary geometric images can learn the planar data-to-tomographic image relationship for 64 x 64 tomograms. As a result, a properly trained ANN can produce accurate, novel image reconstructions but without the high computational cost inherent in some traditional reconstruction techniques. We also present a method of deriving activation functions for a backpropagation ANN that make it readily trainable for cardiac SPECT image reconstruction. The activation functions are derived from the estimated probability density functions (p.d.f.s) of the ANN training set data. The performance of the statistically tailored ANNs are compared with the performance of standard sigmoidal back-propagation ANNs, both in terms of their trainability and generalization ability. The results presented demonstrate that statistically tailored ANNs are significantly better than standard sigmoidal ANNs at reconstructing novel tomographic images based on a simulated SPECT image training set or a rudimentary geometric image training set. Neural network based image reconstruction has two potential advantages over conventional reconstruction methods. The first advantage is that ANNs can rapidly reconstruction tomograms. Secondly, the quality of the reconstructions produced are directly correlated to the quality of the images used to train the ANN.