  In this paper, we explore a novel method for tomographic image reconstruction
in the field of SPECT imaging. Deep Learning methodologies and more
specifically deep convolutional neural networks (CNN) are employed in the new
reconstruction method, which is referred to as "CNN Reconstruction - CNNR". For
training of the CNNR Projection data from software phantoms were used. For
evaluation of the efficacy of the CNNR method, both software and hardware
phantoms were used. The resulting tomographic images are compared to those
produced by filtered back projection (FBP) [1], the "Maximum Likelihood
Expectation Maximization" (MLEM) [1] and ordered subset expectation
maximization (OSEM) [2].
