PET Image Reconstruction Using ANN

The aim of this study is to improve the positron emission tomography (PET) image quality for medical diagnosis. The statistical reconstructions on the maximum a posteriori (MAP) algorithm often results in a blurring effect, which fails to determine the toughness class in the reconstructed image. The development of new reconstruction algorithms for PET is an active field of research. In this article, artificial neural network (ANN) is proposed for replicating the output image, which is generated from the acquired projection data with the corresponding angles using the PET images. This article proposes the advantage of arranging the neural network to stock up the information of the continuous capacity. This reduces the storage space and recuperates as much sequence of the continuous quantity as possible. The performance of image quality parameters using ANN is better when compared with MAP, FBP-NN (filtered back projection with nearest neighbor interpolation). Thus ANN provides 63% better peak signal to noise ratio (PSNR) when compared with FBP-NN and 47% better when compared to MAP. Thus, ANN is better than FBP and MAP algorithm, by providing better PSNR. (C) 2014 Wiley Periodicals, Inc.