A statistically tailored neural network approach to tomographic image reconstruction

Abstract

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.

Reconstrução de Imagens Tomográficas Geradas por Projeções Fan-Beam com Detetores Eqüidistantes usando Redes Neurais Parcialmente Conectadas

“…e os valores de s dados pela equação 4. 2 Considerando-se a geometria fan-beam e fazendo um paralelo com a equação 9, os pesos passam a ser dados por…”

“…Redes neurais já foram estudadas como alternativas de reconstrução de imagens de tomografia computadorizada [1,2,3], numa abordagem convencional de perceptron multicamada com treinamento backpropagation. Neste artigo apresentamos uma alternativa para obtenção de imagens tomográficas usando redes neurais parcialmente conectadas, sendo estas conexões determinadas pela geometria do problema e não necessitando de treinamento, conforme trabalho já desenvolvido anteriormente para projeções obtidas com raios paralelos [4,5].…”

Neural Network Image Reconstruction for Magnetic Particle Imaging

“…A neural network technique has recently been studied for image reconstruction in computerized tomography. The neural network maps the input data to the output data through a nonlinear activation function.…”

A New Approach to Image Reconstruction from Projections Using a Recurrent Neural Network

“…In the framework of soft computing, the most popular approach to image reconstruction from projections is based on neural networks-a very popular and important tool of artificial intelligence systems for solving image processing problems, e.g., as described in (Cierniak and Rutkowski, 2000). The idea of a neural network applied to image reconstruction from projections is presented in (Kerr and Barlett, 1995a;Kerr and Barlett, 1995b;Kerr and Barlett, 1995c;Knoll et al, 1999;Munllay et al, 1994). Unfortunately, the supervised learning of algorithms described in these papers cannot lead to good performance.…”