Biomedical subjects

E B Bartlett

Publications and source records attributed to E B Bartlett.

3 recordsLinked to original sources

PubMed1995

Neural network reconstruction of single-photon emission computed tomography images.

An artificial neural network (ANN) trained on high-quality medical tomograms or phantom images may be able to learn the planar data-to-tomographic image relationship with very high precision. As a result, a properly trained ANN can produce comparably accurate image reconstruction without the high computational cost inherent in some traditional reconstruction techniques. We have previously 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, we present a method of deriving activation functions for a backpropagation ANN that make it readily trainable for full SPECT image reconstruction. The activation functions used for this work are based on the estimated probability density functions (PDFs) of the ANN training set data. The statistically tailored ANN and the standard sigmoidal backpropagation ANN methods are compared both in terms of their trainability and generalization ability. The results presented show that a statistically tailored ANN can reconstruct novel tomographic images of a quality comparable with that of the images used to train the network. Ultimately, an adequately trained ANN should be able to properly compensate for physical photon transport effects, background noise, and artifacts while reconstructing the tomographic image.

Algorithms↗

PubMed1995

Medical image processing utilizing neural networks trained on a massively parallel computer.

While finding many applications in science, engineering, and medicine, artificial neural networks (ANNs) have typically been limited to small architectures. In this paper, we demonstrate how very large architecture neural networks can be trained for medical image processing utilizing a massively parallel, single-instruction multiple data (SIMD) computer. The two- to three-orders of magnitude improvement in processing time attainable using a parallel computer makes it practical to train very large architecture ANNs. As an example we have trained several ANNs to demonstrate the tomographic reconstruction of 64 x 64 single photon emission computed tomography (SPECT) images from 64 planar views of the images. The potential for these large architecture ANNs lies in the fact that once the neural network is properly trained on the parallel computer the corresponding interconnection weight file can be loaded on a serial computer. Subsequently, relatively fast processing of all novel images can be performed on a PC or workstation.

Computer Systems↗

PubMed1995

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.

Heart↗