PMID: 11339747May 8, 2001Paper

Artificial neural network Radon inversion for image reconstruction

Medical Physics
A F RodriguezK L Leenders

Abstract

Image reconstruction techniques are essential to computer tomography. Algorithms such as filtered backprojection (FBP) or algebraic techniques are most frequently used. This paper presents an attempt to apply a feed-forward back-propagation supervised artificial neural network (BPN) to tomographic image reconstruction, specifically to positron emission tomography (PET). The main result is that the network trained with Gaussian test images proved to be successful at reconstructing images from projection sets derived from arbitrary objects. Additional results relate to the design of the network and the full width at half maximum (FWHM) of the Gaussians in the training sets. First, the optimal number of nodes in the middle layer is about an order of magnitude less than the number of input or output nodes. Second, the number of iterations required to achieve a required training set tolerance appeared to decrease exponentially with the number of nodes in the middle layer. Finally, for training sets containing Gaussians of a single width, the optimal accuracy of reconstructing the control set is obtained with a FWHM of three pixels. Intended to explore feasibility, the BPN presented in the following does not provide reconstruction ac...Continue Reading

References

Nov 1, 1978·Physics in Medicine and Biology·S R Deans
Jan 1, 1991·IEEE Transactions on Medical Imaging·C R Floyd
Jan 1, 1982·IEEE Transactions on Medical Imaging·L A Shepp, Y Vardi
Jan 1, 1995·IEEE Transactions on Neural Networks·C Comtat, C Morel
Jan 1, 1992·IEEE Transactions on Neural Networks·S G Kong, B Kosko

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Citations

Oct 11, 2013·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Daniël Maria Pelt, Kees Joost Batenburg

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