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arXiv · 1311.7251

Spatially-Adaptive Reconstruction in Computed Tomography using Neural Networks

Joseph Shtok·Michael Zibulevsky·Michael Elad
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Abstract

We propose a supervised machine learning approach for boosting existing signal and image recovery methods and demonstrate its efficacy on example of image reconstruction in computed tomography. Our technique is based on a local nonlinear fusion of several image estimates, all obtained by applying a chosen reconstruction algorithm with different values of its control parameters. Usually such output images have different bias/variance trade-off. The fusion of the images is performed by feed-forward neural network trained on a set of known examples. Numerical experiments show an improvement in reconstruction quality relatively to existing direct and iterative reconstruction methods.

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Joseph Shtok, Michael Zibulevsky, Michael Elad. 2013-11-28. Spatially-Adaptive Reconstruction in Computed Tomography using Neural Networks. https://arxiv.org/abs/1311.7251

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