A Local and Iterative Neural Reconstruction Algorithm for Cone-Beam Data
Ignazio Gallo
In: SPIE Medical Imaging, February 2010, San Diego, California.

Abstract

This work presents a new neural algorithm designed for the reconstruction of tomographic images from Cone Beam data. The algorithm is iterative and based on a set of neural networks that are working locally and sequentially. The algorithm was compared with the iterative ART algorithm and the ltered backprojection (FBP) method. The results show how the proposed algorithm is much more accurate even in the presence of noise and under conditions of lack of data.

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