4 March 2010 A local and iterative neural reconstruction algorithm for cone-beam data
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Abstract
This work presents a new neural algorithm designed for the reconstruction of tomographic images from Cone Beam data. The main objective of this work is the search of a new reconstruction method, able to work locally, more robust in presence of noisy data and in situations with a small number of projections. This study should be intended as the first step to evaluate the potentialities of the proposed algorithm. The algorithm is iterative and based on a set of neural networks that are working locally and sequentially. All the x-rays passing through a cell of the volume to be reconstructed, give origin to a neural network which is a single-layer perceptron network. The network does not need a training set but uses the line integral of a single x-ray as ground-truth of each output neuron. The neural network uses a gradient descent algorithm in order to minimize a local cost function by varying the value of the cells to be reconstructed. The proposed strategy was first evaluated in conditions where the quality and quantity of input data varies widely, using a the Shepp-Logan Phantom. The algorithm was also compared with the iterative ART algorithm and the well known filtered backprojection 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. In situations with little noise the reconstruction, after a few iterations, is almost identical to the original.
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Ignazio Gallo, Ignazio Gallo, } "A local and iterative neural reconstruction algorithm for cone-beam data", Proc. SPIE 7622, Medical Imaging 2010: Physics of Medical Imaging, 762253 (4 March 2010); doi: 10.1117/12.843829; https://doi.org/10.1117/12.843829
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