A comparative study of reconstruction methods for neutron tomography
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Micieli, Davide
Carbone, Vincenzo
Gorini, Giuseppe
Tassi, Enrico
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Dottorato di ricerca in Scienze e Tecnologie Fisiche, Chimiche e dei Materiali, in convenzione con il CNR. XXXI Ciclo; Neutron tomography is a well established technique to non-destructively investigate the
inner structure of a wide range of objects. The main disadvantages of this technique are
the time-consuming data acquisition, which generally requires several hours, and the
low signal to noise ratio of the acquired images. One way for decreasing the total scan
time is to reduce the number of radiographs. However, the Filtered Back-Projection,
which is the most widely used reconstruction method in neutron tomography, generates
low quality images affected by artifacts when the number of projections is limited
or the signal to noise ratio of the radiographs is low.
This doctoral thesis is focused on the comparative analysis of different reconstruction
techniques, aimed at finding the data processing procedures suitable for neutron tomography
that shorten the scan time without reduction of the reconstructed image quality.
At first the performance of the algebraic reconstruction methods were tested using
experimental neutron data and studied as a function of the number of projections and for
different setups of the imaging system. The reconstructed images were quantitatively
compared in terms of image quality indexes.
Subsequently, the recently introduced Neural Network Filtered Back-Projection
method was proposed in order to reduce the acquisition time during a neutron tomography
experiment. This is the first study which proposes and tests a machine learning
based reconstruction method for neutron tomography. The Neural Network Filtered
Back-Projection method was quantitatively compared to conventional reconstruction
algorithms used in neutron tomography.
Finally, we present NeuTomPy, a new Python package for tomographic data processing
and reconstruction. NeuTomPy is a cross-platform toolbox ready to work with
neutron data. The first release of NeuTomPy includes pre-processing algorithms, a
wide range of classical and state-of-the-art reconstruction methods and several image
quality indexes, in order to evaluate the reconstruction quality. This software is free
and open-source, hence researchers can freely use it and contribute to the project.Soggetto
Imaging; Tomografia a neutroni; Machine learning; Metodi numerici; Metodi di ricostruzione tomografi
Relazione
FIS/07;