A comparative study of reconstruction methods for neutron tomography

Abstract

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.