Search

Neutron Tomography (NT) is a non-destructive technique to investigate the inner structure of a wide range of objects and, in some cases, provides valuable results in comparison to the more common X-ray imaging techniques. However, NT is time consuming and scanning a set of similar objects during a beamtime leads to data redundancy and long acquisition times. Nowadays NT is unfeasible for quality checking study of large quantities of similar objects. One way to decrease the total scan time is to reduce the number of projections. Analytical reconstruction methods are very fast but under this condition generate streaking artifacts in the reconstructed images. Iterative algorithms generally provide better reconstruction for limited data problems, but at the expense of longer reconstruction time. In this study, we propose the recently introduced Neural Network Filtered Back-Projection (NN-FBP) method to optimize the time usage in NT experiments. Simulated and real neutron data were used to assess the performance of the NN-FBP method as a function of the number of projections. For the first time a machine learning based algorithm is applied and tested for NT image reconstruction problem. We demonstrate that the NN-FBP method can reliably reduce acquisition and reconstruction times and it outperforms conventional reconstruction methods used in NT, providing high image quality for limited datasets.

In this article we present the NeuTomPy Toolbox, a new Python package for tomographic data processing and reconstruction. The toolbox includes pre-processing algorithms, artifacts removal and a wide range of iterative reconstruction methods as well as the Filtered Back Projection algorithm. The NeuTomPy toolbox was conceived primarily for neutron tomography datasets and developed to support the need of users and researchers to compare state-of-the-art reconstruction methods and choose the optimal data processing workflow for their data. In fact, in several cases sparse-view datasets are acquired to reduce scan time during a neutron tomography experiment. Hence, there is great interest in improving quality of the reconstructed images by means of iterative methods and advanced image-processing algorithms. The toolbox has a modular design, multi-threading capabilities and it supports Windows, Linux and Mac OS operating systems. The NeuTomPy toolbox is open source and it is released under the GNU General Public License v3, encouraging researchers and developers to contribute. In this paper we present an overview of the main toolbox functionalities and finally we show a typical usage example.

Neutron Tomography (NT) is a non-destructive technique to investigate the inner structure of a wide range of objects and, in some cases, provides valuable results in comparison to the more common X-ray imaging techniques. However, NT is time consuming and scanning a set of similar objects during a beamtime leads to data redundancy and long acquisition times. Nowadays NT is unfeasible for quality checking study of large quantities of similar objects. One way to decrease the total scan time is to reduce the number of projections. Analytical reconstruction methods are very fast but under this condition generate streaking artifacts in the reconstructed images. Iterative algorithms generally provide better reconstruction for limited data problems, but at the expense of longer reconstruction time. In this study, we propose the recently introduced Neural Network Filtered Back-Projection (NN-FBP) method to optimize the time usage in NT experiments. Simulated and real neutron data were used to assess the performance of the NN-FBP method as a function of the number of projections. For the first time a machine learning based algorithm is applied and tested for NT image reconstruction problem. We demonstrate that the NN-FBP method can reliably reduce acquisition and reconstruction times and it outperforms conventional reconstruction methods used in NT, providing high image quality for limited datasets.

In this article we present the NeuTomPy Toolbox, a new Python package for tomographic data processing and reconstruction. The toolbox includes pre-processing algorithms, artifacts removal and a wide range of iterative reconstruction methods as well as the Filtered Back Projection algorithm. The NeuTomPy toolbox was conceived primarily for neutron tomography datasets and developed to support the need of users and researchers to compare state-of-the-art reconstruction methods and choose the optimal data processing workflow for their data. In fact, in several cases sparse-view datasets are acquired to reduce scan time during a neutron tomography experiment. Hence, there is great interest in improving quality of the reconstructed images by means of iterative methods and advanced image-processing algorithms. The toolbox has a modular design, multi-threading capabilities and it supports Windows, Linux and Mac OS operating systems. The NeuTomPy toolbox is open source and it is released under the GNU General Public License v3, encouraging researchers and developers to contribute. In this paper we present an overview of the main toolbox functionalities and finally we show a typical usage example.

Neutron Tomography (NT) is an established technique to non-destructively investigate the inner structure of a wide range of objects. The major drawback of NT is the long acquisition time required to perform a tomographic scan compared to an X-ray Computed Tomography (CT) scan, due to the limited particle flux of the existing neutron sources. Therefore, in the NT field there is great interest in the reduction of scan time, dictated by the high neutrons production cost, aimed at optimizing the beamtime use at neutron imaging beamlines. A way for decreasing the total scan time is to reduce the number of projections. Generally, iterative reconstruction methods have advantages over analytical algorithms, such as the widely used Filtered Back-Projection (FBP), when data are noisy and limited. This research is focused on the comparative analysis of different reconstruction techniques, aimed at finding the data processing procedure suitable for NT, that reduces the scan time without reduction of the reconstructed image quality. For this purpose, a phantom sample was analysed by means of a white beam NT performed at the IMAT beamline, ISIS Neutron Source, U.K . Experimental data were used to test the performances of the FBP algorithm and different iterative reconstruction methods 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 and the benefits of iterative methods for the limited data problem are discussed.

The cold neutron imaging and diffraction instrument IMAT at the second target station of the pulsed neutron source ISIS is currently being commissioned and prepared for user operation. IMAT will enable white-beam neutron radiography and tomography. One of the benefits of operating on a pulsed source is to determine the neutron energy via a time of flight measurement, thus enabling energy-selective and energy-dispersive neutron imaging, for maximizing image contrasts between given materials and for mapping structure and microstructure properties. We survey the hardware and software components for data collection and image analysis on IMAT, and provide a step-by-step procedure for operating the instrument for energy-dispersive imaging using a two-phase metal test object as an example

The cold neutron imaging and diffraction instrument IMAT at the second target station of the pulsed neutron source ISIS is currently being commissioned and prepared for user operation. IMAT will enable white-beam neutron radiography and tomography. One of the benefits of operating on a pulsed source is to determine the neutron energy via a time of flight measurement, thus enabling energy-selective and energy-dispersive neutron imaging, for maximizing image contrasts between given materials and for mapping structure and microstructure properties. We survey the hardware and software components for data collection and image analysis on IMAT, and provide a step-by-step procedure for operating the instrument for energy-dispersive imaging using a two-phase metal test object as an example

Some cultured and natural pearls can be reliably distinguished by visual inspection and by the use of lens and microscope. However, assessing the origin of the pearls could be not straightforward since many different production techniques can now be found in the pearl market, for example in salt or freshwater environments, with or without a rigid nucleus. This wide range of products requires the use of new effective scientific techniques. Indeed, X-ray radiography has been used by gemologists since last century as the only safe and non-destructive way to visually inspect the interior of a pearl, and recently, also X-ray computed micro-tomography was used to better visualize the inner parts of the gems. In this study we analyzed samples of natural and cultured pearls by means of two non-destructive techniques: the X-ray Phase-Contrast Imaging (PCI) and the Neutron Imaging (NI). PCI and NI results will be combined for the first time, to better visualize the pearls internal morphology, thus giving relevant indications on the pearl formation process

This work presents the first neutron characterization of artefacts from the grave-goods of Kha and Merit, a unique cultural treasure of mankind and preserved at the Museo Egizio in Turin. This magnificent collection, discovered in the early years of the last century, includes an impressive amount of artefacts such as coffins, textiles, metallic and ceramic pottery, and alabaster containers. The present study investigates two sealed potteries, using enhanced Neutron Tomography, Radiography, and Prompt Gamma Activation Analysis techniques. The neutron probe provides access to the morphological reconstructions of the inner parts of the vases and to map, with unprecedented details, the elemental composition of the surfaces and bulk areas of the potteries. The present work extends the knowledge to hitherto unknown contents of the sealed vases unlocking their secrets.