Eline Janssens

Fast and flexible X-ray tomography using the ASTRA toolbox

Object reconstruction from a series of projection images, such as in computed tomography (CT), is a popular tool in many different application fields. Existing commercial software typically provides sufficiently accurate and convenient-to-use reconstruction tools to the end-user. However, in applications where a non-standard acquisition protocol is used, or where advanced reconstruction methods are required, the standard software tools often are incapable of computing accurate reconstruction images. This article introduces the ASTRA Toolbox. Aimed at researchers across multiple tomographic application fields, the ASTRA Toolbox provides a highly efficient and highly flexible open source set of tools for tomographic projection and reconstruction. The main features of the ASTRA Toolbox are discussed and several use cases are presented.

Inline discrete tomography system

It is proposed an inline X-ray Computed Tomography imaging setup.Discrete Tomography is well suited for the proposed scanning setup.The use of a conveyor belt with object rotation allows a full angular sampling.The use of prior knowledge about the external object shape leads to better results. X-ray Computed Tomography (CT) has been applied in agriculture engineering for quality and defect control in food products. However, conventional CT systems are neither cost effective nor flexible, making the deployment of such technology unfeasible for many industrial environments. In this work, we propose a simple and cost effective X-ray imaging setup that comprises a linear translation of the object in a conveyor belt with a fixed X-ray source and detector, with which a small number of X-ray projections can be acquired within a limited angular range. Due to the limitations of such geometry, conventional reconstruction techniques lead to misshapen images. Therefore, we apply a Discrete Tomography reconstruction technique that incorporates prior knowledge of the density of the objects materials. Moreover, we further improve the reconstruction results with the following strategies: (i) an image acquisition involving object rotation during a linear translation in the conveyor-belt; and (ii) an image reconstruction incorporating prior knowledge of the object support (e.g., obtained from optic sensors). Experiments based on simulation as well as real data demonstrate substantial improvement of the reconstruction quality compared to conventional reconstruction methods.

Neural netwok based X-ray tomography for fast inspection of apples on a conveyor belt system

The throughput of an inline computed tomography (CT) based inspection system depends on the speed of its image reconstruction algorithm. Filtered back projection (FBP) provides fast reconstructions, but requires many high quality radiographs from all angles to obtain accurate reconstructions. This is not achievable in an inline environment. Iterative reconstruction methods yield adequate reconstructions from limited, but they are slow. Recently a new reconstruction algorithm was introduced [1] that can handle limited data and is very fast: the neural network FBP (NN-FBP). In this work, we introduce a neural network (NN) based Hilbert transform FBP (NN-hFBP) for inline inspection. This method reconstructs images with a filter-based Hilbert transform FBP method. The filters are application specific and trained by a neural network. Comparison of the NN-hFBP and conventional reconstruction methods applied to inline fan-beam X-ray data of apples shows that the NN-hFBP yields high quality images in a short reconstruction time.