Adapted acquisition trajectory and iterative reconstruction for few-views CT inspection

8th Conference on Industrial Computed Tomography, Wels, Austria (iCT 2018)International audienceOne of the major stakes of industrial Computed Tomography (CT) is the reduction of acquisition time in order to allow its usedirectly in production lines. With the introduction of robotic systems, which are capable of taking each piece from the productionconveyor and to manipulate them inside an X-ray beam or to directly move the X-ray equipment around the object, newopportunities arise. Especially, the inspection trajectory is no more limited to one acquisition plane and when combined with anadequate algorithm, a satisfactory CT reconstruction can be obtained from few X-ray projections.Such 3D trajectory makes the classical analytic reconstruction algorithms inappropriate and requires the use of 3D iterativereconstruction algorithms. In this work, we focus on two iterative algorithms, the well-known Simultaneous AlgebraicReconstruction Technique (SART) and the Discrete Algebraic Reconstruction Technique (DART), which integrates priorknowledge on the object, to perform 3D reconstructions from a limited data set. The performances of both algorithms on complextrajectories are evaluated in simulation environment using the CIVA software from CEA List and real acquisitions on nonstandard trajectories are performed with the robotic platform and reconstruction results with SART and DART algorithms arepresented and compared

Robust 3D/2D hybrid registration for integrating a priori CAD model into X-ray Computed Tomography

International audienceLe positionnement d’un modèle CAO d’une pièce est effectué à partir de projections expérimentales par rayons X (RX) afin de pouvoir l’utiliser comme information a priori d’une reconstruction tomographique RX. La méthode proposée est un asservissement visuel hybride en points ainsi qu’en moments d’images. La robustesse et la précision de la méthode sont testées sur six objets de formes variées en considérant à chaque fois une centaine d’initialisations différentes. Enfin, un pas vers l’automatisation complète du procédé est envisagé

Fast algorithms based on Empirical Interpolation Methods for selecting best projections in Sparse-View X-ray Computed Tomography using a priori information

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X-ray Computed Tomography Reconstruction on Non-Standard Trajectories for Robotized Inspection

International audienceThe inspection of complex industrial parts with X-ray computedtomography (CT) is of great interest to characterize them and detect potential flaws.Classically CT acquisition is based on a circular trajectory and this is by far the mostfrequent case in industrial applications. The use of robotic arms for inspection isrecent and it is mainly done for accessing specific and difficult views in digitalradiographic inspection. A new X-ray inspection platform is being installed at CEALIST, which uses two robotic arms for positioning the X-ray source and thedetector. In parallel to the development of this platform which enables a largevariety of scanning trajectories, work will focus on developing CT reconstructionalgorithms that are adapted to non-standard trajectories. We present here newchallenges brought by these robotized inspections and preliminary results forreconstruction on such trajectories. CIVA software is used to simulate thesecomplex inspections and our developed methods will be integrated as reconstructiontools

Assessing the influence of CT acquisition parameters on flaw detectability through simulation

International audienceX-Ray Computed Tomography (XCT) is a unique tool to fully visualize and understand the nature and size of flaws in industrial parts, with a growing application in different fields such as aeronautics and more recently metal additive manufacturing. The inevitable questions underlying any XCT inspection concern the detectability limit of the measure. What size of defect will be detected with my current configuration? How can I optimize my acquisition material or parameters to improve the detectability limit? Naturally, the known characteristics of the XCT systems (detector pixel size, X-ray tube voltage and focal spot size, magnification) give a first answer to these questions, at least in terms of spatial resolution, but it is more difficult to estimate a priori the visibility of a flaw in terms of contrast. The cost of XCT inspection, the difficulty to design specimen with narrow internal defects and the influence of the geometry of the part on the XCT image quality make experimental analysis of the detectability limits difficult. The simulation brings therefore a promising alternative, provided that it gives a thorough representation of a real inspection

EVALUATION AND COMPARISON OF TWO DEEP-LEARNING STRATEGIES FOR ON-LINE X-RAY COMPUTED TOMOGRAPHY

International audienceX-ray Computed Tomography (CT) has been increasinglyused in many industrial domains for its unique capability of con-trolling both the integrity and dimensional conformity of parts.Still, it fails to be adopted as a standard technique for on-line mon-itoring due to its excessive cost in terms of acquisition time. Thereduction of the number of projections, leading to the so-calledsparse-view CT strategy, while maintaining a sufficient recon-struction quality is therefore one of the main challenges in thisfield. This work aims to evaluate and compare the performancesof two deep learning strategies for the sparse-view reconstructionproblem. As such, we propose an extensive study of these meth-ods, both in terms of data regime and angular sparsity duringtraining. The two strategies present quantitative improvementsover a classical FBP/FDK approach with a PSNR improvementvarying between 11 and 16 dB (depending on the angular spar-sity) ; showing that efficient CT inspection can be performed fromonly few dozens of image

Best projections selection algorithm based on constrained QDEIM for sparse-views X-ray Computed Tomography

iCT 2023 - 12th Conference on Industrial Computed Tomography 2023, 27 February - 2 March 2023 in Fürth, GermanyInternational audienceX-ray Computed Tomography is a non-destructive testing tool increasingly used by manufacturers, with growing interest in in-line testing applications. However, it is still struggling to establish itself due to long acquisition times. Industrial imperatives force us to reduce the number of views. The rise of iterative reconstruction methods has made it possible to partially solve the Sparse-View CT problem thanks to the injection of regularisation terms. Furthermore, these methods also allow more freedom in the acquisition trajectories. In our work, to reduce the number of projections without impacting the reconstruction quality, we have not optimised the reconstruction but rather the choice of the most relevant views. Indeed, not all views provide the same amount of information. We, therefore, present a technique for selecting views when the geometry of the inspected object is known a priori. Our method is based on the Q-Discrete Empirical Interpolation Method and considers the attenuation of the rays

Implementation and Evaluation of Two Helical CTReconstruction Algorithms in CIVA

International audienceThe large majority of industrial CT systems reconstruct the 3D volume by using an acquisition on a circular trajectory.However, when inspecting long objects which are highly anisotropic, this scanning geometry creates severe artifacts in thereconstruction. For this reason, the use of an advanced CT scanning method like helical data acquisition is an ecient way toaddress this aspect known as the long-object problem. Recently, several analytically exact and quasi-exact inversion formulas forhelical cone-beam reconstruction have been proposed. Among them, we identified two algorithms of interest for our case. Thesealgorithms are exact and of filtered back-projection structure. In this work we implemented the filtered-backprojection (FBP) andbackprojection-filtration (BPF) algorithms of Zou and Pan (2004). For performance evaluation, we present a numerical comparisonof the two selected algorithms with the helical FDK algorithm using both complete (noiseless and noisy) and truncated datagenerated by CIVA (the simulation platform for non-destructive testing techniques developed at CEA)

Two Local FBP Algorithms for Helical Cone-beam Computed Tomography

International audienceWhen dealing with X-ray inspection of large industrial objects, we are generally interested in the 3D reconstruction of a region-of-interest potentially containing flaws or other features rather than on the reconstruction of the whole object. This local reconstruction is related to a reduction in the field-of-view which consequently truncates the projections during data acquisition and produces severe artifacts when using the Feldkamp-Davis-Kress algorithm. To reduce these artifacts caused by transversely truncated projections, we can replace the global ramp filtering approach of the Feldkamp-Davis-Kress method into either the local first derivative operator combined with the Hilbert transform or the 1D Laplace operator followed by a non-local filtering step using a logarithmic kernel. In this paper, our contribution is the extension of the two algorithms from circular to helical scanning geometry. For performance evaluation of our implementations, we present a numerical comparison of the two algorithms with the standard helical FDK algorithm using both complete and truncated data generated by CIVA (the simulation platform for non-destructive testing techniques developed at CEA)