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

Bussy, Victor; Vienne, Caroline; Kaftandjian, Valérie

doi:10.1016/j.ndteint.2022.102768

Cited by 8 publications

(2 citation statements)

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“…Initially designed for Reduced Order Models, QDEIM makes it possible to quickly find the optimal positions of sensors to reconstruct a signal from its representation in a low-dimensional tailored space [14]. In [13], we demonstrated how QDEIM could be applied in a different way to select the best views for reconstruction. To apply the QDEIM for view selection, first a set of possible projections along a predefined trajectory is computed (see Figure 1).…”

Section: Q-discrete Empirical Interpolation Methodsmentioning

confidence: 99%

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

Bussy¹,

Vienne²,

Escoda³

et al. 2023

eJNDT

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X-ray Computed Tomography (CT) 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 as a standard technique due to long acquisition times. In order to meet industrial imperatives, recent research aims at reducing this acquisition time by limiting the number of views while keeping a good CT image quality. 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 this work, we propose a method for reducing the number of projections without impacting the reconstruction quality by acquiring only 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 roughly known a priori. Our method is based on the Q-Discrete Empirical Interpolation Method (QDEIM) and considers the attenuation of the rays as an additional constraint.

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Section: Q-discrete Empirical Interpolation Methodsmentioning

confidence: 99%

“…This algebraic criterion is easy and fast to compute. We have shown in [13] that applying QDEIM to the sinogram matrix was tantamount to greedily selecting the most different projections. This property is attractive because it joins concepts evoked on the information provided by a projection [15].…”

Section: Q-discrete Empirical Interpolation Methodsmentioning

confidence: 99%

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

Bussy¹,

Vienne²,

Escoda³

et al. 2023

eJNDT

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Roadmap on computational methods in optical imaging and holography [invited]

Rosen,

Alford,

Allan

et al. 2024

Appl. Phys. B

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Computational methods have been established as cornerstones in optical imaging and holography in recent years. Every year, the dependence of optical imaging and holography on computational methods is increasing significantly to the extent that optical methods and components are being completely and efficiently replaced with computational methods at low cost. This roadmap reviews the current scenario in four major areas namely incoherent digital holography, quantitative phase imaging, imaging through scattering layers, and super-resolution imaging. In addition to registering the perspectives of the modern-day architects of the above research areas, the roadmap also reports some of the latest studies on the topic. Computational codes and pseudocodes are presented for computational methods in a plug-and-play fashion for readers to not only read and understand but also practice the latest algorithms with their data. We believe that this roadmap will be a valuable tool for analyzing the current trends in computational methods to predict and prepare the future of computational methods in optical imaging and holography.

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