Fundamenta Informaticae - Volume 172, issue 2

Authors: Brunetti, Sara | Dulio, Paolo | Frosini, Andrea | Rozenberg, Grzegorz

Article Type: Other

DOI: 10.3233/FI-2020-1895

Citation: Fundamenta Informaticae, vol. 172, no. 2, pp. i-xi, 2020

Authors: Beirinckx, Quinten | Ramos-Llordén, Gabriel | Jeurissen, Ben | Poot, Dirk H.J. | Parizel, Paul M. | Verhoye, Marleen | Sijbers, Jan | den Dekker, Arnold J.

Article Type: Research Article

Abstract: Magnetic resonance imaging (MRI) based T 1 mapping allows spatially resolved quantification of the tissue-dependent spin-lattice relaxation time constant T 1 , which is a potential biomarker of various neurodegenerative diseases, including Multiple Sclerosis, Alzheimer disease, and Parkinson’s disease. In conventional T 1 MR relaxometry, a quantitative T 1 map is obtained from a series of T 1 -weighted MR images. Acquiring such a series, however, is time consuming. This has sparked the development of more efficient T 1 mapping methods, one of which is a super-resolution reconstruction (SRR) framework in which a set of low …resolution (LR) T 1 -weighted images is acquired and from which a high resolution (HR) T 1 map is directly estimated. In this paper, the SRR T 1 mapping framework is augmented with motion estimation. That is, motion between the acquisition of the LR T 1 -weighted images is modeled and the motion parameters are estimated simultaneously with the T 1 parameters. Based on Monte Carlo simulation experiments, we show that such an integrated motion/relaxometry estimation approach yields more accurate T 1 maps compared to a previously reported SRR based T 1 mapping approach. Show more

Keywords: quantitative magnetic resonance imaging, super-resolution, T1 mapping, maximum likelihood estimation, motion correction

DOI: 10.3233/FI-2020-1896

Citation: Fundamenta Informaticae, vol. 172, no. 2, pp. 105-128, 2020

Authors: Lékó, Gábor | Balázs, Péter

Article Type: Research Article

Abstract: In this paper, we propose two strategies of reducing the amount of data needed for binary tomographic reconstructions. We study how the direction dependency changes by reducing the resolution of an image and we point out how to specify the most informative angles for the original image using its downscaled version. We also show how to predict the final acceptable resolution. Applications of the proposed strategies are also mentioned.

Keywords: binary tomography, reconstruction, projection selection, scale invariance, resolution invariance, direction dependency, informative angles

DOI: 10.3233/FI-2020-1897

Citation: Fundamenta Informaticae, vol. 172, no. 2, pp. 129-142, 2020

Authors: Lagerwerf, Marinus J. | Palenstijn, Willem Jan | Bleichrodt, Folkert | Batenburg, K. Joost

Article Type: Research Article

Abstract: Choosing a regularization parameter for tomographic reconstruction algorithms is often a cumbersome task of trial-and-error. Although several automatic and objective criteria have been proposed, each of them yields a different “optimal” value, which may or may not correspond to the actual implicit image quality metrics one would like to optimize for. Exploration of the space of regularization parameters is computationally expensive, as it requires many reconstructions to be computed. In this paper we propose an algorithmic approach for computationally efficient exploration of the regularization parameter space, based on a pixel-wise interpolation scheme. Once a relatively small number of reconstructions …have been computed for a sparse sampling of the parameters, an approximation of the reconstructed image for other parameter values can be computed instantly, thereby allowing both manual and automated selection of the most preferable parameters based on a variety of image quality metrics. We demonstrate that for three common variational reconstruction methods, our approach results in accurate approximations of the reconstructed image and that it can be used in combination with existing approaches for choosing optimal regularization parameters. Show more

Keywords: Spline interpolation, Variational methods, Regularization parameter, Computed Tomography, Total Variation, Total Generalized Variation

DOI: 10.3233/FI-2020-1898

Citation: Fundamenta Informaticae, vol. 172, no. 2, pp. 143-167, 2020

Authors: Menegaz, Gloria | Tomazzoli, Claudio | Cristani, Matteo | Galazzo, Ilaria Boscolo | Storti, Silvia Francesca

Article Type: Research Article

Abstract: Graph-based network modeling is becoming increasingly pervasive touching very different fields. Among these are social networks analysis and brain connectivity modeling. Though apparently very far apart, these two domains share the same questions about how the underlying network is structured and how this can be measured. This determines an a-priori unexpected convergence of the research efforts of two different communities, that is neurosciences and information technology. In this work, we put forth some basic issues emerging from the overlaps of the two domains and propose a first simple measure allowing to capture one among the features of interest: the …transtopic closeness centrality . To this end, the related concepts are briefly recalled and two case studies are considered. Then, relying on social network analysis principles, the transposition to functional brain networks is proposed highlighting and discussing some of the inherent critical issues. Show more

DOI: 10.3233/FI-2020-1899

Citation: Fundamenta Informaticae, vol. 172, no. 2, pp. 169-186, 2020

Authors: Presotto, Luca

Article Type: Research Article

Abstract: Positron Emission Tomography image reconstruction needs a map of photon attenuation probability to provide the correct solution. This map is generally provided by an independent imaging modality. However, it might suffer for artifacts due to patient motion in sequential systems or from intrinsic limitation of the second modality (e.g.: bones that cannot be identified in MR images). It has been shown that such map can be estimated from the PET data themselves, but the solution to this problem has much worse conditioning than the tomographic problem. In this work we propose a new algorithm based on the use of …multiple L 1 regularization terms in the attenuation sub-problem, to incorporate prior knowledge. We also chose optimal maximizers for both sub-problems: preconditioned gradient descent for the emission one and split-Bregman for the attenuation one. The algorithm was then tested using digital phantom simulations. The proposed algorithm proved to provide accurate quantification over a large range of strength of the regularization terms. The algorithm is also able to reconstruct objects outside of the region where the problem is uniquely determined and it is able to fix the undetermined global scaling factor of joint attenuation and emission estimation. Thanks to the maximizers chosen, the algorithm is computationally less expensive than the current standard. Show more

DOI: 10.3233/FI-2020-1900

Citation: Fundamenta Informaticae, vol. 172, no. 2, pp. 187-202, 2020

Authors: Turpin, Léonard | Roux, Stéphane | Caty, Olivier | Denneulin, Sébastien

Article Type: Research Article

Abstract: The tomography of an object with limited angle can be addressed through Iterative Reconstruction Reprojection (IRR) procedure, wherein a standard reconstruction procedure is used together with a “filtering” of the image at each iteration. It is here proposed to use as a filter a phase-field—or Cahn-Hilliard—regularization interlaced with a filtered back-projection reconstruction. This reconstruction procedure is tested on a cone-beam tomography of a 3D woven ceramic composite material, and is shown to retrieve a reconstructed volume with very low artifacts in spite of a large missing angle interval (up to 28%).

Keywords: Iterative Reconstruction Reprojection (IRR), X-ray micro-CT, Limited-angle reconstruction, Regularization, Phase field

DOI: 10.3233/FI-2020-1901

Citation: Fundamenta Informaticae, vol. 172, no. 2, pp. 203-219, 2020

Authors: Brunetti, Sara | Dulio, Paolo | Peri, Carla

Article Type: Research Article

Abstract: We address the problem of reconstructing digital images with finitely many grey levels from the knowledge of their X-rays in a given finite set of lattice directions. The main result of the paper provides sets of 2p (p ≥ 3) lattice directions which uniquely determine images with p grey levels, contained in a finite lattice grid. This extends previous uniqueness results for binary images.

DOI: 10.3233/FI-2020-1902

Citation: Fundamenta Informaticae, vol. 172, no. 2, pp. 221-238, 2020