Relevant bibliographies by topics / Limited-Angle reconstruction

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Limited-Angle reconstruction'

Author: Grafiati
Published: 5 April 2025

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Journal articles on the topic "Limited-Angle reconstruction"

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Jaffe, J. S. "Limited angle reconstruction using stabilized algorithms." IEEE Transactions on Medical Imaging 9, no. 3 (1990): 338–44. http://dx.doi.org/10.1109/42.57772.

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Reeds, J. A., and L. A. Shepp. "Limited Angle Reconstruction in Tomography via Squashing." IEEE Transactions on Medical Imaging 6, no. 2 (June 1987): 89–97. http://dx.doi.org/10.1109/tmi.1987.4307808.

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Kulikajevas, Audrius, Rytis Maskeliūnas, Robertas Damaševičius, and Marta Wlodarczyk-Sielicka. "Auto-Refining Reconstruction Algorithm for Recreation of Limited Angle Humanoid Depth Data." Sensors 21, no. 11 (May 26, 2021): 3702. http://dx.doi.org/10.3390/s21113702.

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With the majority of research, in relation to 3D object reconstruction, focusing on single static synthetic object reconstruction, there is a need for a method capable of reconstructing morphing objects in dynamic scenes without external influence. However, such research requires a time-consuming creation of real world object ground truths. To solve this, we propose a novel three-staged deep adversarial neural network architecture capable of denoising and refining real-world depth sensor input for full human body posture reconstruction. The proposed network has achieved Earth Mover and Chamfer distances of 0.059 and 0.079 on synthetic datasets, respectively, which indicates on-par experimental results with other approaches, in addition to the ability of reconstructing from maskless real world depth frames. Additional visual inspection to the reconstructed pointclouds has shown that the suggested approach manages to deal with the majority of the real world depth sensor noise, with the exception of large deformities to the depth field.
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Schüle, T., C. Schnörr, J. Hornegger, and S. Weber. "A Linear Programming Approach to Limited Angle 3D Reconstruction from DSA Projections." Methods of Information in Medicine 43, no. 04 (2004): 320–26. http://dx.doi.org/10.1055/s-0038-1633875.

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Summary Objectives: We investigate the feasibility of binary-valued 3D tomographic reconstruction using only a small number of projections acquired over a limited range of angles. Methods: Regularization of this strongly ill-posed problem is achieved by (i) confining the reconstruction to binary vessel/non-vessel decisions, and (ii) by minimizing a global functional involving a smoothness prior. Results: Our approach successfully reconstructs volumetric vessel structures from three projections taken within 90°. The percentage of reconstructed voxels differing from ground truth is below 1%. Conclusion: We demonstrate that for particular applications – like Digital Subtraction Angiography – 3D reconstructions are possible where conventional methods must fail, due to a severely limited imaging geometry. This could play an important role for dose reduction and 3D reconstruction using non-conventional technical setups.
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Rothkamm, Oliver, Johannes Gürtler, Jürgen Czarske, and Robert Kuschmierz. "Dense U-Net for Limited Angle Tomography of Sound Pressure Fields." Applied Sciences 11, no. 10 (May 17, 2021): 4570. http://dx.doi.org/10.3390/app11104570.

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Tomographic reconstruction allows for the recovery of 3D information from 2D projection data. This commonly requires a full angular scan of the specimen. Angular restrictions that exist, especially in technical processes, result in reconstruction artifacts and unknown systematic measurement errors. We investigate the use of neural networks for extrapolating the missing projection data from holographic sound pressure measurements. A bias flow liner was studied for active sound dampening in aviation. We employed a dense U-Net trained on synthetic data and compared reconstructions of simulated and measured data with and without extrapolation. In both cases, the neural network based approach decreases the mean and maximum measurement deviations by a factor of two. These findings can enable quantitative measurements in other applications suffering from limited angular access as well.
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Wang, Chengxiang, Li Zeng, Lingli Zhang, Yumeng Guo, and Wei Yu. "An adaptive iteration reconstruction method for limited-angle CT image reconstruction." Journal of Inverse and Ill-posed Problems 26, no. 6 (December 1, 2018): 771–87. http://dx.doi.org/10.1515/jiip-2017-0034.

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Abstract The limited-angle computed tomography (CT) reconstruction problem is an ill-posed inverse problem, and the parameter selection for limited-angle CT iteration reconstruction is a difficult issue in practical application. In this paper, to alleviate the instability of limited-angle CT reconstruction problem and automatize the reconstruction process, we propose an adaptive iteration reconstruction method that the regularization parameter is chosen adaptively via the plot of the normalized wavelet coefficients fitting residual versus that the {\ell_{0}} regularization part. The experimental results show that the reconstructed images using the method with adapted regularization parameter are almost as good as that using the non-adapted parameter method in terms of visual inspection, in addition, our method has an advantage in adaptively choosing the regularization parameter.
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Hoyle, C., M. Sutcliffe, P. Charlton, S. Mosey, and I. Cooper. "Limited-angle ultrasonic tomography back-projection imaging." Insight - Non-Destructive Testing and Condition Monitoring 63, no. 1 (January 1, 2021): 20–28. http://dx.doi.org/10.1784/insi.2021.63.1.20.

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Ultrasonic inspection of through-transmission is limited due to the inability to obtain defect depth information. Loss of signal is used as the only indicator, providing lateral defect information. This is often a problem in ultrasonic inspection. Radiographic acquisition techniques, where the X-ray source acts as the transmitter and the detector as the receiver, are conceptionally similar to ultrasonic through-transmission. In the latter, the tomography back-projection method is used to reconstruct images of an object that has been subjected to a minimum of 180° of rotation, to allow for full coverage of the item. In this paper, a novel approach based on back-projection is presented to improve image resolution and defect detectability. Two ultrasonic transducers in through-transmission configuration are utilised to capture data for image processing. The rotation of the transmitter and receiver is not possible in this set-up and, therefore, the reconstruction relies on the artificial generation of a limited rotation. Two probes are aligned either side of the material and are used to gather the ultrasonic signals. These signals are processed before the reconstruction algorithm is applied to them. Various processing and imaging reconstruction algorithms are explored, building on the basic back-projection method to obtain images that are better focused. This technique could be used within materials where there are high attenuation levels and, therefore, traditional pulse-echo is not feasible.
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Wang, Jiaxi, Li Zeng, Chengxiang Wang, and Yumeng Guo. "ADMM-based deep reconstruction for limited-angle CT." Physics in Medicine & Biology 64, no. 11 (May 29, 2019): 115011. http://dx.doi.org/10.1088/1361-6560/ab1aba.

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Tomitani, T., and M. Hirasawa. "Image reconstruction from limited angle Compton camera data." Physics in Medicine and Biology 47, no. 12 (June 6, 2002): 2129–45. http://dx.doi.org/10.1088/0031-9155/47/12/309.

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Qu, Gang-rong, and Ming Jiang. "Landweber iterative methods for angle-limited image reconstruction." Acta Mathematicae Applicatae Sinica, English Series 25, no. 2 (March 17, 2009): 327–34. http://dx.doi.org/10.1007/s10255-008-8132-8.

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Dissertations / Theses on the topic "Limited-Angle reconstruction"

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Thompson, William. "Source firing patterns and reconstruction algorithms for a switched source, offset detector CT machine." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/source-firing-patterns-and-reconstruction-algorithms-for-a-switched-source-offset-detector-ct-machine(97dc0705-45e2-4b7a-9ef3-1c8a58d5411a).html.

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We present a new theoretical model and reconstruction results for a new class of fast x-ray CT machine -- the Real Time Tomography (RTT) system, which uses switched sources and an offset detector array. We begin by reviewing elementary properties of the Radon and X-ray transforms, and limited angle tomography. Through the introduction of a new continuum model, that of sources covering the surface of a cylinder in R³, we show that the problem of three-dimensional reconstruction from RTT data reduces to inversion of the three-dimensional Radon transform with limited angle data. Using the Paley-Wiener theorem, we then prove the existence of a unique solution and give comments on stability and singularity detection. We show, first in the two-dimensional case, that the conjugate gradient least squares algorithm is suitable for CT reconstruction. By exploiting symmetries in the system, we then derive a method of applying CGLS to the three-dimensional inversion problem using stored matrix coefficients. The new concept of source firing order is introduced and formalised, and some novel visualisations are used to show how this affects aspects of the geometry of the system. We then perform a detailed numerical analysis using the condition number and SVD of the forward projection matrix $A$, to show that the choice of firing order affects the conditioning of the problem. Finally, we give reconstruction results from both simulated phantoms and real experimental data that support the numerical analysis.
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Barquero, Harold. "Limited angular range X-ray micro-computerized tomography : derivation of anatomical information as a prior for optical luminescence tomography." Thesis, Strasbourg, 2015. http://www.theses.fr/2015STRAE033/document.

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Cette thèse traite du couplage d'un tomographe optique par luminescence (LCT) et d'un tomographe par rayons X (XCT), en présence d'une contrainte sur la géométrie d'acquisition du XCT. La couverture angulaire du XCT est limitée à 90 degrés pour satisfaire des contraintes spatiales imposées par le LCT existant dans lequel le XCT doit être intégré. L'objectif est de dériver une information anatomique, à partir de l'image morphologique issue du XCT. Notre approche a consisté i) en l'implémentation d'un algorithme itératif régularisé pour la reconstruction tomographique à angle limité, ii) en la construction d'un atlas anatomique statistique de la souris et iii) en l'implémentation d'une chaîne automatique réalisant la segmentation des images XCT, l'attribution d'une signification anatomique aux éléments segmentés, le recalage de l'atlas statistique sur ces éléments et ainsi l'estimation des contours de certains tissus à faible contraste non identifiables en pratique dans une image XCT standard
This thesis addresses the combination of an Optical Luminescence Tomograph (OLT) and X-ray Computerized Tomograph (XCT), dealing with geometrical constraints defined by the existing OLT system in which the XCT must be integrated. The result is an acquisition geometry of XCT with a 90 degrees angular range only. The aim is to derive an anatomical information from the morphological image obtained with the XCT. Our approach consisted i) in the implementation of a regularized iterative algorithm for the tomographic reconstruction with limited angle data, ii) in the construction of a statistical anatomical atlas of the mouse and iii) in the implementation of an automatic segmentation workflow performing the segmentation of XCT images, the labelling of the segmented elements, the registration of the statistical atlas on these elements and consequently the estimation of the outlines of low contrast tissues that can not be identified in practice in a standard XCT image
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Laurendeau, Matthieu. "Tomographic incompleteness maps and application to image reconstruction and stationary scanner design." Electronic Thesis or Diss., Lyon, INSA, 2024. http://www.theses.fr/2024ISAL0130.

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La tomographie par rayons X est l'une des modalités d'imagerie les plus couramment utilisées dans les domaines médical et industriel. Ces dernières années, de nouvelles sources à rayons X ont été développées sur la base de cathodes en nanotubes de carbone (CNT). Leur taille compacte permet de concevoir une nouvelle génération de scanner multi-sources. Contrairement aux systèmes traditionels dotés d'une seule source mobile, ces scanners adoptent des architectures stationnaires où plusieurs sources sont fixées à des positions précises. Cela profiterait à la fois à l'industrie, avec des scanners moins chers, et aux applications médicales, avec des scanners légers et mobiles qui pourraient être déployés directement sur les sites d'urgence. Cependant, ce type de scanner a une couverture angulaire limitée, posant des défis importants en reconstruction d'images. Cette thèse se concentre sur la conception de tels scanners stationnaires. Trois axes d'étude sont examinés. La première contribution est le développement d'une métrique indépendante de l'objet, afin d'évaluer la capacité de reconstruction d'une géométrie de scanner. Basée sur la condition de Tuy, la métrique évalue l'incomplétude tomographique locale et est visualisée selon des cartes de champs vectoriels 3D. Elle est ensuite améliorée pour traiter les projections tronquées, la rendant plus applicable aux configurations du monde réel. Ces cartes permettent de classer différentes géométries, de prédire la qualité de reconstruction et d'identifier l'origine des artefacts géométriques. Elle est calculée pour une variété de géométries, y compris des scanners existants. La seconde est une nouvelle méthode de régularisation locale qui permet de relever les défis de la reconstruction à angle limité. Basée sur une régularisation de type variation totale directionnelle (DTV), la méthode adapte la force et les poids directionnels à chaque voxel sélectionné à partir de la métrique introduite précédemment. Deux approches sont explorées : des poids directionnels basés sur le ratio par rapport aux axes de l'image ou basés sur l'ellipse. L'algorithme de reconstruction est évalué dans des simulations 2D et 3D, en considérant des données bruitées et non bruitées, ainsi que des données réelles. La troisième est un outil d'optimisation de la géométrie des scanners. Étant donné un nombre fixe de sources et une surface disponible pour leur positionnement, l'outil optimise l'emplacement des sources en minimisant l'incomplétude tomograhique de la région imagée. Plusieurs algorithmes d'optimisation sont implémentés et testés sur des scénarios simples 2D et 3D
Computed tomography (CT) is one of the most commonly used modality for three-dimensional (3D) imaging in the medical and industrial fields. In the past few years, new X-ray sources have been developed based on carbon nanotube (CNT) cathodes. Their compact size enables the design of a new generation of multi-source CT scanners. In contrast to traditional systems with a single moving source, these scanners often adopt stationary architectures where multiple sources are static. It would benefit both industry with cheaper and motionless systems and medical applications with light-weight and mobile scanners which could be brought to emergency sites. However, this type of scanner uses a fewer number of measurements, known as projections, and may acquire data with a limited range of angles, leading to well-known image reconstruction challenges. This thesis focuses on the design of such stationary CT scanners. Three axes of study were investigated. The first contribution is the development of an object-independent metric to assess the reconstruction capability of a given scanning geometry. Based on Tuy's condition, the metric evaluates local tomographic incompleteness and is visualized through 3D vector field maps. It is further extended to handle truncated projections, improving its applicability to real-world configurations. The metric enables ranking different geometries, predicting image quality reconstruction, and identifying the origin of geometric artifacts. It is applied to a variety of geometries, including existing scanners. The second is a novel local regularization method to address limited-angle reconstruction challenges. The method employs a directional total variation (DTV) regularizer whose strength and directional weights are adaptively selected at each voxel. The weights are determined based on the previously introduced metric. Two approaches for directional weights were explored: ratio-based weighting relative to image axes and ellipse-based weighting. The reconstruction algorithm is evaluated in both 2D and 3D simulations, considering noiseless and noisy data, as well as real data. The third is a tool for optimizing the geometry of CT scanners. Given a fixed number of sources and the surface area available for their positions, the tool optimizes the placement of sources based on the proposed metric. Several state-of-the-art optimization algorithms were implemented and tested on simple 2D and 3D scenarios
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Banjak, Hussein. "X-ray computed tomography reconstruction on non-standard trajectories for robotized inspection." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEI113/document.

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La tomographie par rayons X ou CT pour "Computed Tomography" est un outil puissant pour caractériser et localiser les défauts internes et pour vérifier la conformité géométrique d’un objet. Contrairement au cas des applications médicales, l’objet inspecté en Contrôle Non Destructif (CND) peut être très grand et composé de matériaux de haute atténuation, auquel cas l’utilisation d’une trajectoire circulaire pour l’inspection est impossible à cause de contraintes dans l’espace. Pour cette raison, l’utilisation de bras robotisés est l’une des nouvelles tendances reconnues dans la CT, car elle autorise plus de flexibilité dans la trajectoire d’acquisition et permet donc la reconstruction 3D de régions difficilement accessibles dont la reconstruction ne pourrait pas être assurée par des systèmes de tomographie industriels classiques. Une cellule de tomographie X robotisée a été installée au CEA. La plateforme se compose de deux bras robotiques pour positionner et déplacer la source et le détecteur en vis-à-vis. Parmi les nouveaux défis posés par la tomographie robotisée, nous nous concentrons ici plus particulièrement sur la limitation de l’ouverture angulaire imposée par la configuration en raison des contraintes importantes sur le mouvement mécanique de la plateforme. Le deuxième défi majeur est la troncation des projections qui se produit lorsque l’objet est trop grand par rapport au détecteur. L’objectif principal de ce travail consiste à adapter et à optimiser des méthodes de reconstruction CT pour des trajectoires non standard. Nous étudions à la fois des algorithmes de reconstruction analytiques et itératifs. Avant d’effectuer des inspections robotiques réelles, nous comptons sur des simulations numériques pour évaluer les performances des algorithmes de reconstruction sur des configurations d’acquisition de données. Pour ce faire, nous utilisons CIVA, qui est un outil de simulation pour le CND développé au CEA et qui est capable de simuler des données de projections réalistes correspondant à des configurations d’acquisition définies par l’utilisateur
X-ray computed tomography (CT) is a powerful tool to characterize or localize inner flaws and to verify the geometric conformity of an object. In contrast to medical applications, the scanned object in non-destructive testing (NDT) might be very large and composed of high-attenuation materials and consequently the use of a standard circular trajectory for data acquisition would be impossible due to constraints in space. For this reason, the use of robotic arms is one of the acknowledged new trends in NDT since it allows more flexibility in acquisition trajectories and therefore could be used for 3D reconstruction of hardly accessible regions that might be a major limitation of classical CT systems. A robotic X-ray inspection platform has been installed at CEA LIST. The considered system integrates two robots that move the X-ray generator and detector. Among the new challenges brought by robotic CT, we focus in this thesis more particularly on the limited access viewpoint imposed by the setup where important constraints control the mechanical motion of the platform. The second major challenge is the truncation of projections that occur when only a field-of-view (FOV) of the object is viewed by the detector. Before performing real robotic inspections, we highly rely on CT simulations to evaluate the capability of the reconstruction algorithm corresponding to a defined scanning trajectory and data acquisition configuration. For this purpose, we use CIVA which is an advanced NDT simulation platform developed at CEA and that can provide a realistic model for radiographic acquisitions and is capable of simulating the projection data corresponding to a specific CT scene defined by the user. Thus, the main objective of this thesis is to develop analytical and iterative reconstruction algorithms adapted to nonstandard trajectories and to integrate these algorithms in CIVA software as plugins of reconstruction
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Frikel, Jürgen [Verfasser], Brigitte [Akademischer Betreuer] Forster-Heinlein, Samuli [Akademischer Betreuer] Siltanen, and Rupert [Akademischer Betreuer] Lasser. "Reconstructions in limited angle x-ray tomography: Characterization of classical reconstructions and adapted curvelet sparse regularization / Jürgen Frikel. Gutachter: Brigitte Forster-Heinlein ; Samuli Siltanen ; Rupert Lasser. Betreuer: Brigitte Forster-Heinlein." München : Universitätsbibliothek der TU München, 2013. http://d-nb.info/1033164224/34.

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"A hierarchical algorithm for limited-angle reconstruction." Massachusetts Institute of Technology, Laboratory for Information and Decision Systems], 1989. http://hdl.handle.net/1721.1/3110.

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Jerry L. Prince and Alan S. Willsky.
Caption title.
Includes bibliographical references.
Supported by the National Science Foundation. ECS-87-00903 Supported by the U.S. Army Research Office. DAAL03-86-K-0171
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Chang-Han, Tsai, and 蔡昌翰. "Image Reconstruction from Limited-Angle Data Sets." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/72794854240341933866.

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碩士
國立海洋大學
電機工程學系
87
Reconstruction of cross-section images from the projections of an object is a widely used image processing technique. Traditional application of image reconstruction is the X-ray computed tomography for medical imaging, which reconstructs cross sections from projections of human body through the process of computing devices. In recent years, computed tomography has found its success in various applications, such as electron microscopy, astronomy, nondestructive evaluation, and many others. However, in many cases it is not possible to collect projection data over a complete angular range of. This is the so-called limited-angle problem that is mainly caused by the size of the object under test. Lack of complete angular coverge in CT scanning renders most of the Fourier-based image reconstruction methods, such as filtered back-projection (FBP), ineffective. As a result, they usually produce severe artifacts and also degrade accuracy in reconstructed cross sections. The iterative reconstruction-reprojection (IRR) algorithm proposed by Medoff et al. is commonly employed to solve the limited-angle problem. However, lack of sufficient prior information makes IRR less effective in the performance improvement of reconstructed images. Besides, the IRR algorithm has slow convergence rate in a recursive fashion to regularize the limited-angle problem. Therefore, how to maximize the use of prior and accelerate the convergence of the IRR algorithm is the main goal of the thesis. To improve the performance of the IRR algorithm, flawless prototype image is incorporated and difference constraint is developed as additional constraints of prior information. In addition, the constraint in frequency domain is also incorporated to increase convergence rate. Thus the performance of the IRR algorithm in effectiveness and efficiency can be greatly improved.
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"A projection space map method for limited angle reconstruction." Massachusetts Institute of Technology, Laboratory for Information and Decision Systems], 1987. http://hdl.handle.net/1721.1/3035.

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Abstract:
Jerry L. Prince and Alan S. Willsky.
Caption title.
Includes bibliographical references.
Supported by the National Science Foundation. ECS-8312921 Supported by the U.S. Army Research Office. DAAG29-84-K-005 DAAL03-86-K-1071 Partially supported by a U.S. Army Research Office Fellowship.
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Hsin, Jing-Han, and 辛景翰. "Computed Tomography Reconstruction by Linear Programming from Limited Angle Projections." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/78367561199321421229.

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Chang, Chen-Hao, and 張宸豪. "Three-dimensional Image Reconstruction from Limited-angle Data in Diffraction Tomography." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/07407398661624199728.

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碩士
國立臺灣大學
生物產業機電工程學研究所
100
Image reconstruction from limited-angle data is an important issue in diffraction tomography (DT). The limitation of angular coverage usually occurs due to the physical constraints in measurement systems. Insufficient information will deteriorate the quality of reconstructed images. In our experimental setup, the angular range of the data scanning is limited. Therefore, in this research we developed a new reconstruction approach which consists of POCS and FISTA to resolve the limited-angle problems in DT. Besides, we compared the reconstructed results of three iterative algorithms, including the constrained iterative Fourier inversion method, projection onto convex sets-steepest descent (POCS-SD) and projection onto convex sets-fast iterative shrinkage-thresholding algorithm (POCS-FISTA). POCS-SD and POCS-FISTA utilize the total variation (TV)-minimization technique which is a kind of edge-preserving technique. According to the results of numerical simulation, the performance among these three iterative methods had little difference from noiseless limited-angle data. When Gaussian noise was present in the scattered field, the reconstructed results by POCS-FISTA were closest to the ideal values. Furthermore, both of POCS-FISTA and POCS-SD performed well on de-noising. On the contrary, the constrained iterative Fourier inversion method performed poorly about noise suppression. Finally, we have also successfully reconstructed the refractive index distribution of objects according to the experimental results. Moreover, the comparison of reconstructed results by different methods was consistent with the results of numerical simulation.
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Books on the topic "Limited-Angle reconstruction"

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Reconstruction Algorithm Characterization and Performance Monitoring in Limited-Angle Chromotomography. Storming Media, 2003.

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Book chapters on the topic "Limited-Angle reconstruction"

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Zhou, Bo, Xunyu Lin, and Brendan Eck. "Limited Angle Tomography Reconstruction: Synthetic Reconstruction via Unsupervised Sinogram Adaptation." In Lecture Notes in Computer Science, 141–52. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20351-1_11.

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Grünbaum, F. Alberto. "The Limited Angle Problem in Reconstruction from Projections." In Inverse Methods in Electromagnetic Imaging, 277–98. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-010-9444-3_18.

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Tam, K. C. "Limited-Angle Image Reconstruction in Non-Destructive Evaluation." In Signal Processing and Pattern Recognition in Nondestructive Evaluation of Materials, 205–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-83422-6_16.

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Hanson, Kenneth M., and George W. Wecksung. "Bayesian Approach to Limited-Angle Reconstruction in Computed Tomography." In Maximum-Entropy and Bayesian Spectral Analysis and Estimation Problems, 255–72. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3961-5_15.

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Fang, Lu. "Plenoptic Reconstruction." In Advances in Computer Vision and Pattern Recognition, 75–189. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-6915-5_4.

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AbstractEmpowered by advanced plenoptic sensing systems, light-field imaging becomes one of the most extensively used methods for capturing 3D views of a scene. In contrast to the traditional input to a 3D graphics system, namely, scenes consisting of pre-defined geometric primitives with different materials and sets of lights, the input to a light field is only a set of 2D images which are informative and cost effective. Unfortunately, due to the limited sensor resolution, existing systems must balance the spatial and angular resolution, i.e., one can obtain dense sampling images in the spatial dimension but only sparse sampling images in the angular (viewing angle) dimension or vice versa.
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Huang, Yixing, Alexander Preuhs, Günter Lauritsch, Michael Manhart, Xiaolin Huang, and Andreas Maier. "Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior." In Machine Learning for Medical Image Reconstruction, 101–12. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33843-5_10.

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Ben Yedder, Hanene, Majid Shokoufi, Ben Cardoen, Farid Golnaraghi, and Ghassan Hamarneh. "Limited-Angle Diffuse Optical Tomography Image Reconstruction Using Deep Learning." In Lecture Notes in Computer Science, 66–74. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32239-7_8.

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Hammernik, Kerstin, Tobias Würfl, Thomas Pock, and Andreas Maier. "A Deep Learning Architecture for Limited-Angle Computed Tomography Reconstruction." In Informatik aktuell, 92–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-54345-0_25.

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Yang, Guang, John H. Hipwell, Christine Tanner, David J. Hawkes, and Simon R. Arridge. "Joint Registration and Limited-Angle Reconstruction of Digital Breast Tomosynthesis." In Breast Imaging, 713–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31271-7_92.

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Wang, Ce, Haimiao Zhang, Qian Li, Kun Shang, Yuanyuan Lyu, Bin Dong, and S. Kevin Zhou. "Improving Generalizability in Limited-Angle CT Reconstruction with Sinogram Extrapolation." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 86–96. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87231-1_9.

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Conference papers on the topic "Limited-Angle reconstruction"

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Nikishkov, Yuri, Ekaterina Bostaph, and Andrew Makeev. "Nondestructive Inspection of Composite Structures based on Limited Angle X-ray Computed Tomography." In Vertical Flight Society 71st Annual Forum & Technology Display, 1–11. The Vertical Flight Society, 2015. http://dx.doi.org/10.4050/f-0071-2015-10262.

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New methods for nondestructive inspection and structural diagnostics of composites based on X-ray Computed Tomography led to improved evaluation of subsurface condition of composites. Currently, strict limitations related to generating X-ray projections all around the inspected object in a full CT scan prohibit CT application to large structures as the commercial industrial X-ray Computed Tomography systems utilize full 360-degree projection angle range for high-quality three-dimensional reconstruction of the inspected objects. The objective of this work is to assess the possibility for breaking through the current limits of X-ray Computed Tomography in order to enable high-fidelity nondestructive inspection of large aircraft structures. This work presents methods that use limited (less than 180-degree) number of angular projections for three-dimensional volume reconstruction. The limited-angle reconstructions are demonstrated on detection of the defects in composite articles pertinent to rotorcraft industry.
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Hori, K., and T. Hashimoto. "Direct image reconstruction using deep image prior in limited-angle SPECT." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD), 1. IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10658412.

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Peng, Junbo, Richard Qiu, Tonghe Wang, Xiangyang Tang, and Xiaofeng Yang. "Optimization-based image reconstruction for limited-angle dual-energy cone-beam CT." In Physics of Medical Imaging, edited by John M. Sabol, Shiva Abbaszadeh, and Ke Li, 83. SPIE, 2025. https://doi.org/10.1117/12.3047401.

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Lv, L., F. Weng, G. Chen, and Q. Huang. "A Deep Reconstruction Method for Limited-Angle and Low-Dose PET Imaging in Biology-Guided Radiotherapy." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD), 1. IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10657296.

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Gontarz, Michał, Wojciech Krauze, Vibekananda Dutta, and Małgorzata Kujawińska. "Missing Cone Problem Correction with Deep Learning Based Segmentation." In Digital Holography and Three-Dimensional Imaging, M2A.4. Washington, D.C.: Optica Publishing Group, 2024. http://dx.doi.org/10.1364/dh.2024.m2a.4.

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The missing cone problem comes from limited angle scanning in Holographic Tomography. It causes an object elongation along the optical axis. This paper proposes creating a mask of the object via segmentation of reconstruction.
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"Limited angle reconstruction with two dictionaries." In 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (2013 NSS/MIC). IEEE, 2013. http://dx.doi.org/10.1109/nssmic.2013.6829229.

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Kisner, Sherman J., Eri Haneda, Charles A. Bouman, Sondre Skatter, Mikhail Kourinny, and Simon Bedford. "Limited view angle iterative CT reconstruction." In IS&T/SPIE Electronic Imaging, edited by Charles A. Bouman, Ilya Pollak, and Patrick J. Wolfe. SPIE, 2012. http://dx.doi.org/10.1117/12.917781.

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Du, Nan, Yusheng Feng, and Artyom M. Grigoryan. "Image reconstruction from limited-angle range projections." In SPIE Medical Imaging, edited by Robert M. Nishikawa and Bruce R. Whiting. SPIE, 2013. http://dx.doi.org/10.1117/12.2007598.

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Malalla, Nuhad A. Y., Shiyu Xu, and Ying Chen. "Limited angle C-arm tomosynthesis reconstruction algorithms." In SPIE Medical Imaging, edited by Christoph Hoeschen, Despina Kontos, and Thomas G. Flohr. SPIE, 2015. http://dx.doi.org/10.1117/12.2081699.

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Deng, Xiaojuan, Xuehong Liu, and Hongwei Li. "Limited-angle CT Reconstruction with ℓp Regularization." In the Third International Symposium. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3364836.3364872.

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Reports on the topic "Limited-Angle reconstruction"

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Anirudh, R., H. Kim, K. Champley, J. J. Thiagarajan, and A. Mohan. Improving Limited Angle CT Reconstruction with a Robust GAN Prior. Office of Scientific and Technical Information (OSTI), September 2019. http://dx.doi.org/10.2172/1598955.

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