Dissertations / Theses on the topic 'Reconstruction à angles limités'

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

Nous proposons dans cette thèse deux méthodes de reconstitution d'images tomographiques à partir d'un nombre limité de projections couvrant un angle de vue limité. Ces circonstances se rencontrent aussi bien en Imagerie Médicale, que dans d'autres domaines comme la microscopie électronique. Ces méthodes utilisent des informations a priori sur l'objet afin de retrouver les données manquantes. Nous avons appliqué une de ces méthodes aussi bien à reconstructions bidimensionnelles, que directement tridimensionnelles, et nous l'avons aussi utilisée dans deux cas concrets relevant du domaine des contrôles non destructifs et de la microimagerie plasma laser. Nous présentons aussi une étude des artéfacts paraissant sur les images tomographiques de Médecine Nucléaire à la suite de mauvaises conditions d'acquisition.

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.

Une des thématiques abordée par l'équipe Image et Vidéo-Communication est la reconstruction tomographique discréte à l'aide de la transformée Mojette. Ma thèse s'inscrit dans le cadre de la reconstruction tomographique médicale. La transformée Mojette est une version discrète exacte de la transformée de Radon qui est l'outil mathématique permettant la reconstruction tomographique. Pour évaluer la qualité des reconstructions, nous avons utilisé des fantômes numériques 2D simples (objet carré, rond) en absence puis en présence de bruit. Le coeur de mon travail de thèse est la reconstruction d'un objet à l'aide d'un algorithme de rétroprojection filtrée exacte Mojette en absence de bruit s'appuyant sur la géométrie discrète. Pour un nombre fini de projections dépendant de la taille de l'objet à reconstruire la reconstruction est exacte. La majorité des tomographes industriels utilisent l'algorithme de rétroprojection de projections filtrées (Filtered Back Projection ou FBP) pour reconstruire la région d'intérêt. Cet algorithme possède deux défauts théoriques, un au niveau du filtre utilisé, l'autre au niveau de la rétroprojection elle-même. Nous avons pu mettre au point un algorithme de Mojette FBP. Cet algorithme fait partie des méthodes directes de reconstruction. Il a aussi été testé avec succès en présence de bruit. Cet algorithme permet une équivalence continu-discret lors de la reconstruction. L'étape de projection/rétroprojection Mojette présente la particularité intéressante de pouvoir être décrit par une matrice Toeplitz bloc Toeplitz. Pour utiliser cette propriété nous avons mis en oeuvre un algorithme de gradient conjugué.

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

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

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

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.

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.

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.

Digital tomosynthesis (DTS) is a quasi-three-dimensional (3D) imaging technique which reconstructs images from a limited angle of cone-beam projections with shorter acquisition time, lower imaging dose, and less mechanical constraint than full cone-beam CT (CBCT). However, DTS images reconstructed by the conventional filtered back projection method have low plane-to-plane resolution, and they do not provide full volumetric information for target localization due to the limited angle of the DTS acquisition.

This dissertation presents the optimization and clinical implementation of image guided radiation therapy using limited-angle projections.

A hybrid multiresolution rigid-body registration technique was developed to automatically register reference DTS images with on-board DTS images to guide patient positioning in radiation therapy. This hybrid registration technique uses a faster but less accurate static method to achieve an initial registration, followed by a slower but more accurate adaptive method to fine tune the registration. A multiresolution scheme is employed in the registration to further improve the registration accuracy, robustness and efficiency. Normalized mutual information is selected as the criterion for the similarity measure, and the downhill simplex method is used as the search engine. This technique was tested using image data both from an anthropomorphic chest phantom and from head-and-neck cancer patients. The effects of the scan angle and the region-of-interest size on the registration accuracy and robustness were investigated. The average capture ranges in single-axis simulations with a 44° scan angle and a large ROI covering the entire DTS volume were between -31 and +34 deg for rotations and between -89 and +78 mm for translations in the phantom study, and between -38 and +38 deg for rotations and between -58 and +65 mm for translations in the patient study.

Additionally, a novel limited-angle CBCT estimation method using a deformation field map was developed to optimally estimate volumetric information of organ deformation for soft tissue alignment in image guided radiation therapy. The deformation field map is solved by using prior information, a deformation model, and new projection data. Patients' previous CBCT data are used as the prior information, and the new patient volume to be estimated is considered as a deformation of the prior patient volume. The deformation field is solved by minimizing bending energy and maintaining new projection data fidelity using a nonlinear conjugate gradient method. The new patient CBCT volume is then obtained by deforming the prior patient CBCT volume according to the solution to the deformation field. The method was tested for different scan angles in 2D and 3D cases using simulated and real projections of a Shepp-Logan phantom, liver, prostate and head-and-neck patient data. Hardware acceleration and multiresolution scheme are used to accelerate the 3D estimation process. The accuracy of the estimation was evaluated by comparing organ volume, similarity and pixel value differences between limited-angle CBCT and full-rotation CBCT images. Results showed that the respiratory motion in the liver patient, rectum volume change in the prostate patient, and the weight loss and airway volume change in the head-and-neck patient were accurately estimated in the 60° CBCT images. This new estimation method is able to optimally estimate the volumetric information using 60-degree projection images. It is both technically and clinically feasible for image-guidance in radiation therapy.

Dissertation

碩士
國立成功大學
生物醫學工程學系
105
Computed tomography (CT) is already an indispensable tool and widely applied in clinical diagnosis, so is cone beam computed tomography (CBCT) in maxillofacial, limb and dental applications. Radiation dose reduction of CBCT remains to be an active topic of research. In the literature, image reconstruction with limited angles can directly reduce the radiation dose, and the iterative reconstruction algorithms have superior performance on limited angles reconstruction. Although there are many kinds of iterative reconstruction algorithms, there is no systematic and objective comparison of these iterative reconstruction algorithms. In this work, we compared five common iterative reconstruction algorithms to the non-iterative algorithm, the L. A. Feldkamp, L. C. Davis, and J. W. Kress (FDK) algorithm, which is common in clinical use, to evaluate which iterative reconstruction method is preferred in limited angle reconstruction. Methods: To set up a prototype CBCT system, we integrated three hardware components: X-ray tube, flat-panel detector and a motor with in-house controlling software. Acquired projection data were used to reconstruct image volumes with tomographic iterative GPU-based reconstruction toolbox (TIGRE), an open source toolbox of various image reconstruction methods. A micro-CT bar pattern phantom and anolis sagrei cadaver were used as irradiated objects. We analyzed the reconstructed images with image quality criteria of signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), universal quality Index (UQI ), root mean of the squared error (RMSE) and full width at half maximum (FWHM). Results: Inspecting the coronal and sagittal planes of the reconstructed results, MLEM, OS-SART and OSC-TV were able to maintain a good image quality with limited angle reconstruction. In the micro-CT bar pattern phantom results, the top three best-performing algorithms are OS-SART, OSC-TV, and MLEM. In the anolis sagrei cadaver results, the best algorithms are OSC-TV, MLEM, and OS-SART. Conclusion: In the iterative reconstruction algorithms tested, MLEM, OS-SART and OSC-TV have superior performance than other algorithms. Since their performances in image quality vary between imaged objects, the choice of reconstruction algorithm may be task-dependent and requires further investigation. This study provides future studies with a framework of testing and evaluation for determining the ideal iterative reconstruction method in the CBCT system development.

碩士
淡江大學
資訊工程學系碩士班
99
Using some images to generate the 3D images is a research that receives the attention in multimedia area. Image-Based Rendering (IBR) is one kind of the technique. The main idea is to compute the geometric parameters of the scene from one or more images. After getting the parameters, it can model the scene and paste the suitable textures to generate the 3D effect. In this thesis, we use the concept of IBR to achieve the scene rotation. We shot several videos in the same scene, and we can compute the angle of each frame by our method. We compose all the videos and let user can change the angle when the video is playing. The result is very similarity to Google Map, it is an interactive video technique, and it will be the main stream in 2D-3D video processing area.