Rozprawy doktorskie na temat „Imagerie médicale – Tomodensitométrie”
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Guerra, Rui. "Intégration des mouvements physiologiques en tomodensitométrie : estimation, reproduction et influence en imagerie cardiaque". Thesis, Vandoeuvre-les-Nancy, INPL, 2007. http://www.theses.fr/2007INPL003N/document.
Pełny tekst źródłaThe new idea presented in this work takes into account patient motion in the acquisition and reconstruction processes. For this work, the complete acquisition system has been developed in order to reproduce physiologic motion, analyse their effect and propose correction methods to reduce image artefacts. A new methodology based on Doppler Tissue Imaging was used to find the motion in three dimensions of several coronary artery segments. Based on these data, optimal temporal windows were defined for reconstruction and an analysis of the ideal temporal window in the heart cycle was proposed. Both motion models were then used the control of a motion platform and as input for computer simulations. A first analysis carried on coronary calcification showed the influence of respiratory motion. Estimation and correction of motions were then performed on CT raw data and simulated motion. This works shows that it is necessary to include motion in the acquisition/reconstruction algorithms in CT
Gondim, Teixeira Pedro Augusto. "Développement et amélioration des outils d'imagerie médicale pour la caractérisation des masses tumorales du système ostéo-articulaire". Thesis, Université de Lorraine, 2013. http://www.theses.fr/2013LORR0178/document.
Pełny tekst źródłaMedical imaging plays a major role in the identification, characterization and staging of tumor lesions of the musculoskeletal system. The vast majority of these neoplasms are benign and it is important to recognize and distinguish them from malignant lesions. Malignant lesions carry a worse prognosis and are usually treated aggressively. MRI is currently the method of choice for evaluating musculoskeletal tumors. Despite a high sensitivity for the detection of bone and soft tissue tumors, a large number of identified lesions remain indeterminate in origin after imaging work-up. In recent years, new functional imaging techniques, which allow tumor evaluation in a biochemical and cellular level, have emerged. These techniques such as perfusion, diffusion weighted imaging and MR spectroscopy, originally designed for the evaluation of brain tumors, began to be used for the evaluation of musculoskeletal neoplasms with promising preliminary results. Meanwhile, with the development of wide area-detector CT systems and contrast enhanced ultrasound (CEUS) new ways of assessing tumor perfusion became available in clinical practice. Functional imaging nevertheless remains largely inaccessible outside research oriented imaging centers. The clinical application of these new methods is hindered by various factors, which include the great histological heterogeneity of musculoskeletal tumors and patient related technical difficulties. In this project, the diagnostic performance of several functional imaging methods in clinical practice was assessed. Additionally tools for image quality improvement and artifact reduction were tested. Finally, the diagnostic performance of different perfusion methods (ultrasound, computed tomography and magnetic resonance imaging) was compared
Bérard, Philippe. "Réalisation d'un nouveau prototype combiné TEP/TDM pour l'imagerie moléculaire de petits animaux". Thèse, Université de Sherbrooke, 2010. http://savoirs.usherbrooke.ca/handle/11143/4289.
Pełny tekst źródłaZuluaga, Valencia Maria Alejandra. "Méthodes d'automatisation de la détection des lésions vasculaires dans des images de tomodensitométrie". Phd thesis, Université Claude Bernard - Lyon I, 2011. http://tel.archives-ouvertes.fr/tel-00860825.
Pełny tekst źródłaCedilnik, Nicolas. "Personnalisation basée sur l'imagerie de modèles cardiaques électrophysiologiques pour la planification du traitement de la tachycardie ventriculaire". Thesis, Université Côte d'Azur, 2020. http://www.theses.fr/2020COAZ4097.
Pełny tekst źródłaAcute infarct survival rates have drastically improved over the last decades, mechanically increasing chronic infarct related affections.Among these affections, ischaemic ventricular tachycardia (VT) is a particularly serious arrhythmia that can lead to the often lethal ventricular fibrillation. VT can be treated by radio frequency ablation of the arrhythmogenic substrate.The first phase of this long and risky interventional cardiology procedure is an electrophysiological (EP) exploration of the heart.This phase aims at localising the ablation targets, notably by inducing the arrhythmia in a controlled setting. In this work we propose to re-create this exploration phase in silico, by personalising cardiac EP models.We show that key information about infarct scar location and heterogeneity can be automatically obtained by a deep learning-based automated segmentation of the myocardium on computed tomography (CT) images.Our goal is to use this information to run patient-specific simulations of depolarisation wave propagation in the myocardium, mimicking the interventional cardiology exploration phase.We start by studying the relationship between the depolarisation wave propagation velocity and the left ventricular wall thickness to personalise an Eikonal model, an approach that can successfully reproduce periodic activation maps of the left ventricle recorded during VT.We then propose efficient algorithms to detect the repolarisation wave on unipolar electrograms (UEG), that we use to analyse the UEGs embedded in such intra-cardiac recordings.Thanks to a multimodal registration between these recordings and CT images, we establish relationships between action potential durations/restitution properties and left ventricular wall thickness.These relationships are finally used to parametrise a reaction-diffusion model able to reproduce interventional cardiologists' induction protocols that trigger realistic and documented VTs. inteinterventional cardiologists' induction protocols that trigger realistic and documented VTs
Santelli, Julien. "Nanoparticules multimodales à base de lanthanides pour l'imagerie biomédicale et le suivi de cellules mésenchymateuses". Thesis, Toulouse 3, 2018. http://www.theses.fr/2018TOU30042/document.
Pełny tekst źródłaThe objective of these works was to put into practice the use of lanthanide-based multimodal nanoparticles (NPs) for biomedical imaging in general and mesenchymal cells (MSCs) tracking in particular. To that purpose, two types of NPs have been used, both presenting a gadolinium oxysulfide (Gd2O2S) matrix allowing magnetic resonance imaging (MRI) and computed tomography. The addition of dopant elements brings fluorescence properties: europium (Gd2O2S :Eu3+) is more appropriate for an in vitro examination whereas the combination ytterbium/thulium (Gd2O2S :Yb3+/Tm3+) is more appropriate for an in vivo examination through the up-conversion process. First, we have demonstrated the possibility of visualizing those NPs over-time in a living organism with complementary methods (MRI and fluorescence). The complete study of their biodistribution and ways of elimination allowed us to highlight a hepatobiliary metabolization associated with a slow feces excretion. The labeling of a wide variety of cell types (lines and primary cells from different species) has also pointed out their potential as a universal cell tracer. Thereafter, we focused our research on mesenchymal cells tracking in a cell therapy context. Short, medium and long term biocompatibility was validated via a series of analyses (MTT, neutral red, wound healing and differentiation) and the reliability of the tracer was confirmed by detailed study of the cell labeling. Finally, after developing a custom-made system dedicated to up-conversion imaging in small animals, we were able to perform over-time tracking of those labeled cells after injection in a solid organ. We achieved multimodal imaging of the MSCs with MRI, computed tomography and up-conversion. Altogether, these results underline the potential of these nanoparticles for long term imaging in preclinical and/or clinical studies
Torfeh, Tarraf. "Automatisation du contrôle de qualité d'une installation d'imagerie de repositionnement en radiothérapie externe". Phd thesis, Université de Nantes, 2009. http://tel.archives-ouvertes.fr/tel-00426889.
Pełny tekst źródłaBouilhol, Gauthier. "Incertitudes et mouvement dans le traitement des tumeurs pulmonaires : De la radiothérapie à l’hadronthérapie". Thesis, Lyon, INSA, 2013. http://www.theses.fr/2013ISAL0131/document.
Pełny tekst źródłaThis PhD thesis focuses on the uncertainties and motion management in lung radiation therapy and particle therapy. Passive motion management techniques are considered. They consist in delivering the dose without any respiratory beam monitoring which may be difficult to set up or may introduce additional uncertainties. Clinical and methodological contributions about different treatment steps are proposed. First of all, computed tomography (CT) images for treatment planning must be carefully acquired in the presence of respiration-induced tumor motion. We assessed the impact of motion artifacts on the quality of treatment planning. We also proposed methodologies and recommendations about the optimization of 4D-CT acquisition parameters and an original method for automated motion artifact detection in 4D-CT images. Target delineation introduces one of the main source of uncertainties during radiation therapy treatment planning. We quantified inter-observer variations in the delineation of the gross tumor volume (GTV) and the internal target volume (ITV) using an original method in order to incorporate them in margin calculation. Reduction of motion uncertainties can be achieved by combining an abdominal pressure device with the immobilization system to reduce the amplitude of respiratory motion. We proposed a study to evaluate the usefulness of such a device according to the tumor location within the lung. Delivering the dose to the ITV implies an important exposure of healthy tissues along the tumor trajectory. An alternative strategy consists in irradiating the tumor in its time-averaged mean position, the mid-position. Margins are reduced compared with an ITV-based strategy while maintaining a correct tumor coverage. One part of the work consisted in participating in the implementation of a clinical trial in photon radiation therapy to compare the two strategies, ITV and mid-position. In the margin recipe proposed by van Herk, a Gaussian distribution of all combined errors is assumed. In most cases, respiratory motion has an asymmetric non-Gaussian distribution and the assumption may not be valid for strongly asymmetric tumor motions with a large amplitude. We proposed a numerical population-based model to incorporate asymmetry and non-Gaussianity of respiratory motion in margin calculation. Finally, when taking respiratory motion into account in particle therapy with safety margins, one must consider various parameters, particularly the dose deposit sensitivity to density variations. The last part is dedicated to a discussion on the defining of safety margins in order to optimally take into account respiratory motion
Viti, Mario. "Automated prediction of major adverse cardiovascular events". Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG084.
Pełny tekst źródłaThis research project is expected to be financed by a CIFRE scholarship in collaboration between GE Healthcare and CentraleSupelec. We are seeking to predict Major Adverse Cardiovascular Events (MACE). These are typically embolism and aneurisms in the aorta and the coronary arteries, that give rise respectively to interrupted blood flow to the heart and so a risk of infarctus, or major hemorrhage. Both are life-threatening. When a patient is brought to hospital for an alert (angina, etc), they will undergo an X-ray CAT scan, which can be more or less invasive. A major objective of this research is to utilize as well as possible the available information in the form of 3D images together with patient history and other data, in order to avoid needless, invasive, irradiating or dangerous exams, while simultaneously guaranteeing optimal care and the best possible clinical outcome. The proposed methodologies include image analysis, image processing, computer vision and medical imaging procedures and methods, that will be developed in partnership between GE Healthcare and the CVN lab of CENTRALE SUPELEC
Vilches, Freixas Gloria. "Dual-energy cone-beam CT for proton therapy". Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEI099/document.
Pełny tekst źródłaProton therapy is a promising radiation treatment modality that uses proton beams to treat cancer. Current treatment planning systems rely on an X-ray computed tomography (CT) image of the patient's anatomy to design the treatment plan. The proton stopping-power ratio relative to water (SPR) is derived from CT numbers (HU) to compute the absorbed dose in the patient. Protons are more vulnerable than photons to changes in tissue SPR in the beam direction caused by movement, misalignment or anatomical changes. In addition, inaccuracies arising from the planning CT and intrinsic to the HU-SPR conversion greatly contribute to the proton range uncertainty. In clinical practice, safety margins are added to the treatment volume to account for these uncertainties at the expense of losing organ-sparing capabilities. The use of dual-energy (DE) in proton therapy was first suggested in 2009 to better estimate the SPR with respect to single-energy X-ray imaging. The aim of this thesis work is to investigate the potential improvement in determining proton SPR using DE to reduce the uncertainty in predicting the proton range in the patient. This PhD work is applied to a new imaging device, the Imaging Ring (IR), which is a cone-beam CT (CBCT) scanner developed for image-guided radiotherapy (IGRT). The IR is equipped with a fast kV switching X-ray source, synchronized with a filter wheel, allowing for multi-energy CBCT imaging. The first contribution of this thesis is a method to calibrate a model for the X-ray source and the detector response to be used in X-ray image simulations. It has been validated experimentally on three CBCT scanners. Secondly, the investigations have evaluated the factors that have an impact on the outcome of the DE decomposition process, from the acquisition parameters to the post-processing. Both image- and projection-based decomposition domains have been thoroughly investigated, with special emphasis on projection-based approaches. Two novel DE decomposition bases have been proposed to estimate proton SPRs, without the need for an intermediate variable such as the effective atomic number. The last part of the thesis proposes an estimation of proton SPR maps of tissue characterization and anthropomorphic phantoms through DE-CBCT acquisitions with the IR. A correction for X-ray scattering has been implemented off-line, and a routine to linearly interpolate low-energy and high-energy sinograms from sequential and fast-switching DE acquisitions has been proposed to perform DE material decomposition in the projection domain with real data. DECT-derived SPR values have been compared with experimentally-determined SPR values in a carbon-ion beam
Xie, Bingqing. "Image-domain material decomposition in spectral photon-counting CT for medical applications". Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEI021.
Pełny tekst źródłaMaterial decomposition is a fundamental and primordial problem in spectral photon-counting X-ray CT (sCT). The present thesis focuses on the development of material decomposition methods using spectral and morphological information embedded in multi-energy sCT images. In this framework, three methods were developed. For the first method, by using bounded mass density, local joint sparsity and structural low-rank (DSR) in image domain, we achieve highly accurate decomposition of materials such as gadolinium, iodine and iron. The results on both numerical phantom and physical data demonstrated that the proposed DSR method leads to more accurate decomposition than usual pseudo-inverse method with singular value decomposition (SVD) and current popular sparse regularization method with L1-norm constraint. The second method works in a region-wise manner. It consists in optimizing basis materials based on spatio-energy segmentation of regions-of-interests (ROIs) in sCT images, reducing noise by averaging multi-energy spatial images, and performing a fine material decomposition involving optimized decomposition matrix, denoising regularization and sparsity regularization. The results on both digital and physical data showed that the proposed ROI-wise material decomposition method presents clearly higher reliability and accuracy compared to common decomposition methods based on total variation (TV) or L1-norm (lasso) regularization. In the third method, we propose the notion of super-energy-resolution (SER) sCT imaging, which is realized through establishing the relationship between simulation and physical phantoms by means of coupled dictionary learning in a pixel-wise way. The effectiveness of the proposed methods was validated on digital phantom, physical phantoms and in vivo data. The results showed that for the same decomposition method using lasso regularization, the proposed super-energy-resolution imaging presents much higher decomposition accuracy and detection ability compared to what can be provided by current sCT machine
La, Barbera Giammarco. "Learning anatomical digital twins in pediatric 3D imaging for renal cancer surgery". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT040.
Pełny tekst źródłaPediatric renal cancers account for 9% of pediatric cancers with a 9/10 survival rate at the expense of the loss of a kidney. Nephron-sparing surgery (NSS, partial removal of the kidney) is possible if the cancer meets specific criteria (regarding volume, location and extent of the lesion). Indication for NSS is relying on preoperative imaging, in particular X-ray Computerized Tomography (CT). While assessing all criteria in 2D images is not always easy nor even feasible, 3D patient-specific models offer a promising solution. Building 3D models of the renal tumor anatomy based on segmentation is widely developed in adults but not in children. There is a need of dedicated image processing methods for pediatric patients due to the specificities of the images with respect to adults and to heterogeneity in pose and size of the structures (subjects going from few days of age to 16 years). Moreover, in CT images, injection of contrast agent (contrast-enhanced CT, ceCT) is often used to facilitate the identification of the interface between different tissues and structures but this might lead to heterogeneity in contrast and brightness of some anatomical structures, even among patients of the same medical database (i.e., same acquisition procedure). This can complicate the following analyses, such as segmentation. The first objective of this thesis is to perform organ/tumor segmentation from abdominal-visceral ceCT images. An individual 3D patient model is then derived. Transfer learning approaches (from adult data to children images) are proposed to improve state-of-the-art performances. The first question we want to answer is if such methods are feasible, despite the obvious structural difference between the datasets, thanks to geometric domain adaptation. A second question is if the standard techniques of data augmentation can be replaced by data homogenization techniques using Spatial Transformer Networks (STN), improving training time, memory requirement and performances. In order to deal with variability in contrast medium diffusion, a second objective is to perform a cross-domain CT image translation from ceCT to contrast-free CT (CT) and vice-versa, using Cycle Generative Adversarial Network (CycleGAN). In fact, the combined use of ceCT and CT images can improve the segmentation performances on certain anatomical structures in ceCT, but at the cost of a double radiation exposure. To limit the radiation dose, generative models could be used to synthesize one modality, instead of acquiring it. We present an extension of CycleGAN to generate such images, from unpaired databases. Anatomical constraints are introduced by automatically selecting the region of interest and by using the score of a Self-Supervised Body Regressor, improving the selection of anatomically-paired images between the two domains (CT and ceCT) and enforcing anatomical consistency. A third objective of this work is to complete the 3D model of patient affected by renal tumor including also arteries, veins and collecting system (i.e. ureters). An extensive study and benchmarking of the literature on anatomic tubular structure segmentation is presented. Modifications to state-of-the-art methods for our specific application are also proposed. Moreover, we present for the first time the use of the so-called vesselness function as loss function for training a segmentation network. We demonstrate that combining eigenvalue information with structural and voxel-wise information of other loss functions results in an improvement in performance. Eventually, a tool developed for using the proposed methods in a real clinical setting is shown as well as a clinical study to further evaluate the benefits of using 3D models in pre-operative planning. The intent of this research is to demonstrate through a retrospective evaluation of experts how criteria for NSS are more likely to be found in 3D compared to 2D images. This study is still ongoing
Yu, Boliang. "3D analysis of bone ultra structure from phase nano-CT imaging". Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI016/document.
Pełny tekst źródłaOsteoporosis is a bone fragility disease resulting in abnormalities in bone mass and density. In order to prevent osteoporotic fractures, it is important to have a better understanding of the processes involved in fracture at various scales. As the most abundant bone cells, osteocytes may act as orchestrators of bone remodeling which regulate the activities of both osteoclasts and osteoblasts. The osteocyte system is deeply embedded inside the bone matrix and also called lacuno-canalicular network (LCN). Although several imaging techniques have recently been proposed, the 3D observation and analysis of the LCN at high spatial resolution is still challenging. The aim of this work was to investigate and analyze the LCN in human cortical bone in three dimensions with an isotropic spatial resolution using magnified X-ray phase nano-CT. We performed image acquisition at different voxel sizes of 120 nm, 100 nm, 50 nm and 30 nm in the beamlines ID16A and ID16B of the European Synchrotron Radiation Facility (ESRF - European Synchrotron Radiation Facility - Grenoble). Our first study concerned phase retrieval, which is the first step of data processing and consists in solving a non-linear inverse problem. We proposed an extension of Paganin’s method suited to multi-distance acquisitions, which has been used to retrieve phase maps in our experiments. The method was compared theoretically and experimentally to the contrast transfer function (CTF) approach for homogeneous object. The analysis of the 3D reconstructed images requires first to segment the LCN, including both the segmentation of lacunae and of canaliculi. We developed a workflow based on median filter, hysteresis thresholding and morphology filters to segment lacunae. Concerning the segmentation of canaliculi, we made use of the vesselness enhancement to improve the visibility of line structures, the variational region growing to extract canaliculi and connected components analysis to remove residual noise. For the quantitative assessment of the LCN, we calculated morphological descriptors based on an automatic and efficient 3D analysis method developed in our group. For the lacunae, we calculated some parameters like the number of lacunae, the bone volume, the total volume of all lacunae, the lacunar volume density, the average lacunae volume, the average lacunae surface, the average length, width and depth of lacunae. For the canaliculi, we first computed the total volume of all the canaliculi and canalicular volume density. Moreover, we counted the number of canaliculi at different distances from the surface of each lacuna by an automatic method, which could be used to evaluate the ramification of canaliculi. We reported the statistical results obtained on the different groups and at different spatial resolutions, providing unique information about the organization of the LCN in human bone in three dimensions
Nguyen, Ho Quang. "Material-driven mesh derived from medical images for biomechanical system : application on modeling of the lumbar spine". Thesis, Compiègne, 2016. http://www.theses.fr/2016COMP2313.
Pełny tekst źródłaLow back pain is a common health problem which impacts a large part of the population in industrialized countries. Over the years, numerical modeling has been widely studied to investigate the biomechanics of lumbar spine for strongly assisting clinicians in diagnosis and treatments of this spinal pathology. In recent years, there has been a growing interest in researching and developing patient specific computer modeling which has proven its ability to provide great promises for developing realistic model of individual subject. However, still the specificity of these models is not fully described or is often limited to patient geometry. In fact, few models consider appropriate material properties derived from tissue characterization obtained from medical images. Furthermore, patient specific models can be obtained with geometry and mechanical properties derived from CT, but few from MRI which is well-suited for examining soft tissues. Therefore, development of the high-fidelity, patient-specific finite element model of the lumbar spine still presents the challenge. In this context of patient-specific finite element modeling, mesh generation is a crucial issue which requires an accurate representation of the geometry with well-shaped and sized elements and a relevant distribution of materials. This work presents a methodology for patient-specific finite element modeling which takes both individualized geometry and material properties of biological structures into consideration. In this study, the mesh is driven by personalized material knowledge which is extracted from advanced medical imaging. Additionally, a user-friendly program including image processing, material-driven meshing and material properties assignment, named C3M for “Computed Material-driven Mesh Model”, has been developed to generate efficiently subject-specific FE models derived from medical images. This process is applied to generate a patient specific FE model of lumbar spine based on both MRI and CT images. This approach opens a new direction to improve the meshing process using material knowledge derived from medical images. The proposed model allows an accurate and straightforward assembly of vertebrae and IVDs considering both geometry and material properties reflecting patient-specificity
Hohweiller, Tom. "Méthodes de décomposition non-linéaire pour l'imagerie X spectrale". Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI097.
Pełny tekst źródłaSpectral tomodensitometry is a new emerging x-ray imaging modality. If the dual-energy principle was already known for quite some time, new developments on photon-counting detectors now allowing acquiring more energy bins than before. This modality allows reducing some artifacts presents in x-ray imaging, such as beam hardening, but mostly to decompose the data into the chemical composition of the imaged tissue. It also enables the use of new markers (i.e. gold) with an energic discontinuity. The use of these markers also allows to locate and quantify them in the patient, granting great potential for medical imaging. Decomposition in the projection domain followed by a tomographic reconstruction is a classical processing for those spectral data. However, decomposition methods in the projection domain are unstable for a high number of energy bins. Classical calibration technic is numerically unstable for more than two energy bins. This thesis aims to developed new material decomposition methods in the projections domains. After expressing the spectral forward model, the decomposition problem is expressed and dealt as a non-linear inverse problem. It will be solved by minimizing a cost function composed by a term characterizing the fidelity of the decomposition regarding the data and an \textit{a priori} of the decomposed material maps. We will firstly present an adaptation of the cost function that takes into account the Poissonian noise on the data. This formulation allows having better decomposed maps for a high level of noise than classical formulation. Then, two constrained algorithms will be presented. The first one, a projected Gauss-Newton algorithm, that enforces positivity on the decomposed maps, allows having better decomposed maps than an unconstrained algorithm. To improve the first algorithm, another one was developed that also used an egality constrain. The equality allows having images with fewer artifacts than before. These methods are tested on a numerical phantom of a mouse and thorax. To speed up the decomposition process, an automatic choice of parameters is presented, which allow faster decomposition while keeping good maps. Finally, the methods are tested on experimental data that are coming from a spectral scanner prototype
Dillenseger, Jean-Philippe. "Imagerie préclinique multimodale chez le petit animal : qualification des instruments et des méthodes (IRM, µTDM et µTEMP)". Thesis, Strasbourg, 2017. http://www.theses.fr/2017STRAD026/document.
Pełny tekst źródłaPreclinical imaging is mostly performed on mouse animal models (61%). It is a necessary step in preclinical research, in compliance the first two recommendations of the 3Rs rules (reduction, refinement and replacement). In order to give a biological significance to measurements extracted from in vivo-acquired mouse images, it is necessary to evaluate instruments performances but also experimental procedures involved. The qualification of apparatuses requires the use of specific phantoms while the evaluation of methods requires procedures tests on non-pathological animals before experimentations. The scope of this work was to develop tools and methods to qualify imaging instruments and in vivo procedures. The need for quantification in small animal imaging, leads us to consider preclinical imaging instruments as metrological tools; which means integrating measurement uncertainty into
Chevalier, Frédéric. "Le diagnostic assisté par ordinateur de l'image tomodensitométrique : étude de paramètres adaptés". Paris 12, 1991. http://www.theses.fr/1991PA120036.
Pełny tekst źródłaMarache-Francisco, Simon. "Évaluation de la correction du mouvement respiratoire sur la détection des lésions en oncologie TEP". Phd thesis, INSA de Lyon, 2012. http://tel.archives-ouvertes.fr/tel-00770662.
Pełny tekst źródłaRani, Kaddour. "Stratégies d’optimisation des protocoles en scanographie pédiatrique". Thesis, Université de Lorraine, 2015. http://www.theses.fr/2015LORR0282/document.
Pełny tekst źródłaFor the last 10-years, computed tomography (CT) procedures and their increased use have been a major source for concern in the scientific community. This concern has been the starting point for several studies aiming to optimize the dose while maintaining a diagnostic image quality. In addition, it is important to pay special attention to dose levels for children (age range considered to be from a newborn baby to a 16-y-old patient). Indeed, children are more sensitive to ionizing radiations, and they have a longer life expectancy. Optimizing the CT protocols is a very difficult process due to the complexity of the acquisition parameters, starting with the individual patient characteristics, taking into account the available CT device and the required diagnostic image quality. This PhD project is contributing to the advancement of knowledge by: (1) Developing a new approach that can minimize the number of testing CT scans examinations while developing a predictive mathematical model allowing radiologists to prospectively anticipate how changes in protocols will affect the image quality and the delivered dose for four models of CT scan. (2) Setting-up a Generic Optimized Protocol (based on the size of the phantom CATPAHN 600) for four models of CT scan. (3) Developing a methodology to adapt the GOP to five sizes of pediatric patient using Size Specific Dose Estimate calculation (SSDE). (4) Evaluating subjective and objective image quality between size-based optimised CT protocol and age-based CT protocols. (5) Developing a CT protocol optimization tool and a tutorial helping the radiologists in the process of optimization
Lesage, David. "Modèles, primitives et méthodes de suivi pour la segmentation vasculaire : application aux coronaires en imagerie tomodensitométrique 3D". Phd thesis, Télécom ParisTech, 2009. http://pastel.archives-ouvertes.fr/pastel-00005908.
Pełny tekst źródłaKhellaf, Feriel. "List-mode proton CT reconstruction". Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEI074.
Pełny tekst źródłaProton therapy is used for cancer treatment to achieve better dose conformity by exploiting the energy-loss properties of protons. Proton treatment planning systems require knowledge of the stopping-power map of the patient’s anatomy to compute the absorbed dose. In clinical practice, this map is generated through a conversion from X-ray computed tomography (CT) Hounsfield units to proton stopping power relative to water (RSP). This calibration generates uncertainties as photon and proton physics are different, which leads to the use of safety margins and the reduction of dose conformity. In order to reduce uncertainties, proton CT (pCT) was proposed as a planning imaging modality since the reconstructed quantity is directly the RSP. In addition to energy loss, protons also undergo multiple Coulomb scattering (MCS) inducing non-linear paths, thus making the pCT reconstruction problem different from that of X-ray CT. The objective of this thesis is to improve image quality of pCT list-mode reconstruction. The use of a most likely path (MLP) formalism for protons to account for the effects of MCS has improved the spatial resolution in pCT. This formalism assumes a homogeneous medium. The first contribution of this thesis is a study on proton paths in heteregeneous media: the accuracy of the MLP was evaluated against a Monte Carlo generated path in different heterogeneous configurations. Results in terms of spatial, angular, and energy distributions were analyzed to assess the impact on reconstruction. The second contribution is a 2D directional ramp filter used for pCT data reconstruction. An intermediate between a filtered backprojection and a backproject-filter approach was proposed, based on the extension of the usual ramp filter to two dimensions, in order to preserve the MLP spatial information. An expression for a band-limited 2D version of the ramp filter was derived and tested on simulated pCT list-mode data. Then, a comparison of direct reconstruction algorithms in terms of spatial resolution and RSP accuracy was conducted. Five algorithms, including the 2D directional ramp, were tested to reconstruct different simulated phantoms. Results were compared between reconstruction from data acquired using idealized or realistic trackers. Finally, the last contribution is a deconvolution method using the information on the MLP uncertainty in order to improve spatial resolution of pCT images
Li, Yufei. "Joint super-resolution/segmentation approaches for the tomographic images analysis of the bone micro-structure". Thesis, Lyon, 2018. http://www.theses.fr/2018LYSEI125/document.
Pełny tekst źródłaOsteoporosis is a disease characterized by loss of bone mass and degradation of bone microarchitecture. Although osteoporosis is not a fatal disease, the fractures it causes can lead to serious complications (damage to vessels and nerves, infections, stiffness), sometimes accompanied with risk of death. The bone micro-architecture plays an important role for the diagnosis of osteoporosis. Two common CT devices to scan bone micro architecture is High resolution-peripheral Quantitative CT and Micro CT. The former device gives access to in vivo investigation, but its spatial resolution is inferior. Micro CT gives better spatial resolution, but it is constrained to ex vivo measurement. In this thesis, we attempt to improve the spatial resolution of high resolution peripheral CT images so that the quantitative analysis of the resolved images is close to the one given by Micro CT images. We started from the total variation regularization, to a combination of total variation and double-well potential to enhance the contrast of results. Then we consider to use dictionary learning method to recover more structure details. Afterward, a deep learning method has been proposed to solve a joint super resolution and segmentation problem. The results show that the deep learning method is very promising for future applications
Hanzouli, Houda. "Analyse multi échelle et multi observation pour l'imagerie multi modale en oncologie". Thesis, Brest, 2016. http://www.theses.fr/2016BRES0126/document.
Pełny tekst źródłaThis thesis is a part of the development of more personalized and preventive medicine, for which a fusion of multi modal information and diverse representations of the same modality is needed in order to get accurate and reliable quantification of medical images in oncology. In this study we present two applications for image processing analysis: PET denoising and multimodal PET/CT tumor segmentation. The PET filtering approach called "WCD" take benefit from the complementary features of the wavelet and Curvelets transforms in order to better represent isotropic and anisotropic structures in PET images. This algorithm allows the reduction of the noise while minimizing the loss of useful information in PET images. The PET/CT tumor segmentation application is performed through a Markov model as a probabilistic quadtree graph namely a Hidden Markov Tree (HMT).Our motivation for using such a model is to provide fast computation, improved robustness and an effective interpretational framework for image analysis on oncology. Thanks to two efficient aspects (multi observation and multi resolution), when dealing with Hidden Markov Tree (HMT), we exploit joint statistical dependencies between hidden states to handle the whole data stack. This model called "WCHMT" take advantage of the high resolution of the anatomic imaging (CT) and the high contrast of the functional imaging (PET). The denoising approach led to the best trade-off between denoising quality and structure preservation with the least quantitative bias in absolute intensity recovery. PET/CT segmentation's results performed with WCHMT method has proven a reliable segmentation when providing high Dice Similarity Coeffcient (DSC) with the best trade-off between sensitivity (SE) and positive predictive value (PPV)
Lian, Chunfeng. "Information fusion and decision-making using belief functions : application to therapeutic monitoring of cancer". Thesis, Compiègne, 2017. http://www.theses.fr/2017COMP2333/document.
Pełny tekst źródłaRadiation therapy is one of the most principal options used in the treatment of malignant tumors. To enhance its effectiveness, two critical issues should be carefully dealt with, i.e., reliably predicting therapy outcomes to adapt undergoing treatment planning for individual patients, and accurately segmenting tumor volumes to maximize radiation delivery in tumor tissues while minimize side effects in adjacent organs at risk. Positron emission tomography with radioactive tracer fluorine-18 fluorodeoxyglucose (FDG-PET) can noninvasively provide significant information of the functional activities of tumor cells. In this thesis, the goal of our study consists of two parts: 1) to propose reliable therapy outcome prediction system using primarily features extracted from FDG-PET images; 2) to propose automatic and accurate algorithms for tumor segmentation in PET and PET-CT images. The theory of belief functions is adopted in our study to model and reason with uncertain and imprecise knowledge quantified from noisy and blurring PET images. In the framework of belief functions, a sparse feature selection method and a low-rank metric learning method are proposed to improve the classification accuracy of the evidential K-nearest neighbor classifier learnt by high-dimensional data that contain unreliable features. Based on the above two theoretical studies, a robust prediction system is then proposed, in which the small-sized and imbalanced nature of clinical data is effectively tackled. To automatically delineate tumors in PET images, an unsupervised 3-D segmentation based on evidential clustering using the theory of belief functions and spatial information is proposed. This mono-modality segmentation method is then extended to co-segment tumor in PET-CT images, considering that these two distinct modalities contain complementary information to further improve the accuracy. All proposed methods have been performed on clinical data, giving better results comparing to the state of the art ones
Quiñones, Catherine Thérèse. "Proton computed tomography". Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEI094/document.
Pełny tekst źródłaThe use of protons in cancer treatment has been widely recognized thanks to the precise stopping range of protons in matter. In proton therapy treatment planning, the uncertainty in determining the range mainly stems from the inaccuracy in the conversion of the Hounsfield units obtained from x-ray computed tomography to proton stopping power. Proton CT (pCT) has been an attractive solution as this modality directly reconstructs the relative stopping power (RSP) map of the object. The conventional pCT technique is based on measurements of the energy loss of protons to reconstruct the RSP map of the object. In addition to energy loss, protons also undergo multiple Coulomb scattering and nuclear interactions which could reveal other interesting properties of the materials not visible with the RSP maps. This PhD work is to investigate proton interactions through Monte Carlo simulations in GATE and to use this information to reconstruct a map of the object through filtered back-projection along the most likely proton paths. Aside from the conventional energy-loss pCT, two pCT modalities have been investigated and implemented. The first one is called attenuation pCT which is carried out by using the attenuation of protons to reconstruct the linear inelastic nuclear cross-section map of the object. The second pCT modality is called scattering pCT which is performed by utilizing proton scattering by measuring the angular variance to reconstruct the relative scattering power map which is related to the radiation length of the material. The accuracy, precision and spatial resolution of the images reconstructed from the two pCT modalities were evaluated qualitatively and quantitatively and compared with the conventional energy-loss pCT. While energy-loss pCT already provides the information needed to calculate the proton range for treatment planning, attenuation pCT and scattering pCT give complementary information about the object. For one, scattering pCT and attenuation pCT images provide an additional information intrinsic to the materials in the object. Another is that, in some studied cases, attenuation pCT images demonstrate a better spatial resolution and showed features that would supplement energy-loss pCT reconstructions
Bahig, Houda. "Rôle de la tomodensitométrie à double énergie/double source pour la personnalisation des traitements de radiothérapie". Thèse, 2018. http://hdl.handle.net/1866/22541.
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