Дисертації з теми "Techniques IRM"
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Bonnet, Mathieu. "Angio-IRM des artères pulmonaires : choix techniques et optimisation." Bordeaux 2, 1994. http://www.theses.fr/1994BOR23048.
Thomassin-Naggara, Isabelle. "Etude des tumeurs annexielles du pelvis féminin en IRM fonctionnelle : mise au point des techniques et applications cliniques." Paris 11, 2008. http://www.theses.fr/2008PA112116.
The preoperative characterization of adnexal tumors is crucial for surgical care. Dynamic contrast enhanced MR imaging (DCE-MRI) allows an optimization of the tissue characterization. Adnexal tumors differ according to their dynamic curve enhancement, which reflect the immaturity of the vascular wall and the expression of VEGFR-2 on endothelial cells. For a quantitative analysis of enhancement, a turbo-FLASH sequence with high angles is better to get both optimal dynamic enhancement range and an almost linear relationship between the signal and the concentration of gadolinium. Initialization by a extended Kéty model stabilizes our two-compartmental model which allows the best description of exchanges between the capillary and the interstitial spaces. Using quantitative DCE MRI, malignant adnexal tumors display higher tissue perfusion and blood volume fraction than benign tumors. Finally, quantitative DCE-MRI is a suitable, non-invasive tool to assess physiological microvascular states and variations in normal myometrium, and could potentially be used to assess the role of the inner myometrium in assisted reproductive therapy
Dhouib, Dorra. "Stratégie de compression d'images IRM volumiques pour des communications sans fil." Poitiers, 2010. http://www.theses.fr/2010POIT2251.
Given the significant increase in medical imaging data generated by different types of procedures, reducing the size of the data compression of images has become indispensable. It can minimize the data space in the case of archiving (or storage) or in the case of treatment at a distance (telemedicine) in the latter case, to reduce transfer time and bandwidth. In these applications, compression is obviously preserve useful information. This is possible thanks to the redundancy of information which can be either spatial or frequency. In this context, different effective compression techniques have been developed in recent decades. However, currently, in the radiological services, only lossless compression is the preferred way. Despite the improvements and efforts made by the methods of lossless compression, lossy compression has become feasible. But this type of compression must be done under the supervision of a competent physician, and without having to introduce some distortion, which could cause a possible loss of useful clinical information and therefore significantly influence the diagnosis. Also during transmission, the channel noise may also introduce errors that are likely erroneous diagnosis. For this reason it is important to develop a strategy appropriate to compression, both to reduce the volume of data, and to resist transmission errors. To meet this dual requirement of a strategy based ROI coding and unequal protection of streams is developed in this thesis. The compression scheme based ROI and unequal protection of stream proposed consists of three stages. In a first step we propose to extract the region of interest by hierarchical segmentation of these regions using a segmentation method based on the technical guidelines of the watershed by markers, combined with the technique of active contours by level sets. The resulting regions are then selectively encoded by an encoder 3D wavelet transform based adaptive discrete form BISK3D, where the compression ratio of each region depends on the importance of this region and its usefulness in diagnosis. Indeed the region of interest which contains information useful in diagnosis is coded with "almost lossless" and the rest of the volume lost. The third step of our coding scheme is a strategy of unequal protection (UEP), which is to protect the region of interest with a Reed-Solomon error correcting code with a very high efficiency so as to provide robustness to errors and other regions with the same type of correction code but lower yield by region. Evaluation tests were finally carried out to see the impact of compression errors and then the impact of channel errors on the various streams. To study the performance of our coding strategy in the case of a Gaussian transmission channel, this strategy is compared with 3D SPIHT
Bakhous, Christine. "Modèles d'encodage parcimonieux de l'activité cérébrale mesurée par IRM fonctionnelle." Phd thesis, Université de Grenoble, 2013. http://tel.archives-ouvertes.fr/tel-00933426.
Habib, Dayane. "Diffusion de l'hélium-3 hyperpolarisé dans le tissu pulmonaire : évaluation par différentes techniques IRM." Phd thesis, Université Paris Sud - Paris XI, 2007. http://tel.archives-ouvertes.fr/tel-00435916.
Mellerio, Charles. "Optimisation des techniques avancées en IRM cérébrale dans la détection des lésions développementales épileptogènes." Thesis, Paris 5, 2014. http://www.theses.fr/2014PA05T025/document.
Focal cortical dysplasia type 2 (FCD2) is a common cause of intractable partial epilepsy surgically treatable. Their detection by MRI is an independent factor of good prognosis. The MR imaging diagnosis remains difficult with up to 40% negative MRI. Our main objective is to improve the detection of FCD2from conventional sequences, to assess the relevance of increased magnetic field and validate new tools for detection, in particular by identifying sulcal abnormalities associated with FCD2 automatically and visually. This study was carried out from one of the largest cohort of patients (> 80 patients) with histologically proven FCD2. The evaluation of the frequency of each MR signs showed that, although no abnormality is seen in 41% of cases, the different signs in patients with a positive MRI were never isolated and the combination of the 3 most suggestive signs of FCD2 (cortical thickening, bluring of the gray-white matter interface and "transmantle sign") was found in 27 patients (64%), indicating that MRI can be very suggestive. By increasing the magnetic field from 1.5 to 3T MRI detection rate is only slightly changed but characterization of FCD2 is improved thanks to a better visualization of the " transmantle sign " considered as a MR signature of FCD2. The automated sulcus analysis based on the calculation of a new parameter called "sulcal energy" identifies abnormal sulcal patterns in patients with FCD2 in the central region in comparison to healthy subjects. This result underlines the importance of the identification of sulci and could provide an additional criterion for detecting and locating the lesion in patients with negative MRI. Finally, the visual analysis of sulci by 3D reformatting of the cortex allowed us to describe a new MR sign of FCD2 in the central region: a sulcal pattern called the "Power Button Sign". Given its excellent reproducibility and specificity, it could be used as a new major diagnostic criterion of FCD2 in the central region. All these results contribute to the better understanding of the developmental processes involved in the pathophysiology of FCD2 and offers many opportunities for improving their MR detection
Poujol, Julie. "Techniques d'acquisitions et reconstructions IRM rapides pour améliorer la détection du cancer du sein." Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0143/document.
Breast cancer is nowadays the first cause of female cancer and the first cause of female death by cancer. Breast MRI is only performed in second intention when other imaging modalities cannot lead to a confident diagnosis. In high risk women population, breast MRI is recommended as an annual screening tool because of its higher sensitivity to detect breast cancer. Breast MRI needs contrast agent injection to visualize enhancing lesions and the diagnosis is mostly based on morphological analysis of these lesions. Therefore, an acquisition with high spatial resolution is needed. Despite the use of conventional MRI acceleration techniques, the volume of data to be acquired remains quite large and the temporal resolution of the exam is around one minute. This low temporal resolution may be the cause of the low specificity of breast MRI exam. Breast MRI with higher temporal resolution will allow the use of pharmacokinetic models to access physiological parameters and lesion specifications. The main aim of this work is to develop a MRI sequence allowing a flexible use of the acquired data at the reconstruction stage. On the one hand, the images can be reconstructed with a conventional reconstruction like the protocol used in clinical routine. On the other hand, the new MRI sequence will also allow the reconstruction of images with a higher temporal resolution allowing the use of pharmacokinetic models. The development of this sequence was done by modifying the acquisition order in the Fourier domain. A random acquisition of the Fourier domain will allow the reconstruction of sub-sampled domains acquired faster. We paid attention to fat suppression efficiency with this new Fourier domain acquisition order. Tests were performed on phantom, female volunteers and patients. These tests showed that the random acquisition did not impact the quality of images (MRI signal and lesion morphology) obtained by conventional reconstruction thus allowing the conventional diagnosis. The reconstructions of the sub-sampled Fourier domains were made using Compressed Sensing reconstructions to remove sub-sampling artifacts. These reconstructions were developed and tested on digital phantoms reproducing breast MRI. The potential of this new MRI acquisition was tested on an artificial enhancing breast lesion developed especially for this purpose
Poujol, Julie. "Techniques d'acquisitions et reconstructions IRM rapides pour améliorer la détection du cancer du sein." Electronic Thesis or Diss., Université de Lorraine, 2017. http://www.theses.fr/2017LORR0143.
Breast cancer is nowadays the first cause of female cancer and the first cause of female death by cancer. Breast MRI is only performed in second intention when other imaging modalities cannot lead to a confident diagnosis. In high risk women population, breast MRI is recommended as an annual screening tool because of its higher sensitivity to detect breast cancer. Breast MRI needs contrast agent injection to visualize enhancing lesions and the diagnosis is mostly based on morphological analysis of these lesions. Therefore, an acquisition with high spatial resolution is needed. Despite the use of conventional MRI acceleration techniques, the volume of data to be acquired remains quite large and the temporal resolution of the exam is around one minute. This low temporal resolution may be the cause of the low specificity of breast MRI exam. Breast MRI with higher temporal resolution will allow the use of pharmacokinetic models to access physiological parameters and lesion specifications. The main aim of this work is to develop a MRI sequence allowing a flexible use of the acquired data at the reconstruction stage. On the one hand, the images can be reconstructed with a conventional reconstruction like the protocol used in clinical routine. On the other hand, the new MRI sequence will also allow the reconstruction of images with a higher temporal resolution allowing the use of pharmacokinetic models. The development of this sequence was done by modifying the acquisition order in the Fourier domain. A random acquisition of the Fourier domain will allow the reconstruction of sub-sampled domains acquired faster. We paid attention to fat suppression efficiency with this new Fourier domain acquisition order. Tests were performed on phantom, female volunteers and patients. These tests showed that the random acquisition did not impact the quality of images (MRI signal and lesion morphology) obtained by conventional reconstruction thus allowing the conventional diagnosis. The reconstructions of the sub-sampled Fourier domains were made using Compressed Sensing reconstructions to remove sub-sampling artifacts. These reconstructions were developed and tested on digital phantoms reproducing breast MRI. The potential of this new MRI acquisition was tested on an artificial enhancing breast lesion developed especially for this purpose
Dou, Weibei. "Segmentation d'images multispectrales basée sur la fusion d'informations : application aux images IRM." Caen, 2006. http://www.theses.fr/2006CAEN2026.
Trébuchet, Guillaume. "Segmentation par contours actifs de séquences de vélocimétrie IRM : application aux artères carotides." Angers, 2013. https://tel.archives-ouvertes.fr/tel-00956813.
MRI Velocimetry is a useful modality to explore cardiovascular disease. The sequence of phase contrast MRI has the characteristic of providing both anatomical information and the physiological data, thus allow to measure geometric properties of vessels and blood flows. The purpose of this thesis is to automate the segmentation of vessels and velocimetry measurements, manual processing is inadequate to operate the MRI velocimetry for a diagnostic in clinical routine. The work led to propose a segmentation method based on active contours guided by an information region ("region-based" approach), unlike conventional approaches focusing only on cross-border regions ("edge-based" approach). This "region-based" approach was evaluated on data from a phantom made to provide an objective reference. A second evaluation was conducted on the basis of 28 carotid arteries (14 patients) manually segmented by an expert radiologist. The results of the "phantom" data show that the "edge-based" approach leads to an error in measuring the area of the lumen of the segmented carotid and extent linked flows, respectively 18. 4 % and 3. 6%. These errors are larger than those obtained using the proposed approach (respectively 2. 3 % and 0. 7 %). This benefit appears much higher on the database of patients with an underestimation of areas and blood flow, respectively 40. 5 % and 26. 5 % for the "edge-based" approach, against 14. 7 and 6. 4 % for the proposed approach
Bricq, Stéphanie. "Segmentation d’images IRM anatomiques par inférence bayésienne multimodale et détection de lésions." Université Louis Pasteur (Strasbourg) (1971-2008), 2008. https://publication-theses.unistra.fr/public/theses_doctorat/2008/BRICQ_Stephanie_2008.pdf.
Medical imaging provides a growing number of data. Automatic segmentation has become a fundamental step for quantitative analysis of these images in many brain diseases such as multiple sclerosis (MS). We focused our study on brain MRI segmentation and MS lesion detection. At first we proposed a method of brain tissue segmentation based on hidden Markov chains taking into account neighbourhood information. This method can also include prior information provided by a probabilistic atlas and takes into account the artefacts appearing on MR images. Then we extended this method to detect MS lesions thanks to a robust estimator and prior information provided by a probabilistic atlas. We have also developed a 3D MRI segmentation method based on statistical active contours to refine the lesion segmentation. The results were compared with other existing methods of segmentation, and with manual expert segmentations
Bâty, Xavier. "Recalage de séquences cardiaques spation-temporelles IRM et TEP/SCAN." Phd thesis, Université d'Angers, 2007. http://tel.archives-ouvertes.fr/tel-00346306.
Darwich, Mohamad Ayham. "Caractérisation locale des propriétés dynamiques artérielles par IRM haute résolution." Compiègne, 2010. http://www.theses.fr/2010COMP1868.
Arterial pathologies can often represent serious clinical situations which require an immediate medical examination. Although the modification the structure and the vascular geometry is relatively slow and is carried out during years, the consequences can appear, suddenly, fatal. This work represents a contribution to the methodology of the exploration of the movement of blood, and walls arterial for the characterization of arterial elasticity, and to measure the deterioration of the arterial walls in a noninvasive way. We present two protocols of dynamic imagery based on a bared echo of gradient sequence, with a high temporal resolution (- 5 ms). The relation between MR signal and liquid speed is elucidated via an experimental study, testing the influence of the parameters of imagery and the physical properties of circulation liquid. The reproducibility of MR signal behavior encouraged to propose an estimator based on the speed of blond. Further tests of the feasibility were carried out experimentally and on healthy volunteers, andvelocity results were compared with the technique of phase contrast. The application of the sequence on two close planes made it possible to carry out the second estimator based the speed of pulse wave. A pulsatile hydraulique circuit was implemented, and used to estimate this speed in vitro. The protocol was applied in vivo to estimate the speed of the blood pulse wave of the common carotid, with a distance of 5 cm between the planes. Results of these 2 estimators were experimentally verified, and reproducibility was tested
Scherrer, Benoit. "Segmentation des tissus et structures sur les IRM cérébrales : agents markoviens locaux et coopératifs et formulation bayésienne." Grenoble INPG, 2008. https://tel.archives-ouvertes.fr/tel-00361317.
Accurate magnetic resonance brain scan segmentation is critical in a number of clinical and neuroscience applications. This task is challenging due to artifacts, low contrast between tissues and inter-individual variability that inhibit the introduction of a priori knowledge. In this thesis, we propose a new MR brain scan segmentation approach. Unique features of this approach include (1) the coupling of tissue segmentation, structure segmentation and prior knowledge construction, and (2) the consideration of local image properties. Locality is modeled through a multi-agent framework: agents are distributed into the volume and perform a local Markovian segmentation. As an initial approach (LOCUS, Local Cooperative Unified Segmentation), intuitive cooperation and coupling mechanisms are proposed to ensure the consistency of local models. Structures are segmented via the introduction of spatial localization constraints based on fuzzy spatial relations between structures. In a second approach, (LOCUS-B, LOCUS in a Bayesian framework) we consider the introduction of a statistical atlas to describe structures. The problem is reformulated in a Bayesian framework, allowing a statistical formalization of coupling and cooperation. Tissue segmentation, local model regularization, structure segmentation and local affine atlas registration are then coupled in an EM framework and mutually improve. The evaluation on simulated and real images shows good results, and in particular, a robustness to non-uniformity and noise with low computational cost. Local distributed and cooperative MRF models then appear as a powerful and promising approach for medical image segmentation
Cros, Ghislaine. "Exploitation des propriétés magnétiques du sang en IRM : veinographie, imagerie fonctionnelle." Université Joseph Fourier (Grenoble), 1995. http://www.theses.fr/1995GRE10219.
Khotanlou, Hassan. "Segmentation 3D de tumeurs et de structures internes du cerveau en IRM." Phd thesis, Télécom ParisTech, 2008. http://pastel.archives-ouvertes.fr/pastel-00003662.
Urien, Hélène. "Détection et segmentation de lésions dans des images cérébrales TEP-IRM." Electronic Thesis or Diss., Paris, ENST, 2018. http://www.theses.fr/2018ENST0004.
The recent development of hybrid imaging combining Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) is an opportunity to exploit images of a same structure obtained simultaneously and providing complementary information. This also represents a real challenge due to the difference of nature and voxel size of the images. This new technology offers attractive prospects in oncology, and more precisely in neuro-oncology thanks to the contrast between the soft tissues provided by the MRI images. In this context, and as part of the PIM (Physics in Medicine) project of Paris-Saclay University, the goal of this thesis was to develop a multimodal segmentation pipeline adapted to PET and MRI images, including a tumor detection method in PET and MRI, and a segmentation method of the tumor in MRI. This process must be generic to be applied to multiple brain pathologies, of different nature, and for different clinical application. The first part of the thesis focuses on tumor detection using a hierarchical approach. More precisely, the detection method uses a new spatial context criterion applied on a max-tree representation of the MRI and PET images to select potential lesions. The second part presents a MRI tumor segmentation method using a variational approach. This method minimizes a globally convex energy function guided by PET information. Finally, the third part proposes an extension of the detection and segmentation methods developed previously to MRI multimodal segmentation, and also to longitudinal follow-up. The detection and segmentation methods were tested on images from several data bases, each of them standing for a specific brain pathology and PET radiotracer. The dataset used for PET-MRI detection and segmentation is composed of PET and MRI images of gliomas and meningiomas acquired from different systems, and images of brain lesions acquired on the hybrid PET-MRI system of Frédéric Joliot Hospital at Orsay. The detection method was also adapted to multimodal MRI imaging to detect multiple sclerosis lesions and follow-up studies. The results show that the proposed method, characterized by a generic approach using flexible parameters, can be adapted to multiple clinical applications. For example, the quality of the segmentation of images from the hybrid PET-MR system was assessed using the Dice coefficient, the Hausdorff distance (HD) and the average distance (AD) to a manual segmentation of the tumor validated by a medical expert. Experimental results on these datasets show that lesions visible on both PET and MR images are detected, and that the segmentation delineates precisely the tumor contours (Dice, HD and MD values of 0.85 ± 0.09, 7.28 ± 5.42 mm and 0.72 ± 0.36mm respectively)
Boisgontier, Hervé. "Détection automatique de changements en IRM de diffusion : application à la sclérose en plaques." Strasbourg, 2010. https://publication-theses.unistra.fr/public/theses_doctorat/2010/BOISGONTIER_Herve_2010.pdf.
Medical imaging brings important information to help physicians in their diagnosis and therapeutical decision and for the follow-up of patients. Medical image processing enables to assist physicians with the interpretation and the analysis of images and particularly for the longitudinal follow-up of patients. Diffusion Tensor Imaging (DTI) is a new technique that characterizes in vivo the water diffusion, especially in brain tissue. This thesis deals with the automatic detection of changes that can be observed between DTI acquisitions of patients suffering from multiple sclerosis. In this dissertation, we present statistical tools for detecting and analyzing intra-individual changes, adapted to both specificities of DTI and the follow-up of multiple sclerosis. Several information can be extracted from DTI acquisitions: the apparent diffusion coefficients, the diffusion tensor model and some indices characterizing diverse diffusion properties (mean diffusion, fractional anisotropy). The detection can be performed on these different kinds of representations. We present appropriated methods for each of these sets of images. These methods have been validated on simulated data and on a database of 21 patients suffering from multiple sclerosis
Tricot, Benoit. "Mise en place de techniques d'imagerie cardiaque par résonance magnétique chez le petit animal." Mémoire, Université de Sherbrooke, 2012. http://hdl.handle.net/11143/6367.
Bosc, Marcel. "Contribution à la détection de changements dans des séquences IRM 3D multimodales." Phd thesis, Université Louis Pasteur - Strasbourg I, 2003. http://tel.archives-ouvertes.fr/tel-00005163.
Passat, Nicolas. "Contribution à la segmentation des réseaux vasculaires cérébraux obtenus en IRM : Intégration de connaissance anatomique pour le guidage d'outils de morphologie mathématique." Université Louis Pasteur (Strasbourg) (1971-2008), 2005. https://publication-theses.unistra.fr/public/theses_doctorat/2005/PASSAT_Nicolas_2005.pdf.
Analysing cerebral MRA (magnetic resonance angiography) is a hard task for radiologists, because of the large size of the data and the increasing number of exams being performed. Creation of cerebral vessel segmentation methods from such images then constitutes a research area of great importance in medical imaging. This thesis is devoted to the development of such methods. It is especially focused on their ability to adapt their behaviour to the processed images and their semantic value. This concept of adaptivity is developed by considering high level anatomical knowledge which can be used for guidance of image processing tools. The first part of the presented work consists in proposing preliminary solutions for knowledge modelling. The atlas notion, which has already been successfully used for non vascular structures, is then developed. Two kinds of atlas are proposed, each one taking advantage of multi-modality (angiographic and morphologic) properties of the considered images in order to model anatomical knowledge elements related to brain vessels. The second part of the work deals with the development of segmentation methods using this knowledge for guiding mathematical morphology tools. These methods, based on region-growing, watershed, grey-level hit-or-miss transform and homotopic thinning, use the proposed atlases to fit or constraint the behaviour of these image processing tools with respect to the image properties. This thesis can be considered as an introduction to a new methodology of vascular structure segmentation, which tends to fuse the potential of the existing image processing tools with learning and knowledge based strategies which are generally only used by human specialists
Odille, Freddy. "Imagerie adaptative en IRM : utilisation des informations de mouvements physiologiques pour l’optimisation des processus d’acquisition et de reconstruction." Thesis, Nancy 1, 2007. http://www.theses.fr/2007NAN10107/document.
Magnetic resonance imaging (MRI) is a relatively slow imaging technique. In the context of cardiac and abdominal imaging, patient motion is a major impediment that disturbs the spatial encoding process needed to form an image. Motion results in image deteriorations, called artifacts. These artifacts can take complex forms as this encoding occurs in an unusual space (Fourier/coil sensitivity hybrid space). Generally the patient is asked for a breathhold in order to minimize the influence of respiration, and the acquisition is synchronized to the electrocardiogram in order to handle cardiac contraction. These methods are imperfect and not always applicable, and therefore alternative approaches are desirable. We propose to integrate prior knowledge in the acquisition and reconstruction processes, based on a specially designed platform, developed to acquire and analyze physiological data during the MRI examination. Various solutions are investigated to implement this adaptive imaging, with special care to the correction of motion induced spatial encoding errors. For that purpose, we build a predictive model that allows elastic displacement fields in the field of view to be predicted, from linear combinations of signals provided by the platform. Then we define a generalized reconstruction framework in which predicted displacement data are included, leading to the reconstruction of a motion-compensated image. The hypotheses are analyzed, and the predictive model, as well as the proposed reconstruction methods, are validated on real cardiac and abdominal data from healthy volunteers, in 2D and 3D free breathing scans
Gautier, Laurent. "Aide à la ségmentation d'images par la théorie des croyances : application aux séquences d'images IRM du rachis lombaire." Littoral, 2001. http://www.theses.fr/2001DUNK0057.
The current need for the fusion of data in image processing results directly from the multiplication of the data available starting from or of the systems of medical imagery which are used jointly to observe a same phenomenon under different aspects. The problem, which we pose in fusion data, can be expressed like a problem of decision on the truth or the probability of a proposal being given one or more information resulting from a same sensor or sensors different. With regard to the applications, it is a question of taking into account the vague, incomplete and dubious aspect of the data learned on each sensor and the redundant, complementary and conflict aspect of the whole of information. The complex characteristics of the informative systems must be introduced into all the stages of a process of fusion, from the assumptions the decision. The general goal of the thesis is a contribution to the segmentation 2D of images by belief theory applied to the sequences of images by Magnetic Resonance Imagery (MRI) of the lumbar rachis. The medical objective, in the long term, is the total and local analysis curve of the spinal column for the study of its deformations 3D starting from sequences obtained by MRI. Within the framework of this work, we were interested at the first stage : the segmentation of the vertebrae. The use of the traditional methods of segmentation did not enable us to obtain the contour of the vertebrae on images MRI acquisited. We then decided to exploit the contribution of the methods of data fusion to help us in the validation of the points resulting from the segmentation by active contour. For that, we propose a generic diagram of fusion data within the framework of our application. It diagram must allow of exploit the data exit of different level of analyze (at level of pixel, at level of contour) for extract the information the more reliable and the more exact in a goal of assistance with segmentation, in order to take in account the effect of partial volume dependent with protocol of acquisition MRI. For the architecture of fusion suggested, based on the theory of beliefs, we tried to justify the choice : a priori knowledge ; frame of discernement ; model of representation ; parameters essential to the discrimination of the starting assumptions ; strategy of fusion, distributed or global ; decision criteria. We discuss the validity of the found results, of the prospects considered and we finish for example of computation result of deformation 3D spinal column and vertebra
Soullié, Paul. "Développement méthodologique pour l’optimisation de l’imagerie des propriétés électromagnétiques en IRM." Electronic Thesis or Diss., Université de Lorraine, 2020. http://www.theses.fr/2020LORR0152.
It is now well accepted that electromagnetic (EM) properties of biological tissues are characteristic features related to their contents or their structure. It has been an old issue to seek for ways to estimate these properties in vivo, and has thus led the scientific community to develop numerous specific tools, from raw measurement technologies to imaging methods more recently. On a fundamental level, these works make it progressively possible to reveal some specific physiological mechanisms and are contributing to improve our understanding of the living. Recently, Magnetic Resonance Imaging (MRI) has become a privileged tool in this framework, allowing among other things the reconstruction of electrical and magnetic properties in different frequency ranges, with its distinctive resolution power. Recent progress suggest it could be possible to provide a tissue-specific electromagnetic MR contrast, that we consider as promising new biomarker from a clinical perspective. In a quest to give new insights for a better understanding of in vivo electromagnetic phenomena, as well as contributing to a comprehensive approach of EM modelling, we have endeavoured to develop an innovative electromagnetic mapping method with an MR scanner. In the MR community, the study of EM properties led to the development of two main research fields: the so-called “low-frequency imaging”, under 1 MHz, and the “high-frequency imaging”, above 50 MHz, depending on the device used for stimulation. Given both these approaches, we have considered strategies that avoid additional hardware, and that could provide qualitative as well as quantitative results in the context of a classical clinical examination then. Low-frequency methods have been evaluated with simulation tools and have been progressively dismissed for practical and theoretical reasons: in that frequency range, information is polluted by the noise. Conversely, we have developed a new mapping method for electrical properties in the high frequency range, built from existing methodologies. Importantly, we wanted to provide a method that could easily be translated to clinical applications at a reasonable computational cost. To that end, we first performed simulation studies, and then MR acquisitions with specific dedicated EM phantoms. We finally used volunteers’ in vivo data to assess the performance or our algorithm in a realistic context. Our reconstruction method fits particularly well with acquisition schemes based on gradient-recalled echo with ultrashort echo-times (UTE), or more dramatically with zero echo-time (ZTE). By isolating the local EM signature in the MR signal, we use them to provide quantitative maps of electromagnetic properties, and we are able to estimate the sensitivity of these reconstructed maps to our model parameters. Our simulation results first and foremost show that our method improves the overall theoretical reconstruction quality as compared to existing related mapping techniques. Qualitative results confirm the possibility of a direct distinction, in terms of contrast, between media with variables electromagnetic properties. Quantitative results are encouraging, we observe satisfactory absolute values for reconstructed EM properties in the given frequency range. Our framework contributes to the development of EM imaging in MRI, and gives new insights for reconstruction model optimization. Efforts are still needed to achieve better use of UTE/ZTE sequences and to improve the overall quality of our reconstructions. After final numerical optimization, the reproducibility of the method will be evaluated in several test organs before its integration to a standard clinical protocol
Boussion, Nicolas. "Localisation et extension des perturbations liées aux foyers épileptogènes par fusion multimodale fonctionnelle et morphologique : application aux techniques d'imagerie TEMP, TEP, IRM." Lyon 1, 2001. http://www.theses.fr/2001LYO1T269.
Taleb-Ahmed, Abdelmalik. "Étude de techniques de représentation 3D d'objets biologiques à partir d'acquisitions radiologiques X et IRM, applications en neuroradiologie et en morphogenèse céphalique." Lille 1, 1992. http://www.theses.fr/1992LIL10026.
Dewalle, Anne-Sophie. "Nouvelles approches de traitement du signal et de l'image en IRM fonctionnelle d'activation cérébrale." Lille 1, 2006. https://ori-nuxeo.univ-lille1.fr/nuxeo/site/esupversions/c3ff9e5f-e824-4cc2-a6ea-a22cb4383bc2.
Moussa, Richard. "Segmatation multi-agents en imagerie biologique et médicale : application aux IRM 3D." Thesis, Bordeaux 1, 2011. http://www.theses.fr/2011BOR14426.
Image segmentation is a crucial operation for image processing. It is always the starting point of shape analysis process, motion detection, visualization, and quantitative estimation of linear distances, surfaces and volumes. For this, the segmentation consists on classifying the voxels into classes based on their local strengths, their spatial location and shape characteristics or neighborhood. The difficulty of the results stability of segmentation methods for medical images comes from the different types of noise present inside every image. In these images, the noise takes two forms: a physical noise due to the acquisition system, in our case, MRI (Magnetic Resonance Imaging), and a physiological noise due to the patient. These noises should be considered for all methods of segmentation. In this thesis, we focused on Multi-Agent models based on the biological behavior of spiders and ants to perform the task of segmentation. For spiders, we proposed a semi-automatic method using the histogram of the image to determine the number of objects to be detected. As for ants, we proposed two approaches: one that uses the so-called classical gradient of the image and the second, more original, which uses an intervoxel partition of the image. We also proposed a way to speed up the segmentation process through the use of the GPU (Graphics Processing Unit). Finally, these two methods were evaluated on MR images of brain and were compared with conventional methods of segmentation: region growing and Otsu for the model of spiders and Sobel gradient for the ants
Lecocq, Angèle. "Optimisation des techniques non invasives d'IRM de perfusion cérébrale et d'imagerie spectroscopique par résonance magnétique pour l'exploration des pathologies cérébrales." Thesis, Aix-Marseille, 2014. http://www.theses.fr/2014AIXM5065.
Conventional MRI's lack of specificity in clinical routine limits our ability to perform correct diagnoses or follow-ups of pathological diseases. Two forms of NMR imaging, perfusion weighed and spectroscopic imaging provide information about two closely related characteristics :cerebral perfusion and metabolism. However, these techniques are not widely used due to the complexity of implementation and a lack of quantification.The general aim was to optimize and validate completely non-invasive NMR techniques for further human clinical applications in the context of exploring large cerebral volumes and determining absolute or pseudo-absolute quantification of cerebral perfusion and metabolism. Concerning perfusion, three arterial spin labeling sequences, PASL PICORE, PASL FAIR and pCASL, were compared in terms of sensitivity and reproducibility. The pCASL sequence was then integrated to a protocol applied to patients suffering from multiple sclerosis. In relation to metabolism, a protocol was applied in order to access absolute and pseudo-absolute metabolite quantification by water SI normalization from MRI proton density. This technique was validated on 2D CSI and then on 3D with EPSI sequence with two orientations, AC-PC and AC-PC+15 in order to generate reliable normative values of metabolites for the whole brain. The use of those spectroscopic techniques on patients suffering from multiple sclerosis allowed demonstrating the feasibility in clinic.This work demonstrates that reliable absolute quantification in perfusion weighted and spectroscopic imaging can be obtained with extensive coverage and with an acquisition time compatible with the reality of clinical exams
Urien, Hélène. "Détection et segmentation de lésions dans des images cérébrales TEP-IRM." Thesis, Paris, ENST, 2018. http://www.theses.fr/2018ENST0004/document.
The recent development of hybrid imaging combining Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) is an opportunity to exploit images of a same structure obtained simultaneously and providing complementary information. This also represents a real challenge due to the difference of nature and voxel size of the images. This new technology offers attractive prospects in oncology, and more precisely in neuro-oncology thanks to the contrast between the soft tissues provided by the MRI images. In this context, and as part of the PIM (Physics in Medicine) project of Paris-Saclay University, the goal of this thesis was to develop a multimodal segmentation pipeline adapted to PET and MRI images, including a tumor detection method in PET and MRI, and a segmentation method of the tumor in MRI. This process must be generic to be applied to multiple brain pathologies, of different nature, and for different clinical application. The first part of the thesis focuses on tumor detection using a hierarchical approach. More precisely, the detection method uses a new spatial context criterion applied on a max-tree representation of the MRI and PET images to select potential lesions. The second part presents a MRI tumor segmentation method using a variational approach. This method minimizes a globally convex energy function guided by PET information. Finally, the third part proposes an extension of the detection and segmentation methods developed previously to MRI multimodal segmentation, and also to longitudinal follow-up. The detection and segmentation methods were tested on images from several data bases, each of them standing for a specific brain pathology and PET radiotracer. The dataset used for PET-MRI detection and segmentation is composed of PET and MRI images of gliomas and meningiomas acquired from different systems, and images of brain lesions acquired on the hybrid PET-MRI system of Frédéric Joliot Hospital at Orsay. The detection method was also adapted to multimodal MRI imaging to detect multiple sclerosis lesions and follow-up studies. The results show that the proposed method, characterized by a generic approach using flexible parameters, can be adapted to multiple clinical applications. For example, the quality of the segmentation of images from the hybrid PET-MR system was assessed using the Dice coefficient, the Hausdorff distance (HD) and the average distance (AD) to a manual segmentation of the tumor validated by a medical expert. Experimental results on these datasets show that lesions visible on both PET and MR images are detected, and that the segmentation delineates precisely the tumor contours (Dice, HD and MD values of 0.85 ± 0.09, 7.28 ± 5.42 mm and 0.72 ± 0.36mm respectively)
Tor, díez Carlos. "Segmentation automatique de la surface corticale dans des IRM cérébrales des nouveaux-nés." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2019. http://www.theses.fr/2019IMTA0152/document.
Clinical studies for preterm infants (less than 32 weeks of gestation) emphasize the fact that an important part of the very or extreme preterm infants will present cognitive, motor or behavioral disorders. The clinical aim is to improve brain development studies and be able to detect and predict abnormalities in neonatal subjects. Among the medical imaging, MRI can provide non-invasive non-ionizing morphological 3D images with a spatial resolution of the order of a millimeter, properties that are well adapted to this issue. In addition, the segmentation of these images provides quantitative anatomical information, such as volume or shape. There are many existing methods for adult MRI that successfully segment brain subparts. However, these methods cannot be directly applied to the newborn, where the maturation of brain tissue modifies the contrasts in the image (for example, the non-myelination of the white matter). Moreover, factors related to the resolution together with structural fineness, especially in the cortex, induce partial volume effects in tissue boundaries. This thesis focuses on the segmentation of the cortical surface in neonatal infants using MR images, with satisfactory accuracy for further applications (such as the generation of surface meshes). In this thesis, we first focused on the so-called atlas or multi-atlas approaches. This family of methods is known for its effectiveness in brain segmentation, thanks to spatial priors that can be embedded in the model for guiding the segmentation. However, since the neonatal cortex is very thin, there are often discontinuities or wrong connections. In order to tackle this issue, a topological correction step is proposed to fill gaps and separate erroneous connections. The results emphasize the potential of these two types of approaches for this purpose
Duché, Quentin. "Étude des effets de volume partiel en IRM cérébrale pour l'estimation d'épaisseur corticale." Thesis, Rennes 1, 2015. http://www.theses.fr/2015REN1S035/document.
The work developed in this thesis is within the scope of magnetic resonance imaging (MRI) acquisition and image processing for the automated analysis of brain structures. The measurement of structural modifications with time such as cortical atrophy requires the application of image processing algorithms. They must compensate for MRI artifacts such as intensity inhomogeneities or partial volume (PV) effects to allow for brain tissues segmentation then cortical thickness estimation. We suggest a new PV model relying on the physics of acquisition named bi-exponential model that differs from the commonly used linear model by modelling brain tissues and image acquisition. It requires the use of two differently contrasted and perfectly coregistered images. This model has been validated with simulations and physical and digital phantoms in a first place. In parallel, the recent MP2RAGE sequence provides two coregistered images and their combination results in a bias-field corrected image as well as a T1 map of the scanned tissues. We tested our model with in vivo MP2RAGE data and demonstrated that using the linear PV model leads to a systematic gray matter proportion underestimation in PV voxels. These errors result in cortical thickness underestimation. Our results favor the following assumption: PV modelling with MP2RAGE images must differ from the usual linear PV model applied for images obtained from more classic sequences. The bi-exponential model is an adapted solution to this particular sequence
Lepagnot, Julien. "Conception de métaheuristiques pour l'optimisation dynamique : application à l'analyse de séquences d'images IRM." Phd thesis, Université Paris-Est, 2011. http://tel.archives-ouvertes.fr/tel-00674754.
Pontabry, Julien. "Construction d'atlas en IRM de diffusion : application à l'étude de la maturation cérébrale." Thesis, Strasbourg, 2013. http://www.theses.fr/2013STRAD039/document.
Diffusion weighted MRI (dMRI) is an in vivo imaging modality which raises a great interest in the neuro-imaging community. The intra-structural information of cerebral tissues is provided in addition to the morphological information from structural MRI (sMRI). These imaging modalities bring a new path for population studies, especially for the study in utero of the normal humanbrain maturation. The modeling and the characterization of rapid changes in the brain maturation is an actual challenge. For these purposes, this thesis memoir present a complete processing pipeline from the spatio-temporal modeling of the population to the changes analyze against the time. The contributions are about three points. First, the use of high order diffusion models within a particle filtering framework allows to extract more relevant descriptors of the fetal brain, which are then used for image registration. Then, a non-parametric regression technique was used to model the temporal mean evolution of the fetal brain without enforce a prior knowledge. Finally, the shape changes are highlighted using features extraction and selection methods
Duran, Audrey. "Intelligence artificielle pour la caractérisation du cancer de la prostate par agressivité en IRM multiparamétrique." Thesis, Lyon, 2022. http://theses.insa-lyon.fr/publication/2022LYSEI008/these.pdf.
Prostate cancer (PCa) is the most frequently diagnosed cancer in men in more than half the countries in the world and the fifth leading cause of cancer death among men in 2020. Diagnosis of PCa includes multiparametric magnetic resonance imaging acquisition (mp-MRI) - which combines T2 weighted (T2-w), diffusion weighted imaging (DWI) and dynamic contrast enhanced (DCE) sequences - prior to any biopsy. The joint analysis of these multimodal images is time demanding and challenging, especially when individual MR sequences yield conflicting findings. In addition, the sensitivity of MRI is low for less aggressive cancers and inter-reader reproducibility remains moderate at best. Moreover, visual analysis does not currently allow to determine the cancer aggressiveness, characterized by the Gleason score (GS). This is why computer-aided diagnosis (CAD) systems based on statistical learning models have been proposed in recent years, to assist radiologists in their diagnostic task, but the vast majority of these models focus on the binary detection of clinically significant (CS) lesions. The objective of this thesis is to develop a CAD system to detect and segment PCa on mp-MRI images but also to characterize their aggressiveness, by predicting the associated GS. In a first part, we present a supervised CAD system to segment PCa by aggressiveness from T2-w and ADC maps. This end-to-end multi-class neural network jointly segments the prostate gland and cancer lesions with GS group grading. The model was trained and validated with a 5-fold cross-validation on a heterogeneous series of 219 MRI exams acquired on three different scanners prior prostatectomy. Regarding the automatic GS group grading, Cohen’s quadratic weighted kappa coefficient (κ) is 0.418 ± 0.138, which is the best reported lesion-wise kappa for GS segmentation to our knowledge. The model has also encouraging generalization capacities on the PROSTATEx-2 public dataset. In a second part, we focus on a weakly supervised model that allows the inclusion of partly annotated data, where the lesions are identified by points only, for a consequent saving of time and the inclusion of biopsy-based databases. Regarding the automatic GS group grading on our private dataset, we show that we can approach performance achieved with the baseline fully supervised model while considering 6% of annotated voxels only for training. In the last part, we study the contribution of DCE MRI, a sequence often omitted as input to deep models, for the detection and characterization of PCa. We evaluate several ways to encode the perfusion from the DCE MRI information in a U-Net like architecture. Parametric maps derived from DCE MR exams are shown to positively impact segmentation and grading performance of PCa lesions
Randrianarisolo, Solofohery. "Estimation des déformations du ventricule gauche sur des séquences ciné-IRM non-marquées." Phd thesis, Université Paris-Est, 2009. http://tel.archives-ouvertes.fr/tel-00473769.
He, Rui. "Évaluation d'une analyse voxel à voxel dans l'accident vasculaire cérébral à partir d'images IRM multiparamétriques." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAS026/document.
Stroke is the leading cause of disability in adults. Beyond the narrow time window and possible risks of thrombolysis and mechanical thrombectomy, cell-therapies have strong potential. Reports showed that transplanted stem cells can enhance functional recovery after ischemic stroke in rodent models.To assess the mechanisms underlying the cell-therapy benefit after stroke, imaging is necessary. Multiparametric magnetic resonance imaging (MRI), including diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI), has become the gold standard to evaluate stroke characteristics. MRI also plays an important role in the monitoring of cerebral tissue following stroke from the acute to the chronic phase. However, the spatial heterogeneity of each stroke lesion and its dynamic reorganization over time, which may be related to the effect of a therapy, remain a challenge for traditional image analysis techniques. To evaluate the effect of new therapeutic strategies, spatial and temporal lesion heterogeneities need to be more accurately characterized and quantified.The current image analysis techniques, based on mean values obtained from regions of interest (ROIs), hide the intralesional heterogeneity. Histogram-based techniques provide an evaluation of lesion heterogeneity but fail to yield spatial information. The parametric response map (PRM) is an alternative, voxel-based analysis technique, which has been established in oncology as a promising tool to better investigate parametric changes over time at the voxel level which concern the therapeutic response or prognosis of disease.The PhD project was divided into two parts: a preclinical and a clinical study. The goal of the first study was to evaluate the PRM analysis using MRI data collected after the intravenous injection of human mesenchymal stem cells (hMSCs) in an experimental stroke model. The apparent diffusion coefficient (ADC), cerebral blood volume (CBV) and vessel size index (VSI) were mapped using 7T MRI. Two analytic procedures, the standard whole-lesion approach and the PRM, were performed on data collected at 4 time points in transient middle cerebral artery occlusion (MCAo) models treated with either hMSC or vehicle and in sham animals. During the second PhD project, 6 MR parametric maps (diffusion and perfusion maps) were collected in 30 stroke patients (the ISIS / HERMES clinical trial). MRI data, analyzed with both a classic mean value and a PRM approaches, were correlated with the evaluation of functional recovery after stroke measured with the National Institutes of Health Stroke Scale (NIHSS) and the modified Rankin Scale (mRS) at 4 time points.In both studies, PRM analysis of MR parametric maps reveals fine changes of the lesion induced by a cell therapy (preclincal study) and correlate with long-term prognosis (clinical study).In conclusion, the PRM analysis could be used as an imaging biomarker of therapeutic efficacy and of prognostic biomarker of stroke patients
Taquet, Jonathan. "Techniques avancées pour la compression d'images médicales." Phd thesis, Université Rennes 1, 2011. http://tel.archives-ouvertes.fr/tel-00629429.
Nempont, Olivier. "Modèles structurels flous et propagation de contraintes pour la segmentation et la reconnaissance d'objets dans les images : application aux structures normales et pathologiques du cerveau en IRM." Phd thesis, Télécom ParisTech, 2009. http://pastel.archives-ouvertes.fr/pastel-00005269.
Kang, Han. "Contribution to automatic corporal tissue classification by integrating qualitative medical knowledge : application to the analysis of musculo skeletal diseases and disabilities from MRI sequences." Valenciennes, 2009. http://ged.univ-valenciennes.fr/nuxeo/site/esupversions/127272e9-4071-4c58-8b9d-b80fd5395c78.
In the diagnosis using MRI images, image segmentation techniques plays a key role. Nevertheless, segmentation with context independent features such as grey level and texture often leads to unsatisfactory results because these general features can not take into account the specialized background knowledge. Therefore, it is necessary to incorporate our a priori knowledge on medical image analysis for obtaining better results of tissue classification. In this context, two main contributions have been proposed in order to improve FCM-based image segmentation quality. The first contribution is that we developed a new FCM-based algorithm for image segmentation. The second contribution is the development of an intelligent system for tissue classification. It consists of two steps. The first step is a specific tissue classification system of thigh. The second step is a generalized intelligent system for tissue classification
Forero, Vargas Manuel Guillermo. "Cartographies électriques cérébrales sur les surfaces réelles du scalp." Compiègne, 1996. http://www.theses.fr/1996COMPD913.
Roullier, Vincent. "Classification floue et modélisation IRM : application à la quantification de la graisse pour une évaluation optimale des risques pathologiques associés à l'obésité." Phd thesis, Université d'Angers, 2008. http://tel.archives-ouvertes.fr/tel-00348028.
Chevaillier, Béatrice. "Analyse de données d'IRM fonctionnelle rénale par quantification vectorielle." Phd thesis, Université de Metz, 2010. http://tel.archives-ouvertes.fr/tel-00557235.
Faraj, Achraf Al. "Biodistribution and biological impact of nanoparticles using multimodality imaging techniques : (Magnetic resonance imaging)." Phd thesis, Université Claude Bernard - Lyon I, 2009. http://tel.archives-ouvertes.fr/tel-00696221.
Bianchi, Andrea. "Magnetic resonance imaging techniques for pre-clinical lung imaging." Thesis, Bordeaux, 2014. http://www.theses.fr/2014BORD0060/document.
In this work, ultra-short echo time (UTE) Magnetic Resonance Imaging (MRI) sequences are investigated as flexible tools for the noninvasive study of experimental models of lung diseases in mice. In small animals radial UTE sequences can indeed efficiently limit the negative impact on lung image quality due to the fast spin dephasing caused by the multiple air/tissue interfaces. In addition, radial UTE sequences are less sensitive to motion artifacts compared to standard Cartesian acquisitions. As a result, radial UTE acquisitions can provide lung images in small animals at sub-millimetric resolution with significant signal to noise ratio in the lung parenchyma, while working with physiological conditions (freely-breathing animals). In this thesis, UTE proton MRI sequences were shown to be efficient instruments to quantitatively investigate a number of hallmarks in longitudinal models of relevant lung diseases with minimal interference with the lung pathophysiology, employing easilyimplementable fast protocols. The synergic use of positive contrast agents, along with anadvantageous administration modality, was shown to be a valuable help in the increase of sensitivity of UTE MRI. At the same time, UTE MRI was shown to be an extremely useful and efficacious sequence for studying positive contrast agents in lungs
Benaichouche, Ahmed Nasreddine. "Conception de métaheuristiques d'optimisation pour la segmentation d'images : application aux images IRM du cerveau et aux images de tomographie par émission de positons." Thesis, Paris Est, 2014. http://www.theses.fr/2014PEST1106/document.
Image segmentation is the process of partitioning a digital image into homogeneous non-overlapped regions with respect to some characteristics, such as gray value, motion, texture, etc. It is used in various applications like medical imaging, objects detection, biometric system, remote sensing, robot navigation, video surveillance, etc. The success of the machine vision system depends heavily on its performance, because characteristics and decisions are extracted and taken from its result. The first image segmentation algorithms were introduced in the 70's. Since then, various techniques and methods were experimented to improve the results. Nevertheless, up till now, no method produces a perfect result for a wide variety of images. Metaheuristics are a high level procedure designed to solve hard optimization problems. These problems are in general characterized by their incomplete, uncertain or noised data, or faced to low computing capacity. Metaheuristics have been extremely successful in a wide variety of fields and demonstrate significant results. This is due to the fact that they can applied to solve any problem which can be formulated as an optimization problem. These methods are, mainly, inspired from physics (simulated annealing), biology (evolutionary algorithms), or ethology (particle swarm optimization, ant colony optimization).In recent years, metaheuristics are starting to be exploited to solve segmentation problems with varying degrees of success and allow to consider the problem with different perspectives. Bearing this in mind, we propose in this work three segmentation and post-segmentation approaches based on mono or multiobjective optimization metaheuristics. The proposed methods were evaluated on databases containing synthetic images, simulated MRI images, real MRI images and PET images. The obtained results show the efficiency of the proposed ideas
Constantinides, Constantin. "Segmentation automatisée du ventricule gauche en IRM cardiaque : Evaluation supervisée et non supervisée de cette approche et application à l'étude de la viabilité myocardique." Electronic Thesis or Diss., Paris, ENST, 2012. http://www.theses.fr/2012ENST0034.
The aim of this work is to perform an automated segmentation of the Left Ventricle on short-axis cardiac MR images with as few user interactions as possible. Based on a recently developed semi-automated segmentation method, a fully automated segmentation method is proposed that includes three main steps: the heart localization, the definition of a region of interest around the left ventricle, and finally its segmentation. The algorithm developed here takes into account anatomic and functional a priori information such as the temporal features of the heartbeat, the pseudo-circular shape of the LV, and the 3D continuity, combined with the image intensity features. The segmentation process is achieved using deformable models combined with morphological filters, which improve the model performances when dealing with heterogeneous gray levels within the cavity. The work achieved within the MedIEval group (Medical Imaging Evaluation) allowed to compare both proposed methods with 6 other methods, including 3 manual delineations by experts. In particular, an approach for ranking segmentation methods without using a gold standard was applied to the ejection fractions estimated by the 8 methods. Finally, the proposed segmentation method was used in a clinical research work about the regional contraction and thequantification of the myocardial infarction extent.Future work includes the automated segmentation of the right ventricle as well as the estimation of a robust mutual shape from several segmentation methods
Lefeuvre, Jennifer. "Characterization of spinal cord lesions in the marmoset EAE model using MRI and histopathology techniques." Thesis, Sorbonne université, 2019. http://www.theses.fr/2019SORUS208.
Up to 90% of multiple sclerosis (MS) patients present spinal cord lesions. Magnetic resonance imaging (MRI) of spinal cord lesions is still a difficult challenge. Consequently, the evolution of spinal cord lesions in MS and their contribution to disease progression remain poorly understood. The brain of common marmoset with experimental autoimmune encephalomyelitis (EAE) displays closer radiological and pathological features as well sensori-motor deficits with MS. The objective of this thesis was to develop new MRI protocols at 7 Tesla in association with histopathological analysis to better characterize the type of spinal cord lesions in the marmoset EAE, and to understand their spatiotemporal evolution. A first postmortem study demonstrated a strong resemblance to MS focal lesions in terms of shape and distribution, as well a heterogeneous subpial pathology between animals and along the spinal cord length. Secondly, we implemented a robust in vivo experimental setup in order to adapt to the morphology of the animals and created a 12-element phase-array coil. This new setup enabled us to image for the first time the entire spinal cord of nonhuman primates with EAE during the disease. We also found a strong association between the lesion load and the disability scores. These new findings highlight the relevance of the spinal cord lesions in the marmoset EAE model for studying the disease mechanisms of spinal cord lesions in MS
Grigis, Antoine. "Approches statistiques pour la détection de changements en IRM de diffusion : application au suivi longitudinal de pathologies neuro-dégénératives." Phd thesis, Université de Strasbourg, 2012. http://tel.archives-ouvertes.fr/tel-00750933.
Troalen, Thomas. "IRM quantitative de la perfusion myocardique par marquage de spins artériels = Quantitative myocardial perfusion MRI using arterial spin labeling." Thesis, Aix-Marseille, 2014. http://www.theses.fr/2014AIXM5006/document.
Myocardial blood flow is an important factor of tissue viability and function. Diffuse changes in microcirculation preceding heart failure are involved in various cardiac pathologies. This work aim at improving existing techniques allowing quantitative and non-invasive myocardial perfusion assessment using arterial spin labeling. The first step of my work was to design an alternative approach to quantify myocardial blood flow in mice. The so called steady-pulsed ASL (spASL) is based on a regularly repeated pulsed labeling in order to build up a stationary regime of the magnetization under the influence of perfusion. The associated theoretical model has been developed in parallel to quantify tissue blood flow. We have shown that spASL allows to obtain similar results than the previously employed techniques, with the additional advantages of an increased sensitivity to the perfusion signal and a reduced acquisition time. A transfer towards clinical imaging for human applications was then undertaken. The spASL labeling scheme has been preserved while adapting the readout module to the specificities of cardiac MRI when applied to free-breathing human acquisitions. A dedicated post-processing, which includes a retrospective motion correction, has emerged subsequently to improve the robustness of our measurements. In parallel to the developments made for human studies, some optimization of the spASL technique when applied to rodent have been carried out depending on the conducted studies