Littérature scientifique sur le sujet « IRMf à échantillonnage compressif »
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Thèses sur le sujet "IRMf à échantillonnage compressif"
Chauffert, Nicolas. « Echantillonnage compressé le long de trajectoires physiquement plausibles en IRM ». Thesis, Paris 11, 2015. http://www.theses.fr/2015PA112234/document.
Texte intégralMagnetic Resonance Imaging~(MRI) is a non-invasive and non-ionizing imaging technique that provides images of body tissues, using the contrast sensitivity coming from the magnetic parameters (T$_1$, T$_2$ and proton density). Data are acquired in the $k$-space, corresponding to spatial Fourier frequencies. Because of physical constraints, the displacement in the $k$-space is subject to kinematic constraints. Indeed, magnetic field gradients and their temporal derivative are upper bounded. Hence, the scanning time increases with the image resolution. Decreasing scanning time is crucial to improve patient comfort, decrease exam costs, limit the image distortions~(eg, created by the patient movement), or decrease temporal resolution in functionnal MRI. Reducing scanning time can be addressed by Compressed Sensing~(CS) theory. The latter is a technique that guarantees the perfect recovery of an image from undersampled data in $k$-space, by assuming that the image is sparse in a wavelet basis. Unfortunately, CS theory cannot be directly cast to the MRI setting. The reasons are: i) acquisition~(Fourier) and representation~(wavelets) bases are coherent and ii) sampling schemes obtained using CS theorems are composed of isolated measurements and cannot be realistically implemented by magnetic field gradients: the sampling is usually performed along continuous or more regular curves. However, heuristic application of CS in MRI has provided promising results. In this thesis, we aim to develop theoretical tools to apply CS to MRI and other modalities. On the one hand, we propose a variable density sampling theory to answer the first inpediment. The more the sample contains information, the more it is likely to be drawn. On the other hand, we propose sampling schemes and design sampling trajectories that fulfill acquisition constraints, while traversing the $k$-space with the sampling density advocated by the theory. The second point is complex and is thus addressed step by step. First, we propose continuous sampling schemes based on random walks and on travelling salesman~(TSP) problem. Then, we propose a projection algorithm onto the space of constraints that returns the closest feasible curve of an input curve~(eg, a TSP solution). Finally, we provide an algorithm to project a measure onto a set of measures carried by parameterizations. In particular, if this set is the one carried by admissible curves, the algorithm returns a curve which sampling density is close to the measure to project. This designs an admissible variable density sampler. The reconstruction results obtained in simulations using this strategy outperform existing acquisition trajectories~(spiral, radial) by about 3~dB. They permit to envision a future implementation on a real 7~T scanner soon, notably in the context of high resolution anatomical imaging
Amor, Zaineb. « Non-Cartesian Sparkling encoding for High spatio-temporal resolution functional Magnetic Resonance Imaging (fMRI) at 7 Tesla and beyond ». Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPAST032.
Texte intégralFunctional MRI (fMRI) is currently one of the most commonly used functional neuroimaging techniques to probe brain activity non-invasively through the blood oxygen level-dependent (BOLD) contrast that reflects neurovascular coupling. It offers an interesting trade-off between spatial and temporal resolution in order to study the whole brain as an aggregation of intrinsic functional systems. The quest for higher spatial and/or temporal resolution in fMRI while preserving a sufficient temporal signal-to-noise ratio~(tSNR) has generated a tremendous amount of methodological contributions in the last decade ranging from Cartesian vs. non-Cartesian readouts, 2D vs. 3D acquisition strategies, parallel imaging and/or compressed sensing~(CS) accelerations and simultaneous multi-slice acquisitions to cite a few. In this work, we focus on the use of CS in fMRI; more specifically, we consider Spreading Projection Algorithm for Rapid K-space sampLING (SPARKLING) encoding scheme.The main focus and goal of this thesis involves the evaluation of 3D-SPARKLING as a viable acquisition scheme for high-resolution whole-brain fMRI. In this regard, we initially compared its capabilities with state-of-the-art 3D-EPI. After observing higher sensitivity to static and dynamic magnetic field imperfections in 3D-SPARKLING data, we established an experimental protocol to correct them. Finally, we studied the capabilities and limitations of employing a sliding-window reconstruction in combination with the SPARKLING encoding scheme to enhance temporal resolution during image reconstruction in fMRI retrospectively. A simulation study where the ground truth is controlled was conducted and demonstrated the possibility of detecting high-frequency oscillations in the BOLD signal and separating physiological noise from neural activity
Sipouo, Ngandjon Maurice. « Utilisation de l'échantillonnage compressif pour la détection des véhicules par un réseau de capteurs sans fil ». Mémoire, Université de Sherbrooke, 2012. http://hdl.handle.net/11143/5510.
Texte intégralChahid, Makhlad. « Echantillonnage compressif appliqué à la microscopie de fluorescence et à la microscopie de super résolution ». Thesis, Bordeaux, 2014. http://www.theses.fr/2014BORD0426/document.
Texte intégralMy PhD work deals with the application of Compressed Sensing (or CompressiveSampling, CS) in fluorescence microscopy as a powerful toolkit for fundamental biologicalresearch. The recent mathematical theory of CS has demonstrated that, for aparticular type of signal, called sparse, it is possible to reduce the sampling frequencyto rates well below that which the sampling theorem classically requires. Its centralresult states it is possible to losslessly reconstruct a signal from highly incompleteand/or inaccurate measurements if the original signal possesses a sparse representation.We developed a unique experimental approach of a CS implementation in fluorescencemicroscopy, where most signals are naturally sparse. Our CS microscopecombines dynamic structured wide-field illumination with fast and sensitive singlepointfluorescence detection. In this scheme, the compression is directly integratedin the measurement process. Additionally, we showed that introducing extra dimensions(2D+color) results in extreme redundancy that is fully exploited by CS to greatlyincrease compression ratios.The second purpose of this thesis is another appealing application of CS forsuper-resolution microscopy using single molecule localization techniques (e.g.PALM/STORM). This new powerful tool has allowed to break the diffraction barrierdown to nanometric resolutions. We explored the possibility of using CS to drasticallyreduce acquisition and processing times
Nguyen, Van Khieu. « MR microscopy of neuronal tissue : acquisition acceleration, modelling and experimental validation of water diffusion ». Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS086/document.
Texte intégralCompressed sensing (CS) exploits the compressibility of different types of images to reconstruct undersampled data without loss of information. The technique can be applied to MRI to reduce the acquisition times. The CS is based on three major components: (1) sparsity representation of the signal in some transform domain, (2) incoherent measurements, and (3) sparsity-constrained nonlinear reconstruction method. If the total number of points in the image is larger than four times the number of sparse coefficients, then the reconstruction of under sampled data is feasible. In the first results part of this thesis, we propose a new under sampling model based on the diffusion limited aggregation (DLA) theory and show that it performs better than the random variable under sampling method. The DLA under sampling model was used to implement the CS for T2-weighted and T1-weighted high resolution imaging at the ultra-high magnetic field (17.2T). In both cases, the acquisition time was reduced by 50% while maintaining the quality of the images in terms of spatial resolution, contrast to noise ratio, and signal intensity quantification. Both new CS pulse sequences (csRARE and csFLASH) were implemented in ParaVision 5.1 commercial software. The second results part of the thesis is focused on the study of the time-dependent diffusivity in the abdominal ganglion of Aplysia California. The Aplysia abdominal ganglion was chosen in this imaging study because high resolution MR imaging allows the fine anatomical description of the cellular network (size of individual neurons and orientation of axons). Using the Aplysia ganglia to study the relationship between the cellular structure and the diffusion MRI signal can shed light on this relationship for more complex organisms. We measured the dMRI signal at several diffusion times in the abdominal ganglion and performed simulations of water diffusion in geometries obtained after segmenting high resolution T2-weighted images and incorporating known information about the cellular structure from the literature. To match the dMRI signal in the single cell neurons with numerical simulations signal, the large cell outline was segmented from the anatomical T2 weighted image. Inside this cell shape, an irregularly shaped nucleus was manually generated (around 25-30% volume fraction). The small cells were modeled as small spheres with a smaller concentric spherical nucleus (around 25% volume fraction). The nerve was modeled by combining axons (cylinders) of different diameters consistent with the literature. The numerical dMRI signal can be simulated by solving Bloch-Torrey equation under the geometries domain described above. By fitting the experimental signal to the simulated signal for several types of cells such as: large cell neurons (diameter between 150 µm and 420 µm); cluster of small neuron cells gathered in the shape of a bag (up to 400 cells in adult Aplysia in each bag with cell size between 40 µm to 100 µm in diameter); and nerves (group of axons cylindrical shape diameter from less than 1 µm to 25 µm) at a wide range of diffusion times, we obtained estimates of the intrinsic diffusion coefficient in the nucleus and the cytoplasm (for cell neurons) and the intrinsic diffusion coefficient in the axons (for the nerves). We also evaluated the reliability of using an existing formula for the time-dependent diffusion coefficient to estimate cell size
Huang, Jianping. « Etude de l’imagerie de tenseur de diffusion en utilisant l’acquisition comprimée ». Thesis, Lyon, INSA, 2015. http://www.theses.fr/2015ISAL0136/document.
Texte intégralThe investigation of the micro fiber structures of the heart provides a new approach to explaining heart disease and investigating effective therapy means. Diffusion tensor magnetic resonance (DTMR) imaging or diffusion tensor imaging (DTI) currently provides a unique tool to image the three-dimensional (3D) fiber structures of the heart in vivo. However, DTI is known to suffer from long acquisition time, which greatly limits its practical and clinical use. Classical acquisition and reconstruction methods do not allow coping with the problem. The main motivation of this thesis is then to investigae fast imaging techniques by reconstructing high-quality images from highly undersampled data. The methodology adopted is based on the recent theory of compressed sensing (CS). More precisely, we address the use of CS for magnetic resonance imaging (MRI) and cardiac DTI. First, we formulate the magnetic resonance (MR) image reconstruction as a problem of optimization with data-driven tight frame (TF) and total generalized variation (TGV) constraints in the framework of CS, in which the data-driven TF is used to adaptively learn a set of filters from the highly under-sampled data itself to provide a better sparse approximation of images and the TGV is devoted to regularizing adaptively image regions and thus supprressing staircase effects. Second, we propose a new CS method that employs joint sparsity and rank deficiency prior to reconstruct cardiac DTMR images from highly undersampled k-space data. Then, always in the framework of CS theory, we introduce low rank constraint and total variation (TV) regularizations in the CS reconstruction formulation, to reconstruct cardiac DTI images from highly undersampled k-space data. Two TV regularizations are considered: local TV (i.e. classical TV) and nonlocal TV (NLTV). Finally, we propose two randomly perturbed radial undersampling schemes (golden-angle and random angle) and the optimization with low rank constraint and TV regularizations to deal with highly undersampled k-space acquisitons in cardiac DTI, and compare the proposed CS-based DTI with existing radial undersampling strategies such as uniformity-angle, randomly perturbed uniformity-angle, golden-angle, and random angle
Roux, Marine. « Inférence de graphes par une procédure de test multiple avec application en Neuroimagerie ». Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAT058/document.
Texte intégralThis thesis is motivated by the analysis of the functional magnetic resonance imaging (fMRI). The need for methods to build such structures from fMRI data gives rise to exciting new challenges for mathematics. In this regards, the brain connectivity networks are modelized by a graph and we study some procedures that allow us to infer this graph.More precisely, we investigate the problem of the inference of the structure of an undirected graphical model by a multiple testing procedure. The structure induced by both the correlation and the partial correlation are considered. The statistical tests based on the latter are known to be highly dependent and we assume that they have an asymptotic Gaussian distribution. Within this framework, we study some multiple testing procedures that allow a control of false edges included in the inferred graph.First, we theoretically examine the False Discovery Rate (FDR) control of Benjamini and Hochberg’s procedure in Gaussian setting for non necessary positive dependent statistical tests. Then, we explore both the FDR and the Family Wise Error Rate (FWER) control in asymptotic Gaussian setting. We present some multiple testing procedures, well-suited for correlation (resp. partial correlation) tests, which provide an asymptotic control of the FWER. Furthermore, some first theoretical results regarding asymptotic FDR control are established.Second, the properties of the multiple testing procedures that asymptotically control the FWER are illustrated on a simulation study, for statistical tests based on correlation. We finally conclude with the extraction of cerebral connectivity networks on real data set