<p> Spherical Mean technique (SMT) is a novel method of quantifying the diffusion properties of the nerve fibers bundles in the central nervous system. It does this by calculating the spherical mean of the diffusion signal and fitting it to a parametric equation to obtain per voxel diffusion coefficients. We used Expectation&ndash;Maximization to obtain Gaussian Mixture Models (GMM) to find distinct clusters in per voxel coefficient space. We found that the diffusion properties of all the white matter fibers were clustered into a single Gaussian distribution in 867 brain volume samples. This implies that the diffusion properties of the white matter fibers are relatively homogeneous. Then, we checked this result by comparing the clusters obtained using GMM with tissue classification outputs obtained by clustering Fractional Anisotropy (obtained using Diffusion Tensor modeling), T1 weighted image intensity and B0 image intensity for 867 brain volume samples; we observed that the specific clusters of per voxel diffusion coefficients obtained using GMM represent specific tissue types (grey matter fibers, white matter fibers, cerebrospinal fluid). Since the parameters derived from SMT represent the physical diffusion properties that are independent of microscopic fiber orientation and the distribution of diffusion coefficients of white matter can be modeled by a single Gaussian distribution, we can conclude that the diffusion properties of all white matter fiber are homogeneous.</p><p>

L'évaluation de la carcinose péritonéale (CP) reste difficile et peu standardisée. Nous avons évalué différentes techniques de suivi de la CP en imagerie fonctionnelle, afin de mieux comprendre l'évolution microcirculatoire de la maladie, la valeur des différents biomarqueurs disponibles et essayer de construite une méthodologie d'évaluation multiparamétrique fonctionnelle.Nous avons établi un modèle murin orthotopique de pseudomyxome péritonéale (PMP). Nous avons suivi la perfusion tumorale par échographie Doppler de l'artère mésentérique supérieure (AMS), et montré une accélération des flux au sein de l'AMS accompagnant le développement de la tumeur, ainsi qu'une diminution de ces flux chez les souris traitées par bévacizumab.Puis, nous avons évalué la microcirculation au sein même de la tumeur. Nous avons ainsi pu montrer dans le modèle murin de PMP et dans un modèle de carcinose murine une bonne reproductibilité pour mesurer la valeur du coefficient apparent de diffusion dans la tumeur ainsi que les paramètres de microcirculation extraits par la méthode Intra-Voxe Incoherent Motion (IVIM) en utilisant une séquence d'imagerie par résonance magnétique (IRM) de type HASTE avec un équipement clinique standard à 1.5T. Nous avons ensuite appliqué la méthode IVIM pour le suivi de PMP de souris traitée par anti-angiogénique (sorafénib).L'application de ces méthodes d'imagerie fonctionnelle, combinées entre elles pourraient permettre de construire des modèles prédictifs et de suivi chez les patients atteints de PMP et de proposer des stratégies thérapeutiques personnalisées très précocement au cours de la maladie.5 IRM de diffusion6 Echographie doppler3 Imagerie fonctionnelle 74 Pseudomyxome péritonéal / Assessment of peritoneal carcinomatosis (PC) remains difficult and not well standardized. We have evaluated different techniques for monitoring CP using functional imaging, in order to better understand the microcirculation and the evolution of the disease, the value of various biomarkers available and we tries to establish methodology for functional multiparametric evaluation of the disease.We have established an orthotopic model of peritoneal pseudomyxoma (PMP). We have monitored the tumor perfusion by Doppler ultrasound of the superior mesenteric artery (SMA), and showed an increase in blood flow velocities in the SMA accompanying the development of the tumor, as well as a decrease of these flows in mice treated with bevacizumab.Then, we evaluated the microcirculation within the tumor itself. We have thus shown in the murine model of PMP and in a murine model of PC a good reproducibility of the measurement of the value of the apparent diffusion coefficient in the tumor as well as the microcirculation parameters extracted by the Intra-Voxel Incoherent Motion technique (IVIM) Using a HASTE magnetic resonance imaging (MRI) sequence with standard clinical equipment at 1.5T. We then applied the IVIM method for the monitoring of anti-angiogenic (sorafenib) -treated mouse PMP.The combined application of these functional imaging methods, may allow to construct predictive and follow-up models in patients with PMP and to propose personalized therapeutic strategies very early in the evolution of the disease.

Analogue sandbox models are important in the study of reservoir geology because they can offer insight into geological processes that we are rarely able to observe in nature. Seismic physical modelling is used to study the effects of seismic wave propagation in isotropic and anisotropic media and is particularly suited to isolating the effects of a single parameter independently from all others in an infinitely complex geological system. Seismic physical modelling has also been used for the testing of numerical processing algorithms, aid to evaluate interpretations of field seismic sections with scaled representations of geological formations. For this project, I set about developing methods to combine these two independent modelling techniques for the first time. However, previous attempts to use sand as a seismic modelling material failed due mainly to problems with understanding and controlling the distribution of the grain packing.This research has addressed a number of these problems through systematic laboratory experimentation that has provided new insight into the factors that affect unconsolidated sediment acoustics. An innovative technique of recording seismic physical modelling surveys has been developed so that it is now possible to successfully record ultrasonic reflections within analogue sandbox models in three-dimensions (3-D), providing benefits for both analogue sandbox and seismic modelling disciplines. For sandbox modelling, the recording of seismic images allows more detailed analyses of the structures than previously possible. For seismic modelling, more geologically realistic settings can be modelled at a fraction of the cost and construction time of conventional models. However, the greatest benefit of this new technology is that it is now possible to build seismic physical models from porous media, rather than solid, non-porous materials that ++ / are conventionally used. This scientific advance allows different fluids to be incorporated into physical models for the first time.Time-lapse 3-D seismic is becoming increasingly important in the management of hydrocarbon production, yet there is a lack of model data to support some of the conclusions being deduced. The controlled physical modelling laboratory environment combined with the ability to consistently repeat the 3-D seismic survey process now allows time-lapse seismic experiments to be performed without the need for the costly and time consuming data processing that is necessary to match legacy 3-D field data. This subsequently avoids any pitfalls that may be associated with the process, such as the masking of true fluid flow anomalies or the generation of false anomalies from data acquisition footprints.A series of time-lapse models are presented where the three-dimensional movement of fluids through the models is remotely monitored using time-lapse 3-D seismic data. These models demonstrate the true seismic response that comes from recording real data from models that undergo real changes representative of reservoir environments. Such models are inexpensive and allow rapid data turn around in a matter of days. The techniques developed here provide a new research tool that can be used to improve our understanding of the dynamics of fluid flow within porous sediments, or to study the seismic response of reservoirs as they change with time.

Most EIT machine operates at high frequencies above 10 kHz. Biological systems demonstrate dispersions of electrical impedance characteristics at very low frequencies below 2 kHz due to the presence of membrane surrounding the cells and diffusion polarisation effects. A study was made on the feasibility of the use of low frequencies in a range of 1.12 Hz to 4.55 kHz in EIT. One high frequency of 77.712 kHz similar to that normally used in common EIT was also used as a comparison. The impedance measurements employed a four-terminal method using the BULFIS, an ultra low frequency impedance spectrometer and used conducting and insulating material as the objects/phantoms. The results show that the conductance and capacitance of a metal object disperses at frequency range of 0.1 -10 kHz, which is consistent to the electrical properties of a double layer forming at the metal-electrolyte interface similar to the electrical properties of a membrane. The reconstructed images reveal that at low frequencies the conducting and the insulating bodies were indistinguishable. They appear differently at high frequencies above 4.55 kHz indicating that the use of multi frequency instrumentation in EIT covering the very low frequency range provides information that instrumentation restricted to frequencies above 10 kHz does not supply. While the internal structure of the double layers could not be delineated, the presence of the double layers could be readily detected by the behaviour of the images as the frequency was varied. This has potential for EIT because it might allow the detection of structures from the variation of the images with frequency. This variation with frequency does not occur at the higher frequencies more usually used for EIT.

Thesis (Ph. D.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

Bibliography: p. 257-269. The aim of this thesis was to consolidate and comment on the image measure literatures, and to find through experiment the salient properties of electronically displayed real world complex imagery that impacts on human performance. These experiments were carried out for well specified visual tasks and the appropriate application of image measures to this imagery, to predict human performance, was considered.

Medical imaging is nowadays a vital component of a large number of clinical applications. For comparing images of the same patient (sometimes acquired using different modalities) or for comparing different patients, the images need to be aligned. When images from the same patient or from a collection of patients are acquired using different modalities, their alignment is necessary. Registration is the process of finding the best alignment between a pair or a collection of images. The main topic of this thesis is the conception and the application of intensity-based techniques, both for pairwise and multi-subjects registration. In the context of pairwise registration, we have investigated the use of a stochastic gradient descent technique (SPSA) for optimizing the mutual information metric. For non-rigid registration, the use of volumetric tetrahedral meshes has been implemented as a deformation model in collaboration with A. du Bois d'Aische. The targeted application for our algorithm is the tracking of anatomical changes between pre-operative and intra-operative images in brain, prostate and liver surgery. A second method, equivalent to optical flow but developed for multi-modal images is also described and applied to the problem of atlas to pathological brain registration. In the context of multi-subjects registration, we developed an unbiased atlas generation technique in the Expectation-Maximization framework. At each iteration, the method estimates a reference for the registration problem by performing an average giving more weight to consistent experts (E step). The consistency parameters and the transformation parameters are estimated in the M step. Our atlas generation technique has been applied for aligning 80 brain images segmented in 4 labels (background, gray matter, white matter and ventricles).

The registration of pre-operative volumetric datasets to intra- operative two-dimensional images provides an improved way of verifying patient position and medical instrument loca- tion. In applications from orthopedics to neurosurgery, it has a great value in maintaining up-to-date information about changes due to intervention. We propose a mutual information- based registration algorithm to establish the proper align- ment. For optimization purposes, we compare the perfor- mance of the non-gradient Powell method and two slightly di erent versions of a stochastic gradient ascent strategy: one using a sparsely sampled histogramming approach and the other Parzen windowing to carry out probability density approximation. Our main contribution lies in adopting the stochastic ap- proximation scheme successfully applied in 3D-3D registra- tion problems to the 2D-3D scenario, which obviates the need for the generation of full DRRs at each iteration of pose op- timization. This facilitates a considerable savings in compu- tation expense. We also introduce a new probability density estimator for image intensities via sparse histogramming, de- rive gradient estimates for the density measures required by the maximization procedure and introduce the framework for a multiresolution strategy to the problem. Registration results are presented on uoroscopy and CT datasets of a plastic pelvis and a real skull, and on a high-resolution CT- derived simulated dataset of a real skull, a plastic skull, a plastic pelvis and a plastic lumbar spine segment.

Thesis (Ph. D.)--University of Rhode Island, 2005. / Typescript. Includes bibliographical references (leaves 58-60).

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.