Dissertations / Theses on the topic 'Multi-parametric magnetic resonance imaging'

A better understanding of tumour heterogeneity is central for accurate diagnosis, targeted therapy and personalised treatment of glioblastoma patients. This thesis aims to investigate whether pre-operative multi-parametric magnetic resonance imaging (MRI) can provide a useful tool for evaluating inter-tumoural and intra-tumoural heterogeneity of glioblastoma. For this purpose, we explored: 1) the utilities of habitat imaging in combining multi-parametric MRI for identifying invasive sub-regions (I & II); 2) the significance of integrating multi-parametric MRI, and extracting modality inter-dependence for patient stratification (III & IV); 3) the value of advanced physiological MRI and radiomics approach in predicting epigenetic phenotypes (V). The following observations were made: I. Using a joint histogram analysis method, habitats with different diffusivity patterns were identified. A non-enhancing sub-region with decreased isotropic diffusion and increased anisotropic diffusion was associated with progression-free survival (PFS, hazard ratio [HR] = 1.08, P < 0.001) and overall survival (OS, HR = 1.36, P < 0.001) in multivariate models. II. Using a thresholding method, two low perfusion compartments were identified, which displayed hypoxic and pro-inflammatory microenvironment. Higher lactate in the low perfusion compartment with restricted diffusion was associated with a worse survival (PFS: HR = 2.995, P = 0.047; OS: HR = 4.974, P = 0.005). III. Using an unsupervised multi-view feature selection and late integration method, two patient subgroups were identified, which demonstrated distinct OS (P = 0.007) and PFS (P < 0.001). Features selected by this approach showed significantly incremental prognostic value for 12-month OS (P = 0.049) and PFS (P = 0.022) than clinical factors. IV. Using a method of unsupervised clustering via copula transform and discrete feature extraction, three patient subgroups were identified. The subtype demonstrating high inter-dependency of diffusion and perfusion displayed higher lactate than the other two subtypes (P = 0.016 and P = 0.044, respectively). Both subtypes of low and high inter-dependency showed worse PFS compared to the intermediate subtype (P = 0.046 and P = 0.009, respectively). V. Using a radiomics approach, advanced physiological images showed better performance than structural images for predicting O6-methylguanine-DNA methyltransferase (MGMT) methylation status. For predicting 12-month PFS, the model of radiomic features and clinical factors outperformed the model of MGMT methylation and clinical factors (P = 0.010). In summary, pre-operative multi-parametric MRI shows potential for the non-invasive evaluation of glioblastoma heterogeneity, which could provide crucial information for patient care.

The work described in this thesis concentrates on two aspects of Proton NMR imaging: development and evaluation of new/old experimental sequences and application of those techniques to study some non-medical systems that are of industrial importance. Two-dimensional Fourier transform spin warp imaging technique has been evaluated. Importantly, the adaptation of a conventional high resolution spectrometer to perform imaging has been demonstrated with means of "phantoms". This includes calibration of magnetic field gradients, mapping the static magnetic field and radiofrequency field distributions and intensity measurements related to proton spin densities. In addition, a preliminary study describes microscopic imaging of glass capillary tube phantoms containing water. Several different sequences related to Chemical Shift imaging including the one developed during the study have been described. A brief insight into chemical shift artifacts as well as some experimental methods of minimizing some of them have also been presented. The potential of NMR imaging to study non-medical systems has been explored in three different areas of interest: Chromatography columns. Porous rock samples and Wood samples. A variety of NMR imaging sequences have been used to study some interesting and challenging features of these systems which clearly extends the scope of NMR imaging science.<br>Science, Faculty of<br>Chemistry, Department of<br>Graduate

Magnetic resonance imaging (MRI) is the preferred imaging modality for visualization of intracranial soft tissues. Surgical planning, and increasingly surgical navigation, use high resolution 3-D patient-specific structural maps of the brain. However, the process of MRI is a multi-parameter tomographic technique where high resolution imagery competes against high contrast and reasonable acquisition times. Resolution enhancement techniques based on super-resolution are particularly well suited in solving the problems of resolution when high contrast with reasonable times for MRI acquisitions are needed. Super-resolution is the concept of reconstructing a high resolution image from a set of low-resolution images taken at dierent viewpoints or foci. The MRI encoding techniques that produce high resolution imagery are often sub-optimal for the desired contrast needed for visualization of some structures in the brain. A novel super-resolution reconstruction framework for MRI is proposed in this thesis. Its purpose is to produce images of both high resolution and high contrast desirable for image-guided minimally invasive brain surgery. The input data are multiple 2-D multi-slice Inversion Recovery MRI scans acquired at orientations with regular angular spacing rotated around a common axis. Inspired by the computed tomography domain, the reconstruction is a 3-D volume of isotropic high resolution, where the inversion process resembles a projection reconstruction problem. Iterative algorithms for reconstruction are based on the projection onto convex sets formalism. Results demonstrate resolution enhancement in simulated phantom studies, and in ex- and in-vivo human brain scans, carried out on clinical scanners. In addition, a novel motion correction method is applied to volume registration using an iterative technique in which super-resolution reconstruction is estimated in a given iteration following motion correction in the preceding iteration. A comparison study of our method with previously published methods in super-resolution shows favorable characteristics of the proposed approach.

L'objectif de cette thèse est de proposer de nouveaux algorithmes pour surmonter les limitations actuelles et de relever les défis ouverts dans le traitement de l'imagerie spectroscopique par résonance magnétique (ISRM). L'ISRM est une modalité non invasive capable de fournir la distribution spatiale des composés biochimiques (métabolites) utilisés comme biomarqueurs de la maladie. Les informations fournies par l'ISRM peuvent être utilisées pour le diagnostic, le traitement et le suivi de plusieurs maladies telles que le cancer ou des troubles neurologiques. Cette modalité se montre utile en routine clinique notamment lorsqu'il est possible d'en extraire des informations précises et fiables. Malgré les nombreuses publications sur le sujet, l'interprétation des données d'ISRM est toujours un problème difficile en raison de différents facteurs tels que le faible rapport signal sur bruit des signaux, le chevauchement des raies spectrales ou la présence de signaux de nuisance. Cette thèse aborde le problème de l'interprétation des données d'ISRM et la caractérisation de la rechute des patients souffrant de tumeurs cérébrales. Ces objectifs sont abordés à travers une approche méthodologique intégrant des connaissances a priori sur les données d'ISRM avec une régularisation spatio-spectrale. Concernant le cadre applicatif, cette thèse contribue à l'intégration de l'ISRM dans le workflow de traitement en radiothérapie dans le cadre du projet européen SUMMER (Software for the Use of Multi-Modality images in External Radiotherapy) financé par la Commission européenne (FP7-PEOPLE-ITN)<br>The aim of this thesis is to propose new algorithms to overcome the current limitations and to address the open challenges in the processing of magnetic resonance spectroscopic imaging (MRSI) data. MRSI is a non-invasive modality able to provide the spatial distribution of relevant biochemical compounds (metabolites) commonly used as biomarkers of disease. Information provided by MRSI can be used as a valuable insight for the diagnosis, treatment and follow-up of several diseases such as cancer or neurological disorders. Obtaining accurate and reliable information from in vivo MRSI signals is a crucial requirement for the clinical utility of this technique. Despite the numerous publications on the topic, the interpretation of MRSI data is still a challenging problem due to different factors such as the low signal-to-noise ratio (SNR) of the signals, the overlap of spectral lines or the presence of nuisance components. This thesis addresses the problem of interpreting MRSI data and characterizing recurrence in tumor brain patients. These objectives are addressed through a methodological approach based on novel processing methods that incorporate prior knowledge on the MRSI data using a spatio-spectral regularization. As an application, the thesis addresses the integration of MRSI into the radiotherapy treatment workflow within the context of the European project SUMMER (Software for the Use of Multi-Modality images in External Radiotherapy) founded by the European Commission (FP7-PEOPLE-ITN framework)

Although prostate cancer is the most common cancer in men, it remains a difficult and controversial disease in terms of its diagnostic, risk stratification and treatment pathway. This is mainly due to the shortcomings of the standard diagnostic test, trans rectal ultrasound guided biopsy (TRUSBx), that is triggered following an elevated serum prostate specific antigen (PSA) test and the lack of agreement on disease thresholds that correlate to patient risk, if left untreated (and thus undetected). These factors often complicate the selection of the appropriate management that best fits the individual patient. In this doctoral thesis I propose, examine and validate a different approach that aims to shift the current diagnostic paradigm to that of incorporating an imaging test, multi-parametric magnetic resonance imaging (MP-MRI), prior to TRUS biopsy. First, I will discuss the nature of prostate cancer and highlight the shortcomings of the current diagnostic pathway and their implications. Second, I will analyze the shortcomings in early MP-MRI research that might have hindered its acceptance and adoption into the pathway and review the advances in research that occurred since I started my research. Third, I will discuss the rationale and methodological design considerations behind the PROstate Mri Imaging Study (PROMIS). PROMIS was a multicentre diagnostic paired validating confirmatory cohort study conducted to provide level 1b evidence on diagnostic accuracy of MP-MRI. It was designed to avoid the pitfalls identified in the current literature. I will discuss and analyze the design, conduct and results of the trial and its implications. Finally, I will discuss the wider implications of my work on the clinical practice of prostate cancer management and the future research opportunities made possible by the PROMIS data and its findings.

There are many clinical situations where magnetic resonance imaging (MRI) is preferable over other imaging modalities, while the major disadvantage is the relatively long scan time. Due to limited resources, this means that not all patients can be offered an MRI scan, even though it could provide crucial information. It can even be deemed unsafe for a critically ill patient to undergo the examination. In MRI, there is a trade-off between resolution, signal-to-noise ratio (SNR) and the time spent gathering data. When time is of utmost importance, we seek other methods to increase the resolution while preserving SNR and imaging time. In this work, I have studied one of the most promising methods for this task. Namely, constructing super-resolution algorithms to learn the mapping from a low resolution image to a high resolution image using convolutional neural networks. More specifically, I constructed networks capable of transferring high frequency (HF) content, responsible for details in an image, from one kind of image to another. In this context, contrast or weight is used to describe what kind of image we look at. This work only explores the possibility of transferring HF content from T1-weighted images, which can be obtained quite quickly, to T2-weighted images, which would take much longer for similar quality. By doing so, the hope is to contribute to increased efficacy of MRI, and reduce the problems associated with the long scan times. At first, a relatively simple network was implemented to show that transferring HF content between contrasts is possible, as a proof of concept. Next, a much more complex network was proposed, to successfully increase the resolution of MR images better than the commonly used bicubic interpolation method. This is a conclusion drawn from a test where 12 participants were asked to rate the two methods (p=0.0016) Both visual comparisons and quality measures, such as PSNR and SSIM, indicate that the proposed network outperforms a similar network that only utilizes images of one contrast. This suggests that HF content was successfully transferred between images of different contrasts, which improves the reconstruction process. Thus, it could be argued that the proposed multi-contrast model could decrease scan time even further than what its single-contrast counterpart would. Hence, this way of performing multi-contrast super-resolution has the potential to increase the efficacy of MRI.

Diffusion-weighted (DW) magnetic resonance imaging is an important neuroimaging technique that has successful applications in diagnosis of ischemic stroke and methods based on diffusion tensor imaging (DTI). Tensor measures have been used for detecting changes in tissue microstructure and for non-invasively tracing white matter connections in vivo. The most common image acquistion strategy is to use a DW single-shot echo-planar imaging (ss-EPI) pulse sequence, which is attractive due to its robustness to motion artefacts and high imaging speed. However, this sequence has limited achievable spatial resolution and suffers from geometric distortion and blurring artefacts. Readout-segmented echo-planar imaging (rs-EPI) is a DW sequence that is capable of acquiring high-resolution images by segmenting the acquisition of k- space into multiple shots. The fast, short readouts reduce distortion and blurring and the problem of artefacts due to motion-induced phase changes between shots can be overcome with navigator techniques. The rs-EPI sequence has two main shortcomings. (i) The method is slow to produce image volumes, which is limiting for clinical scans due to patient welfare and prevents us from acquiring very many directions in DTI. (ii) The sequence (like other diffusion techniques) is far from the optimum repetition time (TR) for acquiring data with the highest possible signal-to-noise ratio (SNR) in a given time. The work in this thesis seeks to address both of these important issues using a range of approaches. In Chapter 4 a partial Fourier extension is presented, which addresses point (i) by reducing the number of readout segments acquired and estimating the missing data. This allows reductions in scan time by approximately 40&percnt; and the reliability of the images is demonstrated in comparisons with the original images. The application of a simultaneous multi-slice scheme to rs-EPI, to address points (i) and (ii), is described in Chapter 5. Using the slice-accelerated rs-EPI sequence, tractography data were compared to ss-EPI data and high-resolution trace-weighted data were acquired in clinically relevant scan times. Finally, a 3D multi-slab extension that addresses point (i) is presented in Chapter 6. A 3D sequence could also allow higher resolution in the slice direction than 2D multi-slice methods, which are limited by the difficulties in exciting thin, accurate slices. A 3D version of rs-EPI was simulated and implemented and a k-space acquisition synchronised to the cardiac cycle showed substantial improvements in image artefacts compared to a conventional k-space acquisition.

Diffusion weighted magnetic resonance images offer unique insights into the neural networks of in vivo human brain. In this study, we investigate estimation and statistical analysis of multi-compartment models in high angular resolution diffusion imaging (HARDI) involving the Rician noise model. In particular, we address four important issues in multi-compartment diffusion model estimation, namely, the modelling of Rician noise in diffusion weighted (DW) images, the automatic determination of the number of compartments in the diffusion signal, the application of spatial prior on multi-compartment models, and the evaluation of parameter indeterminacy in diffusion models. We propose an expectation maximization (EM) algorithm to estimate the parameters of a multi-compartment model by maximizing the Rician likelihood of the diffusion signal. We introduce a novel scheme for automatically selecting the number of compartments, via a sparsity-inducing prior on the compartment weights. A non-local weighted maximum likelihood estimator is proposed to improve estimation accuracy utilizing repetitive patterns in the image. Experimental results show that the proposed algorithm improves estimation accuracy in low signal-to-noise-ratio scenarios, and it provides better model selection than several alternative strategies. In addition, we derive the Cram´er-Rao Lower Bound (CRLB) of the maximum Rician likelihood estimator for the balland-stick model and general differentiable diffusion models. The CRLB provides a general theoretical tool for comparing diffusion models and examining parameter indeterminacy in the maximum likelihood estimation problem.<br>published_or_final_version<br>Computer Science<br>Doctoral<br>Doctor of Philosophy

The eye lens grows continuously throughout life and changes its shape as the eye changes focus from a distant to a near object (the process of accommodation). These changes are complex because they may affect not only the shape of the lens, but also its refractive index distribution. To date there has been no satisfactory technique for directly and non-invasively measuring these changes. In this study the refractive index distribution through the isolated lens was measured non-invasively using a novel MRI technique. The dependence of the refractive index value of lens tissue on its transverse relaxation rate (R2) was determined empirically from measurements on lens homogenate samples. Using a multi-spin-echo imaging sequence, data were acquired for constructing R2 maps of a central slice through the isolated lens. These R2 maps were transformed to refractive index maps using the empirically determined dependence of refractive index on R2. Using a standard algorithm for ray tracing through gradient index media, the propagation of light rays through the index map were simulated. The optical properties of the lens, such as focal length, were then measured. The technique was validated by also directly measuring the focal length of each lens using laser ray tracing. The subtle changes in refractive index distribution that are responsible for the dramatic change in the optical properties of the isolated lens with age, were observed for the first time. The decrease in surface power of the isolated lens with age accounted only partially for the decrease in total lens power with age, the remainder resulting from a reduction in the gradient of refractive index (GRIN) power. It is likely that this reduction in GFUN power is the mechanism by which the eye maintains emmetropia (good distant vision) with age despite the increasing curvature of its surfaces. The reduction in the GRIN power of the lens was found to be mainly due to a flattening of the refractive index profile in the central region of the lens, accompanied by steepening of the profile near the edge of the lens. In agreement with a previous MRI study of the isolated human eye lens, this study found a decrease in the refractive index of the nucleus with age. However the age related change in this study was not as large and not found to be statistically significant. The results demonstrate that existing simple models for the optics of the eye lens are inadequate to accurately describe its properties. Several more sophisticated models were considered in an attempt to describe better the age-dependent changes that occur in both the power of the lens and its longitudinal aberration. Mathematical modelling was also used to simulate the accommodative process and investigate possible changes in the index distribution of the lens that may occur with accommodation. A preliminary in vivo study was performed aimed at observing the change in the refractive index distribution of the eye lens with age and accommodation. These results demonstrated the feasibility of the technique for in vivo applications and showed that within experimental error there is little change in the central refractive index of the lens with age. However the resolution achievable with standard clinical imaging sequences and signal detection hardware was not optimal for in vivo refractive index mapping of changes in the human eye lens with accommodation. Finally therefore, methods for refining the technique for in vivo applications are discussed which may make it possible to directly and simultaneously measure both the shape and refractive index distribution of the lens with age and accommodation.

The eye lens grows continuously throughout life and changes its shape as the eye changes focus from a distant to a near object (the process of accommodation). These changes are complex because they may affect not only the shape of the lens, but also its refractive index distribution. To date there has been no satisfactory technique for directly and non-invasively measuring these changes. In this study the refractive index distribution through the isolated lens was measured non-invasively using a novel MRI technique. The dependence of the refractive index value of lens tissue on its transverse relaxation rate (R2) was determined empirically from measurements on lens homogenate samples. Using a multi-spin-echo imaging sequence, data were acquired for constructing R2 maps of a central slice through the isolated lens. These R2 maps were transformed to refractive index maps using the empirically determined dependence of refractive index on R2. Using a standard algorithm for ray tracing through gradient index media, the propagation of light rays through the index map were simulated. The optical properties of the lens, such as focal length, were then measured. The technique was validated by also directly measuring the focal length of each lens using laser ray tracing. The subtle changes in refractive index distribution that are responsible for the dramatic change in the optical properties of the isolated lens with age, were observed for the first time. The decrease in surface power of the isolated lens with age accounted only partially for the decrease in total lens power with age, the remainder resulting from a reduction in the gradient of refractive index (GRIN) power. It is likely that this reduction in GFUN power is the mechanism by which the eye maintains emmetropia (good distant vision) with age despite the increasing curvature of its surfaces. The reduction in the GRIN power of the lens was found to be mainly due to a flattening of the refractive index profile in the central region of the lens, accompanied by steepening of the profile near the edge of the lens. In agreement with a previous MRI study of the isolated human eye lens, this study found a decrease in the refractive index of the nucleus with age. However the age related change in this study was not as large and not found to be statistically significant. The results demonstrate that existing simple models for the optics of the eye lens are inadequate to accurately describe its properties. Several more sophisticated models were considered in an attempt to describe better the age-dependent changes that occur in both the power of the lens and its longitudinal aberration. Mathematical modelling was also used to simulate the accommodative process and investigate possible changes in the index distribution of the lens that may occur with accommodation. A preliminary in vivo study was performed aimed at observing the change in the refractive index distribution of the eye lens with age and accommodation. These results demonstrated the feasibility of the technique for in vivo applications and showed that within experimental error there is little change in the central refractive index of the lens with age. However the resolution achievable with standard clinical imaging sequences and signal detection hardware was not optimal for in vivo refractive index mapping of changes in the human eye lens with accommodation. Finally therefore, methods for refining the technique for in vivo applications are discussed which may make it possible to directly and simultaneously measure both the shape and refractive index distribution of the lens with age and accommodation.

In this thesis, we develop statistical methods for extracting significant information from biomedical signals. Biomedical signals are not only generated from a complex system but also affected by various random factors during their measurement. The biomedical signals may then be studied in two aspects: observational noise that biomedical signals experience and intrinsic nature that noise-free signals possess. We study Magnetic Resonance (MR) images and speech signals as applications in the one- and two-dimensional signal representation. In MR imaging, we study how observational noise can be effectively modeled and then removed. Magnitude MR images suffer from Rician-distributed signal-dependent noise. Observing that the squared-magnitude MR image follows a scaled non-central Chi-square distribution on two degrees of freedom, we optimize the parameters involved in the proposed Rician-adapted Non-local Mean (RNLM) estimator by minimizing the Chi-square unbiased risk estimate in the minimum mean square error sense. A linear expansion of RNLM's is considered in order to achieve the global optimality of the parameters without data-dependency. Parallel computations and convolution operations are considered as acceleration techniques. Experiments show the proposed method favorably compares with benchmark denoising algorithms. Parametric modelings of noise-free signals are studied for robust speech applications. The voiced speech signals are often modeled as the harmonic model with the fundamental frequency, commonly assumed to be a smooth function of time. As an important feature in various speech applications, pitch, the perceived tone, is obtained by way of estimating the fundamental frequency. In this thesis, two model-based pitch estimation schemes are introduced. In the first, an iterative Auto Regressive Moving Average technique estimates harmonically tied sinusoidal components in noisy speech signals. Dynamic programming implements the smoothness of the fundamental frequency. The second introduces the Continuous-time Voiced Speech (CVS) model, which models the smooth fundamental frequency as a linear combination of block-wise continuous polynomial bases. The model parameters are obtained via a convex optimization with constraints, providing an estimate of the instantaneous fundamental frequency. Experiments validate robustness and accuracy of the proposed methods compared with some current state-of-the-art pitch estimation algorithms.<br>Engineering and Applied Sciences

The design of pre-contoured fracture fixation implants (plates and nails) that correctly fit the anatomy of a patient utilises 3D models of long bones with accurate geometric representation. 3D data is usually available from computed tomography (CT) scans of human cadavers that generally represent the above 60 year old age group. Thus, despite the fact that half of the seriously injured population comes from the 30 year age group and below, virtually no data exists from these younger age groups to inform the design of implants that optimally fit patients from these groups. Hence, relevant bone data from these age groups is required. The current gold standard for acquiring such data–CT–involves ionising radiation and cannot be used to scan healthy human volunteers. Magnetic resonance imaging (MRI) has been shown to be a potential alternative in the previous studies conducted using small bones (tarsal bones) and parts of the long bones. However, in order to use MRI effectively for 3D reconstruction of human long bones, further validations using long bones and appropriate reference standards are required. Accurate reconstruction of 3D models from CT or MRI data sets requires an accurate image segmentation method. Currently available sophisticated segmentation methods involve complex programming and mathematics that researchers are not trained to perform. Therefore, an accurate but relatively simple segmentation method is required for segmentation of CT and MRI data. Furthermore, some of the limitations of 1.5T MRI such as very long scanning times and poor contrast in articular regions can potentially be reduced by using higher field 3T MRI imaging. However, a quantification of the signal to noise ratio (SNR) gain at the bone - soft tissue interface should be performed; this is not reported in the literature. As MRI scanning of long bones has very long scanning times, the acquired images are more prone to motion artefacts due to random movements of the subject‟s limbs. One of the artefacts observed is the step artefact that is believed to occur from the random movements of the volunteer during a scan. This needs to be corrected before the models can be used for implant design. As the first aim, this study investigated two segmentation methods: intensity thresholding and Canny edge detection as accurate but simple segmentation methods for segmentation of MRI and CT data. The second aim was to investigate the usability of MRI as a radiation free imaging alternative to CT for reconstruction of 3D models of long bones. The third aim was to use 3T MRI to improve the poor contrast in articular regions and long scanning times of current MRI. The fourth and final aim was to minimise the step artefact using 3D modelling techniques. The segmentation methods were investigated using CT scans of five ovine femora. The single level thresholding was performed using a visually selected threshold level to segment the complete femur. For multilevel thresholding, multiple threshold levels calculated from the threshold selection method were used for the proximal, diaphyseal and distal regions of the femur. Canny edge detection was used by delineating the outer and inner contour of 2D images and then combining them to generate the 3D model. Models generated from these methods were compared to the reference standard generated using the mechanical contact scans of the denuded bone. The second aim was achieved using CT and MRI scans of five ovine femora and segmenting them using the multilevel threshold method. A surface geometric comparison was conducted between CT based, MRI based and reference models. To quantitatively compare the 1.5T images to the 3T MRI images, the right lower limbs of five healthy volunteers were scanned using scanners from the same manufacturer. The images obtained using the identical protocols were compared by means of SNR and contrast to noise ratio (CNR) of muscle, bone marrow and bone. In order to correct the step artefact in the final 3D models, the step was simulated in five ovine femora scanned with a 3T MRI scanner. The step was corrected using the iterative closest point (ICP) algorithm based aligning method. The present study demonstrated that the multi-threshold approach in combination with the threshold selection method can generate 3D models from long bones with an average deviation of 0.18 mm. The same was 0.24 mm of the single threshold method. There was a significant statistical difference between the accuracy of models generated by the two methods. In comparison, the Canny edge detection method generated average deviation of 0.20 mm. MRI based models exhibited 0.23 mm average deviation in comparison to the 0.18 mm average deviation of CT based models. The differences were not statistically significant. 3T MRI improved the contrast in the bone–muscle interfaces of most anatomical regions of femora and tibiae, potentially improving the inaccuracies conferred by poor contrast of the articular regions. Using the robust ICP algorithm to align the 3D surfaces, the step artefact that occurred by the volunteer moving the leg was corrected, generating errors of 0.32 ± 0.02 mm when compared with the reference standard. The study concludes that magnetic resonance imaging, together with simple multilevel thresholding segmentation, is able to produce 3D models of long bones with accurate geometric representations. The method is, therefore, a potential alternative to the current gold standard CT imaging.

Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.<br>Includes bibliographical references (leaves 60-62). Also available in electronic version. Access restricted to campus users.

This study deals with fibre suspension flows through cylindrical pipes. Thepresent work aims at measurements of opaque flows, which are common inindustries. Nuclear magnetic resonance imaging (NMRI) and ultrasound velocimetryprofiling (UVP) were employed as non-invasive and optic-independenttools to measure the velocity profiles. As a first experiment, a paper-pulp suspensionflow through a sudden contraction and expansion was investigated.The results show the NMRI technique can be used to measure the stronglyunsteady flow such as separated regions though the MR signal is attenuateddue to the turbulence in the flow. The flow loop had however an insufficientinlet length which caused asymmetric profiles at the test section. As a secondexperiment, a flow loop which provided fully developed flows at the test sectionwas designed. After that, the velocity profiles of rayon-fibre and micro-spheresuspension flows were measured by the NMRI and the UVP independently.In principle, these two techniques measure the different velocities of the fibresuspensionflows, i.e. the velocity of the water and the fibre. In dilute suspensionflows, where the velocities of the two phases were assumed to be thesame, the velocity profiles were in good agreement. This shows the validityof the two measurement techniques. However, it should be pointed out thatthere is a limitation of the current UVP method for highly concentrated flows.The velocity profiles obtained by the UVP at high concentrations seems notto represent physics while the NMRI is not affected by the concentrations. Itis argued that the advances of the NMRI for the measurement of the highlyconcentrated flows.

L'imagerie par résonance magnétique (IRM) est la technique d'imagerie médicale de référence pour sonder in vivo et non invasivement les tissus mous du corps humain, en particulier le cerveau.L'amélioration de la résolution de l'IRM en un temps d'acquisition standard (400µm isotrope en 15 minutes) permettrait aux médecins d'améliorer considérablement leur diagnostic et le suivi des patients. Cependant, le temps d'acquisition en IRM reste long. Pour réduire ce temps, la récente théorie de l'échantillonnage comprimée (EC) a révolutionné la façon d'acquérir des données dans plusieurs domaines dont l'IRM en surmontant le théorème de Shannon-Nyquist. Avec l'EC, les données peuvent alors être massivement sous-échantillonnées tout en assurant des conditions optimales de reconstruction des images.Dans ce contexte, les thèses de doctorat précédemment soutenue au sein du laboratoire ont été consacrées à la conception et à la mise en oeuvre de scénarios d'acquisition physiquement plausibles pour accélérer l'acquisitions. Un nouvel algorithme d'optimisation pour la conception de trajectoire non cartésienne avancée appelée SPARKLING pour Spreading Projection Algorithm for Rapid K-space samplING en est né. Les trajectoires SPARKLING générées ont conduit à des facteurs d'accélération allant jusqu'à 20 en 2D et 70 pour les acquisitions 3D sur des images à haute résolution pondérées en T*₂ acquises à 7 Tesla. Ces accélérations n'étaient accessibles que grâce au rapport signal/bruit d'entrée élevé fourni par l'utilisation de bobines de réception multi-canaux (IRMp). Cependant, ces résultats ont été obtenus au détriment d'une reconstruction longue et complexe. Dans cette thèse, l'objectif est de proposer une nouvelle approche de reconstruction en ligne d'images acquies par IRMp non cartésiennes. Pour atteindre cet objectif, nous nous appuyons sur une approche en ligne où reconstruction et acquisition s'entremèlent. Par conséquent, la reconstruction débute avant la fin de l'acquisition et un résultat partiel est délivré au cours de l'examen. L'ensemble du pipeline est compatible avec une implémentation réelle à travers l'interface Gadgetron pour produire les images reconstruites à la console du scanner.Ainsi, après avoir exposé la théorie de l'échantillonage comprimé, nous présentons l'état de l'art de la méthode dédiée à la reconstruction en imagerie multi-canaux. En particulier, nous nous concentrerons d'abord sur les méthodes d'autocalibration qui présentent l'avantage d'être adaptées à l'échantillonnage non cartésien et nous proposons une méthode simple mais efficace pour estimer le profil de sensibilité des différents cannaux. Cependant, en raison de leur dépendance au profil de sensibilité, ces méthodes ne sont pas adapatable à la reconstruction en ligne. Par conséquent, la deuxième partie se concentre sur la suppression des ces profils et celà grâce à l'utilisation de norme mixte promouvant une parcimonie structurée. Ensuite, nous adaptons différentes réularization basées sur la parcimonie structurée pour reconstruire ces images fortement corrélées. Enfin, la méthode retenue sera appliquée à l'imagerie en ligne<br>Magnetic resonance imaging (MRI) is the reference medical imaging technique for probing in vivo and non-invasively soft tissues in the human body, notably the brain. MR image resolution improvement in a standard scanning time (e.g., 400µm isotropic in 15 min) would allow medical doctors to significantly improve both their diagnosis and patients' follow-up. However the scanning time in MRI remains long, especially in the high resolution context. To reduce this time, the recent Compressed Sensing (CS) theory has revolutionized the way of acquiring data in several fields including MRI by overcoming the Shannon-Nyquist theorem. Using CS, data can then be massively under-sampled while ensuring conditions for optimal image recovery.In this context, previous Ph.D. thesis in the laboratory were dedicated to the design and implementation of physically plausible acquisition scenarios to accelerate the scan. Those projects deliver new optimization algorithm for the design of advanced non-Cartesian trajectory called SPARKLING: Spreading Projection Algorithm for Rapid K-space samplING. The generated SPARKLING trajectories led to acceleration factors up to 20 in 2D and 60 for 3D-acquisitions on highly resolved T₂* weighted images acquired at 7~Tesla.Those accelerations were only accessible thanks to the high input Signal-to-Noise Ratio delivered by the usage of multi-channel reception coils. However, those results are coming at a price of long and complex reconstruction.In this thesis, the objective is to propose an online approach for non-Cartesian multi-channel MR image reconstruction. To achieve this goal we rely on an online approach where the reconstruction starts from incomplete data.Hence acquisition and reconstruction are interleaved, and partial feedback is given during the scan. After exposing the Compressed Sensing theory, we present state-of the art method dedicated to multi-channel coil reconstruction. In particular, we will first focus on self-calibrating methods that presents the advantage to be adapted to non-Cartesian sampling and we propose a simple yet efficient method to estimate the coil sensitivity profile.However, owing to its dependence to user-defined parameters, this two-step approach (extraction of sensitivity maps and then image reconstruction) is not compatible with the timing constraints associated with online reconstruction. Then we studied the case of calibration-less reconstruction methods and splits them into two categories, the k-space based and the domain-based. While the k-space calibration-less method are sub-optimal for non-Cartesian reconstruction, due to the gridding procedure, we will retain the domain-based calibration-less reconstruction and prove theirs for online purposes. Hence in the second part, we first prove the advantage of mixed norm to improve the recovery guarantee in the pMRI setting. Then we studied the impact of structured sparse induced norm on the reconstruction multi-channel purposes, where then and adapt different penalty based on structured sparsity to handle those highly correlated images. Finally, the retained method will be applied to online purposes. The entire pipeline, is compatible with an implementation through the Gadgetron pipeline to deliver the reconstruction at the scanner console

Background Visual hallucinations (VH) are an important non-motor complication of Parkinson’s disease (PD) which carries a negative prognosis, but their biological basis is unclear. Multi-modal magnetic resonance imaging (MRI) can be used to evaluate structural and functional brain mechanisms underpinning VH in PD. Methods To assess cerebral microstructure and resting functional activities in patients with idiopathic PD and VH, I compared PD patients with VH (PDVH) and PD patients without VH (PDnonVH), while healthy controls (HC) were also recruited for comparison. Diffusion tensor imaging was used to calculate mean diffusivity (MD) and fractional anisotropy (FA). Structural MRI was used to calculate voxel-based intensity of grey matter (GM) and white matter (WM) across the entire brain and compared among groups. Furthermore, functional magnetic resonance imaging of the brain, acquired during rest, was processed to calculate the amplitude of low-frequency fluctuations (ALFF) and functional connectivity (FC) to inform a model of VH. In addition, hippocampal volume, shape, mean diffusivity and FC across the whole brain was further examined. Hippocampal dependent visual spatial memory performance was compared between groups, and predicted correlations with hippocampal microstructural indices and VH severity were tested. Results In the first study, PDVH had lower FA than both PDnonVH and HC in the right occipital lobe and left parietal lobe, but increased FA in the right infero-medial fronto-occipital fasciculus and posterior inferior longitudinal fasciculus. Moreover, PDVH patients showed less GM volume compared to PDnonVH in the right lingual gyrus of the occipital lobe. In the second study, PDVH patients compared to non-hallucinators showed lower ALFF in occipital lobes, with greater ALFF in temporo-parietal region, limbic lobe and right cerebellum. The PDVH group also showed alteration in functional connectivity between occipital region and corticostriatal regions. Finally in the third study, although there were no gross hippocampal volume and shape differences across groups, individuals with PDVH had higher diffusivity in hippocampus than PDnonVH and HC. Both PD groups had significantly poorer visuospatial memory compared to HC. Poorer visuospatial memory was correlated with higher hippocampal diffusivity in HC and more severe VH in the PDVH group.FC between hippocampus and primary visual cortex, dorsal/ventral visual pathways was also lower in PDVH than other groups, whereas FC between hippocampus and default mode network regions was greater in PDVH group compared to others. Conclusion Compared to PDnonVH groups, the PDVH group had multiple structural deficits in primary and associative visual cortices. In term of hemodynamic activity, the PDVH group had lower ALFF in occipital lobe, but greater ALFF in regions that comprise the dorsal visual pathway. Moreover, this lower ALFF in the primary visual cortex was accompanied by lower functional connectivity across components of the ventral/dorsal visual pathway in the PDVH group compared to the PDnonVH group. Moreover, evidence supporting a specific role for the hippocampus in PDVH was obtained. In the absence of gross macrostructural anomalies, hippocampal microstructure and functional connectivity was compromised in PDVH. I observed an association between visuospatial memory and hippocampal integrity and suggest that hippocampal pathology and consequent disruption in visuospatial memory plays a key contribution to VH in PD. Thus, in the PDVH group, "bottom-up" primary visual cortex and “top-down” visual association pathways and attentional networks appear to be disrupted.<br>published_or_final_version<br>Psychiatry<br>Doctoral<br>Doctor of Philosophy

Disturbed hemodynamic conditions are often related to pathologies of the cardiovascular system. Phase‐contrast Magnetic Resonance Imaging (MRI) provides a non‐invasive technique for the assessment of time‐resolved blood velocity vector fields within arbitrary imaging volumes. Besides velocity vector field information, parameters related to turbulence can be calculated with advanced multi‐point velocity encoding schemes. However, long scan times are currently the main barrier for the acceptance of the method in a clinical setting. The following work presents data‐driven MRI reconstruction algorithms for undersampled measurements with the focus on accurate flow quantification and visualization. An extension of an auto‐calibrated parallel imaging reconstruction framework for arbitrary kspace trajectories is proposed. The exploitation of temporal correlations as present in timeresolved data demonstrates further advances of scan time reduction when assessing mean velocity and turbulent kinetic energy. While most prior knowledge imposed in advanced MR image reconstruction is designed to work on magnitude images or assumes smooth background phase behavior, dedicated provisions are required for image reconstruction of phase‐contrast MRI data. To this end, it is proposed to incorporate the divergence‐free condition of blood flow into a separate magnitude and phase reconstruction framework for improving the accuracy of image reconstruction of blood velocity vector fields. To address respiratory motion artifacts, retrospective non‐rigid respiratory motion correction incorporated into an iterative parallel imaging reconstruction algorithm is proposed. Furthermore, optimized k‐t sampling patterns are derived for combined parallel imaging‐ and compressed sensing‐based scan acceleration. Finally, the dynamic parallel imaging technique is applied to study blood flow and turbulence patterns in a relevant patient population with congenital heart disease.

Magnetic Resonance Imaging (MRI) is a non-invasive medical imaging method that is used in the diagnosis of many common diseases. Compared to other medical imaging modalities, MRI has the ability to provide high-resolution 2D and 3D images in arbitrary orientations, without the use of potentially damaging ionizing radiation. Myocardial perfusion MRI is a promising non- invasive clinical way to detect cardiac disease. It can also provide quantitative analysis for blood flow within the heart. However, MRI requires longer scan times to acquire images at comparable resolutions to some other imaging modalities. Increasing image resolution, both spatially and temporally, is very important to myocardial perfusion MRI. The work presented in this dissertation focuses on the development of novel dynamic contrast-enhanced (DCE) MRI that is able to achieve both high spatial and temporal resolutions, as well as suitable spatial coverage of the heart. Three novel acquisition and reconstruction frame- works are proposed and analyzed in this dissertation. The first framework we propose uses a highly undersampled 3D Cartesian acquisition and total variation (TV) constrained reconstruction to accelerate the acquisition of myocardial perfu- sion images. This technique increases temporal resolution for contrast tracking without sacrificing spatial resolution. An analysis of the effect of different k-space trajectories using this technique is performed. The purpose of the second framework is to simplify cardiac perfusion studies. An ECG- gated saturation recovery sequence is regularly used for cardiac perfusion imaging. However, using an ungated acquisition has the potential benefit of reducing the acquisition time by eliminating the need for the ECG trigger signal. We present a novel non-Cartesian 2D multi-slice ungated acquisition, and demonstrate that it is a promising alternative to ECG-gated cardiac perfusion studies. An optimization analysis of our ungated acquisition is also presented. The third method in this dissertation combines the 2D ungated acquisition with multi-band excitation, which enables the excitation of multiple slices simultaneously. This method is able to reduce scan time not only through the ungated acquisition, but also from obtaining multiple slices at once. This allows us to achieve whole heart coverage without sacrificing temporal resolution. The contributions presented in this dissertation demonstrate the basic feasibility of car- diac perfusion MRI achieving whole-heart coverage in a clinical setting by overcoming the major existing limitations: speed of acquisition and spatial coverage.

Die vorliegende Arbeit präsentiert neuartige schnelle Bildgebungstechniken für die Hoch- und Ultrahochfeld Magnetresonanztomographie. Zunächst werden die Grundprinzipien schneller Spin-Echo Techniken beleuchtet. Diese physikalischen Überlegungen bilden die Grundlage für die Entwicklung modifizierter Techniken. In einer ersten Entwicklungsstufe wird eine neue Variante der schnellen Spin-Echo Bildgebung vorgestellt. Diese Technik generiert anatomischen und funktionellen Bildkontrast innerhalb von nur einer Datenaufnahme. Der entscheidende Vorteil des entwickelten Ansatzes besteht in einer wesentlichen Verkürzung der Messzeit. Darüber hinaus wird eine deutliche Reduktion von Bildfehlern ermöglicht, die im konventionellen Fall häufig durch Bewegung erzeugt werden. Die zweite Entwicklungsstufe befasst sich mit der Implementierung einer schnellen Spin-Echo Technik zur Abbildung des physikalischen Phänomens der Brownschen Molekularbewegung. Diffusionsmessungen der Molekülbewegungen sind durch die Überlagerung von makroskopischen Bewegungen sehr anspruchsvoll. Diese Schwierigkeit wird in der vorliegenden Arbeit methodisch überwunden, indem eine diffusionsgewichtete schnelle Spin Echo Technik implementiert wird. Die dritte Entwicklungsstufe konzentriert sich auf suszeptibiltätsgewichtete schnelle Spin-Echo Bildgebung. Herkömmliche Techniken zur suszeptibiltätsgewichteten Bildgebung sind anfällig für Artefakte, die sich in Signalauslöschungen äußern. Um dieser Herausforderung methodisch zu begegnen, untersucht diese Arbeit das Potential einer suszeptibiltätsgewichteten schnellen Spin-Echo Technik zur Charakterisierung der Mikrostruktur des Herzmuskels bei 7.0 T. Ein Ziel der in dieser Arbeit neu entwickelten schnellen Spin-Echo Methoden besteht darin, Limitierungen bestehender Techniken zu beheben. Damit soll richtungsweisend über die Grundlagenforschung hinaus die Basis für klinische Anwendungen der entwickelten physikalischen Erkenntnisse und Methoden gelegt werden.<br>This thesis presents novel fast imaging techniques for magnetic resonance imaging. Rapid Acquisition with Relaxation Enhancement (RARE) is a fast imaging technique. An ever growing number of clinical applications render clinically and physically motivated advancement of RARE imaging necessary. This thesis focuses on the advancement of RARE imaging. For this purpose, the basic principle of RARE imaging is examined. The first part proposes a novel RARE variant which provides simultaneous anatomical and functional contrast within one acquisition. This approach provides an alternative versus conventional RARE variants where sequential acquisitions are put to use to achieve different image contrasts. With the speed gain of the proposed approach a substantial shortening of scanning time can be accomplished together with a reduction in the propensity for motion. The second part focuses on diffusion weighted MRI. Probing diffusion on a micrometer scale is challenging because of MRI’s sensitivity to bulk motion. Unfortunately, conventional rapid diffusion weighted imaging techniques are prone to severe image distortions. Realizing this constraint, a diffusion weighted RARE technique that affords the generation of diffusion weighted images free of distortion is implemented. The third part is formed around susceptibility weighted MRI. The underlying biophysical mechanisms allow the assessment of tissue microstructure. Common susceptibility weighted imaging techniques are prone to image artifacts. Recognizing the opportunities of susceptibility weighted MRI the potential of a susceptibility weighted RARE technique is investigated with the goal to assess myocardial microstructure. The goal of the novel RARE developments is to overcome constraints of existing imaging techniques. The physical considerations and the novel methodology introduced in this thesis are brought beyond the scope of basic research. Moreover, the foundation for clinical applicability is created.

Interventional magnetic resonance imaging (MRI) aims at performing minimally invasive percutaneous interventions, such as tumor ablations and biopsies, under MRI guidance. During such interventions, the acquired MR image planes are typically aligned to the surgical instrument (needle) axis and to surrounding anatomical structures of interest in order to efficiently monitor the advancement in real-time of the instrument inside the patient's body. Object tracking inside the MRI is expected to facilitate and accelerate MR-guided interventions by allowing to automatically align the image planes to the surgical instrument. In this PhD thesis, an image-based workflow is proposed and refined for automatic image plane alignment. An automatic tracking workflow was developed, performing detection and tracking of a passive marker directly in clinical real-time images. This tracking workflow is designed for fully automated image plane alignment, with minimization of tracking-dedicated time. Its main drawback is its inherent dependence on the slow clinical MRI update rate. First, the addition of motion estimation and prediction with a Kalman filter was investigated and improved the workflow tracking performance. Second, a complementary optical sensor was used for multi-sensor tracking in order to decouple the tracking update rate from the MR image acquisition rate. Performance of the workflow was evaluated with both computer simulations and experiments using an MR compatible testbed. Results show a high robustness of the multi-sensor tracking approach for dynamic image plane alignment, due to the combination of the individual strengths of each sensor.

Magnetresonanztomographie (MRT) ist eine nichtinvasive Bildgebungsmethode, die in der Medizin sowie in der Forschung eingesetzt wird und auf der magnetischen Kernresonanz beruht. Die Erforschung der Ultrahochfeld (UHF) MRT ab Magnetfeldstärken von 7.0 Tesla und darüber ist durch einen intrinsischen Signalgewinn hin zu hohen Magnetfeldstärken motiviert und beschäftigt sich mit den dabei auftretenden physikalischen Effekten ebenso wie mit den dazu notwendigen neuartigen Technologien. Die vorliegende Arbeit untersucht Mehrkanalantennen zur Anregung der magnetischen Kernresonanz sowie zum Empfang des resultierenden Signals bei 7.0 T. Für die magnetische Kernresonanz von Protonen ergibt sich eine Resonanzfrequenz von 300 MHz. Die zugehörige Wellenlänge in menschlichem Gewebe verlässt in diesem Frequenzbereich im Verhältnis zu den Körperabmessungen den quasistatischen Bereich. Die sich ergebende Wellenausbreitung hat Interferenzmuster in den erzeugten Bildern zur Folge, die zu klinisch nicht verwertbaren Bildinformationen führen können. Vor diesem Hintergrund wurden in dieser Arbeit Mehrkanalantennen mit 4, 8 und 16 unabhängigen Elementen zur Signalanregung und zum Empfang konzipiert, aufgebaut und untersucht. Die Erkenntnisse mündeten in der erfolgreichen Implementierung der weltweit ersten 32-Kanal Antenne zur kardiovaskulären Bildgebung bei 7.0 T. Darüber hinaus wurde eine Antenne entwickelt, welche die ersten auf der Natriumkonzentration beruhenden bewegten MRT Bilder des menschlichen Herzens bei 7.0 T ermöglichte. Der Zusammenhang zwischen Natriumkonzentration und Zellintegrität ermöglicht direkte und ortsaufgelöste Einblicke in physiologische Prozesse. Die Ergebnisse dieser Arbeit belegen die breite Anwendbarkeit von Mehrkanalantennen in der UHF MRT zur Protonen-und Natriumbildgebung und bilden eine solide technologische Basis für breitere klinische Studien, um die Ultrahochfeld MRT reif für den routinemäßigen Einsatz im Gesundheitswesen zu machen.<br>Magnetic resonance imaging (MRI) is a non-invasive imaging method based on the effect of nuclear magnetic resonance. It is used in healthcare as well as in research. MRI at magnetic field strengths of 1.5 Tesla and 3 Tesla is well established. The gain in signal-to-noise ratio (SNR) intrinsic to higher magnetic field strength fuels the vigorous research field of Ultrahigh field (UHF) MRI at 7.0 T and above. Nevertheless for MRI based upon proton imaging the wavelength of the transmitted electro-magnetic fields slowly departs from the semi-static regime and reaches the dimension of the transection of the human body at 7.0 T. This gives rise to constructive and destructive interferences that potentially render image quality non-diagnostic for clinical use. Therefore is work proposes the worlds’ first 32 channel antenna array for cardiovascular MRI at 7.0 T. Electro-magnetic field simulations are utilized to study the capabilities of multi-channel RF antenna arrays to mitigate destructive interferences and provided the basis for a workflow towards homogenization of the electromagnetic radio-frequency field. Pre-clinical studies showed the capabilities and limits of translating the SNR gain of UHF MRI into clinical beneficial numbers, namely increased spatial or temporal resolution or scan time shortening. To make further use of the benefits of UHR MRI and to make a step towards first-hand spatial resolved information of biological processes in human tissue sodium imaging of the human heart was enabled with the design of a tailored antenna array. The results were reconstructed into the first movies of the human heart at 7.0 T based on sodium signal. This profound technological basis for radio frequency excitation and reception in UHF MRI can be expected to pave the way for broader clinical studies at 7.0 T with the ultimate goal to improve the quality and the earliness of treatment decisions in future clinical practice.

A new method for the simulation of radio frequency (RF) coils has been developed. This method utilizes the FEM simulation package Ansoft HFSS as a base for the modeling of RF coils with complex biological loading effects. The abilities of this software have been augmented with custom MATLAB code to enable the fast prediction of lumped element values needed to properly tune and match the coil structure as well as to perform the necessary post processing of simulation data in order to quickly generate and evaluate field data of the resonating coil and compare design variations. This method was evaluated for accuracy and implemented in the re-design of an existing four channel breast coil array for clinical imaging of the female breasts. Based on the simulation results, a commercially viable printed circuit board (PCB) implementation was developed and tested in a clinical 1.5 T MR scanner. The new design allows for wide open bilateral access to the breast regions in order to accommodate various interventional procedures. The layout has also increased axillary B1 field coverage with minor penalty to the signal-to-noise ratio of the coil array, enabling high-resolution imaging over a wide field-of-view.

Functional magnetic resonance imaging (fMRI) is a prime example of multi-disciplinary research. Without the beautiful physics of MRI, there wouldnot be any images to look at in the first place. To obtain images of goodquality, it is necessary to fully understand the concepts of the frequencydomain. The analysis of fMRI data requires understanding of signal pro-cessing, statistics and knowledge about the anatomy and function of thehuman brain. The resulting brain activity maps are used by physicians,neurologists, psychologists and behaviourists, in order to plan surgery andto increase their understanding of how the brain works. This thesis presents methods for real-time fMRI and non-parametric fMRIanalysis. Real-time fMRI places high demands on the signal processing,as all the calculations have to be made in real-time in complex situations.Real-time fMRI can, for example, be used for interactive brain mapping.Another possibility is to change the stimulus that is given to the subject, inreal-time, such that the brain and the computer can work together to solvea given task, yielding a brain computer interface (BCI). Non-parametricfMRI analysis, for example, concerns the problem of calculating signifi-cance thresholds and p-values for test statistics without a parametric nulldistribution. Two BCIs are presented in this thesis. In the first BCI, the subject wasable to balance a virtual inverted pendulum by thinking of activating theleft or right hand or resting. In the second BCI, the subject in the MRscanner was able to communicate with a person outside the MR scanner,through a virtual keyboard. A graphics processing unit (GPU) implementation of a random permuta-tion test for single subject fMRI analysis is also presented. The randompermutation test is used to calculate significance thresholds and p-values forfMRI analysis by canonical correlation analysis (CCA), and to investigatethe correctness of standard parametric approaches. The random permuta-tion test was verified by using 10 000 noise datasets and 1484 resting statefMRI datasets. The random permutation test is also used for a non-localCCA approach to fMRI analysis.

[EN] Alcohol abuse is one of the most alarming issues for the health authorities. It is estimated that at least 23 million of European citizens are affected by alcoholism causing a cost around 270 million euros. Excessive alcohol consumption is related with physical harm and, although it damages the most of body organs, liver, pancreas, and brain are more severally affected. Not only physical harm is associated to alcohol-related disorders, but also other psychiatric disorders such as depression are often comorbiding. As well, alcohol is present in many of violent behaviors and traffic injures. Altogether reflects the high complexity of alcohol-related disorders suggesting the involvement of multiple brain systems. With the emergence of non-invasive diagnosis techniques such as neuroimaging or EEG, many neurobiological factors have been evidenced to be fundamental in the acquisition and maintenance of addictive behaviors, relapsing risk, and validity of available treatment alternatives. Alterations in brain structure and function reflected in non-invasive imaging studies have been repeatedly investigated. However, the extent to which imaging measures may precisely characterize and differentiate pathological stages of the disease often accompanied by other pathologies is not clear. The use of animal models has elucidated the role of neurobiological mechanisms paralleling alcohol misuses. Thus, combining animal research with non-invasive neuroimaging studies is a key tool in the advance of the disorder understanding. As the volume of data from very diverse nature available in clinical and research settings increases, an integration of data sets and methodologies is required to explore multidimensional aspects of psychiatric disorders. Complementing conventional mass-variate statistics, interests in predictive power of statistical machine learning to neuroimaging data is currently growing among scientific community. This doctoral thesis has covered most of the aspects mentioned above. Starting from a well-established animal model in alcohol research, Marchigian Sardinian rats, we have performed multimodal neuroimaging studies at several stages of alcohol-experimental design including the etiological mechanisms modulating high alcohol consumption (in comparison to Wistar control rats), alcohol consumption, and treatment with the opioid antagonist Naltrexone, a well-established drug in clinics but with heterogeneous response. Multimodal magnetic resonance imaging acquisition included Diffusion Tensor Imaging, structural imaging, and the calculation of magnetic-derived relaxometry maps. We have designed an analytical framework based on widely used algorithms in neuroimaging field, Random Forest and Support Vector Machine, combined in a wrapping fashion. Designed approach was applied on the same dataset with two different aims: exploring the validity of the approach to discriminate experimental stages running at subject-level and establishing predictive models at voxel-level to identify key anatomical regions modified during the experiment course. As expected, combination of multiple magnetic resonance imaging modalities resulted in an enhanced predictive power (between 3 and 16%) with heterogeneous modality contribution. Surprisingly, we have identified some inborn alterations correlating high alcohol preference and thalamic neuroadaptations related to Naltrexone efficacy. As well, reproducible contribution of DTI and relaxometry -related biomarkers has been repeatedly identified guiding further studies in alcohol research. In summary, along this research we demonstrate the feasibility of incorporating multimodal neuroimaging, machine learning algorithms, and animal research in the advance of the understanding alcohol-related disorders.<br>[ES] El abuso de alcohol es una de las mayores preocupaciones de las autoridades sanitarias en la Unión Europea. El consumo de alcohol en exceso afecta en mayor o menor medida la totalidad del organismo siendo el páncreas e hígado los más severamente afectados. Además de estos, el sistema nervioso central sufre deterioros relacionados con el alcohol y con frecuencia se presenta en paralelo con otras patologías psiquiátricas como la depresión u otras adicciones como la ludopatía. La presencia de estas comorbidades demuestra la complejidad de la patología en la que multitud de sistemas neuronales interaccionan entre sí. El uso imágenes de resonancia magnética (RM) han ayudado en el estudio de enfermedades psiquiátricas facilitando el descubrimiento de mecanismos neurológicos fundamentales en el desarrollo y mantenimiento de la adicción al alcohol, recaídas y el efecto de los tratamientos disponibles. A pesar de los avances, todavía se necesita investigar más para identificar las bases biológicas que contribuyen a la enfermedad. En este sentido, los modelos animales sirven, por lo tanto, a discriminar aquellos factores únicamente relacionados con el alcohol controlando otros factores que facilitan el desarrollo del alcoholismo. Estudios de resonancia magnética en animales de laboratorio y su posterior evaluación en humanos juegan un papel fundamental en el entendimiento de las patologías psiquatricas como la addicción al alcohol. La imagen por resonancia magnética se ha integrado en entornos clínicos como prueba diagnósticas no invasivas. A medida que el volumen de datos se va incrementando, se necesitan herramientas y metodologías capaces de fusionar información de muy distinta naturaleza y así establecer criterios diagnósticos cada vez más exactos. El poder predictivo de herramientas derivadas de la inteligencia artificial como el aprendizaje automático sirven de complemento a tradicionales métodos estadísticos. En este trabajo se han abordado la mayoría de estos aspectos. Se han obtenido datos multimodales de resonancia magnética de un modelo validado en la investigación de patologías derivadas del consumo del alcohol, las ratas Marchigian-Sardinian desarrolladas en la Universidad de Camerino (Italia) y con consumos de alcohol comparables a los humanos. Para cada animal se han adquirido datos antes y después del consumo de alcohol y bajo dos condiciones de abstinencia (con y sin tratamiento de Naltrexona, una medicaciones anti-recaídas usada como farmacoterapia en el alcoholismo). Los datos de resonancia magnética multimodal consistentes en imágenes de difusión, de relaxometría y estructurales se han fusionado en un esquema analítico multivariable incorporando dos herramientas generalmente usadas en datos derivados de neuroimagen, Random Forest y Support Vector Machine. Nuestro esquema fue aplicado con dos objetivos diferenciados. Por un lado, determinar en qué fase experimental se encuentra el sujeto a partir de biomarcadores y por el otro, identificar sistemas cerebrales susceptibles de alterarse debido a una importante ingesta de alcohol y su evolución durante la abstinencia. Nuestros resultados demostraron que cuando biomarcadores derivados de múltiples modalidades de neuroimagen se fusionan en un único análisis producen diagnósticos más exactos que los derivados de una única modalidad (hasta un 16% de mejora). Biomarcadores derivados de imágenes de difusión y relaxometría discriminan estados experimentales. También se han identificado algunos aspectos innatos que están relacionados con posteriores comportamientos con el consumo de alcohol o la relación entre la respuesta al tratamiento y los datos de resonancia magnética. Resumiendo, a lo largo de esta tesis, se demuestra que el uso de datos de resonancia magnética multimodales en modelos animales combinados en esquemas analíticos multivariados es una herramienta válida en el entendimiento de patologías<br>[CAT] L'abús de alcohol es una de les majors preocupacions per part de les autoritats sanitàries de la Unió Europea. Malgrat la dificultat de establir xifres exactes, se estima que uns 23 milions de europeus actualment sofreixen de malalties derivades del alcoholisme amb un cost que supera els 150.000 milions de euros per a la societat. Un consum de alcohol en excés afecta en major o menor mesura el cos humà sent el pàncreas i el fetge el més afectats. A més, el cervell sofreix de deterioraments produïts per l'alcohol i amb freqüència coexisteixen amb altres patologies com depressió o altres addiccions com la ludopatia. Tot aquest demostra la complexitat de la malaltia en la que múltiple sistemes neuronals interactuen entre si. Tècniques no invasives com el encefalograma (EEG) o imatges de ressonància magnètica (RM) han ajudat en l'estudi de malalties psiquiàtriques facilitant el descobriment de mecanismes neurològics fonamentals en el desenvolupament i manteniment de la addició, recaiguda i la efectivitat dels tractaments disponibles. Tot i els avanços, encara es necessiten més investigacions per identificar les bases biològiques que contribueixen a la malaltia. En aquesta direcció, el models animals serveixen per a identificar únicament dependents del abús del alcohol. Estudis de ressonància magnètica en animals de laboratori i posterior avaluació en humans jugarien un paper fonamental en l' enteniment de l'ús del alcohol. L'ús de probes diagnostiques no invasives en entorns clínics has sigut integrades. A mesura que el volum de dades es incrementa, eines i metodologies per a la fusió d' informació de molt distinta natura i per tant, establir criteris diagnòstics cada vegada més exactes. La predictibilitat de eines desenvolupades en el camp de la intel·ligència artificial com la aprenentatge automàtic serveixen de complement a mètodes estadístics tradicionals. En aquesta investigació se han abordat tots aquestes aspectes. Dades multimodals de ressonància magnètica se han obtingut de un model animal validat en l'estudi de patologies relacionades amb el consum d'alcohol, les rates Marchigian-Sardinian desenvolupades en la Universitat de Camerino (Italià) i amb consums d'alcohol comparables als humans. Per a cada animal es van adquirir dades previs i després al consum de alcohol i dos condicions diferents de abstinència (amb i sense tractament anti-recaiguda). Dades de ressonància magnètica multimodal constituides per imatges de difusió, de relaxometria magnètica i estructurals van ser fusionades en esquemes analítics multivariats incorporant dues metodologies validades en el camp de neuroimatge, Random Forest i Support Vector Machine. Nostre esquema ha sigut aplicat amb dos objectius diferenciats. El primer objectiu es determinar en quina fase experimental es troba el subjecte a partir de biomarcadors obtinguts per neuroimatge. Per l'altra banda, el segon objectiu es identificar el sistemes cerebrals susceptibles de ser alterats durant una important ingesta de alcohol i la seua evolució durant la fase del tractament. El nostres resultats demostraren que l'ús de biomarcadors derivats de varies modalitats de neuroimatge fusionades en un anàlisis multivariat produeixen diagnòstics més exactes que els derivats de una única modalitat (fins un 16% de millora). Biomarcadors derivats de imatges de difusió i relaxometria van contribuir de distints estats experimentals. També s'han identificat aspectes innats que estan relacionades amb posterior preferències d'alcohol o la relació entre la resposta al tractament anti-recaiguda i les dades de ressonància magnètica. En resum, al llarg de aquest treball, es demostra que l'ús de dades de ressonància magnètica multimodal en models animals combinats en esquemes analítics multivariats són una eina molt valida en l'enteniment i avanç de patologies psiquiàtriques com l'alcoholisme.<br>Cosa Liñán, A. (2017). Analytical fusion of multimodal magnetic resonance imaging to identify pathological states in genetically selected Marchigian Sardinian alcohol-preferring (msP) rats [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90523<br>TESIS

Ce projet doctoral a été réalisé au laboratoire BioMaps de l'Université Paris-Saclay et au CMPBME de l'Université Médicale de Vienne. Afin d’améliorer la valeur diagnostique de l'IRM, il est souhaitable de réduire les durées d’acquisition, d’avoir une prise en charge plus efficace des patients et une meilleure qualité des images. Dans ce but, une instrumentation portable avec un matériel optimisé permettrait de réduire le poids, d’augmenter la flexibilité et de transmettre sans fil les signaux RMN, améliorant ainsi la sensibilité, le confort, la sécurité et la facilité d'utilisation de ces dispositifs.Dans ce contexte, nous avons étudié des antennes RF souples à câbles coaxiaux basées sur le principe des résonateurs à lignes à transmission. Ces résonateurs, pouvant posséder plusieurs tours et/ou plusieurs fentes, permettent d'optimiser la taille de l’antenne RF en fonction de l'application visée. Le concept a d'abord été étudiée in silico. De nombreux prototypes ont été construits et leurs performances ont été testées sur table et en IRM à 3 et 7 T. Les antennes coaxiales ont révélé avoir des performances robustes à la déformation, ne dégradent pas le TAS et peuvent améliorer le RSB et l'efficacité de transmission lorsqu'elles sont conformées au relief de la zone imagée. En parallèle, nous avons mené une étude approfondie des technologies de transmission sans fil en IRM. Un premier prototype de communication optique sans fil pour la transmission de données de capteurs de mouvements a été réalisé et testé. Les antennes coaxiales portables que nous avons étudiées offrent une alternative intéressante aux antennes standard en raison de leur faible poids et de leur flexibilité<br>This PhD thesis work was conducted at the BioMaps laboratory at the Université Paris-Saclay and the Center for Medical Physics and Biomedical Engineering (CMPBME) at the Medical University of Vienna.To improve diagnostic value in MRI, shorter acquisitions, more efficient patient handling and improved image quality are needed. Wearable technology with optimized hardware reduces weight, increases flexibility, and could be wireless, thereby improving sensitivity, comfort, safety, and usability.In this work, flexible self-resonant coaxial transmission line resonators were investigated. Coaxial coils with multiple turns and gaps enable size optimization depending on the target application. The design was first studied in silico. Numerous prototypes were constructed and their performance was tested on the bench and in 3 and 7 T MRI. Coaxial coils were shown to be robust against bending, have no SAR penalty and improve SNR and transmit efficiency when form-fitted.A review of wireless MR, associated hardware developments and data transmission technology is given.An optical wireless communication module for sensor data transmission was demonstrated experimentally.Wearable coaxial coils offer an attractive alternative to standard coils due to low weight and flexibility. With wireless motion sensors diagnostic value in e.g. breast, knee, or cardiac MRI could be increased

This thesis is primarily concerned with developing new models and numerical methods based on the fractional generalisation of the Bloch and Bloch-Torrey equations to account for anomalous MRI signal attenuation. The two main contributions of the research are to investigate the anomalous evolution of MRI signals via the fractionalised Bloch equations, and to develop new effective numerical methods with supporting analysis to solve the time-space fractional Bloch-Torrey equations.

Esta dissertação apresenta os principais métodos estatísticos para analisar as séries temporais de fMRI com o objetivo de detectar regiões ativadas e caracterizar o erro envolvido nessa decisão. Na análise de imagens funcionais, devido à baixa razão sinal-ruído, torna-se necessário o uso de técnicas elaboradas de processamento. O resultado da aplicação de técnicas estatísticas sobre as séries temporais obtidas da imagem de fMRI, é um mapa estatístico paramétrico, (Statistical Parametric Map), (SPM), uma imagem 3-D que permite determinar o estado do voxel, ativado ou não ativado, e a significância estatística do resultado. Propomos um novo método baseado na Difusão Anisotrópica Robusta, (Robust Anisotropic Diffusion), (RAD), que explora uma característica fundamental da imagem funcional, a correlação espacial das regiões ativadas do cérebro humano. O método proposto permite obter mapas estatísticos que melhoram a determinação das áreas ativadas a partir de dados fMRI ruidosos. Os novos mapas estatísticos paramétricos, baseados na correlação espacial da imagem fMRI, reduzem os erros do processo de classificação dos voxels, melhorando assim o mapeamento das regiões ativadas no cérebro. Aplicamos a técnica proposta em dados gerados artificialmente, simulando ruído e sinal, e avaliamos o novo método proposto e um método clássico de processamento de fMRI. Apresentamos resultados comparativos entre um método clássico, o método de correlação e o novo método. Calculamos os erros envolvidos e apresentamos a curvas características de operação de um receptor, (Receiver Operating Characteristics), (ROC), para ambos métodos, comparando os parâmetros mais importantes. Também avaliamos o novo método em dados reais de fMRI de um experimento em blocos com estímulo visual.<br>This dissertation presents the main statistical methods to analyse fMRI temporal series to detect activated regions and to characterise the error involved in this decision. Due to low signal to noise ratio, elaborate processing techniques are necessary to analyse functional images. Statistical techniques are usually applied on the temporal series obtained from fMRI, resulting in a Statistical Parametric Map (SPM), a 3-D image that makes it possible to determine the state of a voxel, as activated or not activated, and the statistical significance of the result. We proposed a new, simple an elegant method based on Robust Anisotropic Diffusion (RAD) to exploit the spatial correlation of activated regions of the human brain. The new method, named Robust Anisotropic Diffusion of Statistical Parametric Maps (RADSPM), allows one to obtain statistical maps that improve the determination of activated areas from noisy fMRI data. The new parametric statistical maps, based on the voxel spatial correlation of the fMRI image, reduce the classification error thus improving the significance of the results. We have tested the new technique in both simulated and real fMRI, obtaining sharp and noiseless SPMs with increased statistical significance. We compare results of the new RADSPM method with those of a classic method, the conventional correlation method. We calculate the involved errors and we present Receiver Operating Characteristics (ROC) curves for both methods, comparing the most important parameters for simulated fMRI data. We also evaluate the new method on real data of a visual fMRI experiment.

Different medical imaging modalities provide complementary anatomical and functional information. One increasingly important use of such information is in the clinical management of cardiovascular disease. Multi-modality data is helping improve diagnosis accuracy, and individualize treatment. The Clinical Research Imaging Centre at the University of Edinburgh, has been involved in a number of cardiovascular clinical trials using longitudinal computed tomography (CT) and multi-parametric magnetic resonance (MR) imaging. The critical image processing technique that combines the information from all these different datasets is known as image registration, which is the topic of this thesis. Image registration, especially multi-modality and multi-parametric registration, remains a challenging field in medical image analysis. The new registration methods described in this work were all developed in response to genuine challenges in on-going clinical studies. These methods have been evaluated using data from these studies. In order to gain an insight into the building blocks of image registration methods, the thesis begins with a comprehensive literature review of state-of-the-art algorithms. This is followed by a description of the first registration method I developed to help track inflammation in aortic abdominal aneurysms. It registers multi-modality and multi-parametric images, with new contrast agents. The registration framework uses a semi-automatically generated region of interest around the aorta. The aorta is aligned based on a combination of the centres of the regions of interest and intensity matching. The method achieved sub-voxel accuracy. The second clinical study involved cardiac data. The first framework failed to register many of these datasets, because the cardiac data suffers from a common artefact of magnetic resonance images, namely intensity inhomogeneity. Thus I developed a new preprocessing technique that is able to correct the artefacts in the functional data using data from the anatomical scans. The registration framework, with this preprocessing step and new particle swarm optimizer, achieved significantly improved registration results on the cardiac data, and was validated quantitatively using neuro images from a clinical study of neonates. Although on average the new framework achieved accurate results, when processing data corrupted by severe artefacts and noise, premature convergence of the optimizer is still a common problem. To overcome this, I invented a new optimization method, that achieves more robust convergence by encoding prior knowledge of registration. The registration results from this new registration-oriented optimizer are more accurate than other general-purpose particle swarm optimization methods commonly applied to registration problems. In summary, this thesis describes a series of novel developments to an image registration framework, aimed to improve accuracy, robustness and speed. The resulting registration framework was applied to, and validated by, different types of images taken from several ongoing clinical trials. In the future, this framework could be extended to include more diverse transformation models, aided by new machine learning techniques. It may also be applied to the registration of other types and modalities of imaging data.

Le problème d'estimer une fonction de plusieurs variables à partir d'une observation corrompue surgit dans de nombreux domaines d'ingénierie. Par exemple, en imagerie médicale cette tâche a attiré une attention particulière et a, même, motivé l'introduction de nouveaux concepts qui ont trouvé des applications dans de nombreux autres domaines. Cet intérêt est principalement du au fait que l'analyse des données médicales est souvent effectuée dans des conditions difficiles car on doit faire face au bruit, au faible contraste et aux transformations indésirables inhérents aux systèmes d'acquisition. D'autre part , le concept de parcimonie a eu un fort impact sur la reconstruction et la restauration d'images au cours des deux dernières décennies. La parcimonie stipule que certains signaux et images ont des représentations impliquant seulement quelques coefficients non nuls. Cela est avéré être vérifiable dans de nombreux problèmes pratiques. La thèse introduit de nouvelles constructions d'a priori de parcimonie dans le cas des ondelettes et de la variation totale. Ces constructions utilisent une notion d'anisotopie généralisée qui permet de regrouper des variables ayant des comportements similaires : ces comportement peuvent peut être liée à la régularité de la fonction, au sens physique des variables ou bien au modèle d'observation. Nous utilisons ces constructions pour l'estimation non-paramétriques de fonctions. Dans le cas des ondelettes, nous montrons l'optimalité de l'approche sur les espaces fonctionnelles habituels avant de présenter quelques exemples d’applications en débruitage de séquences d'images, de données spectrales et hyper-spectrales, écoulements incompressibles ou encore des images ultrasonores. En suite, nous modélisons un problème déconvolution de données d'imagerie par résonance magnétique fonctionnelle comme un problème de minimisation faisant apparaître un a priori de variation totale structuré en espace-temps. Nous adaptons une généralisation de l'éclatement explicite-implicite pour trouver une solution au problème de minimisation<br>The problem of estimating a multivariate function from corrupted observations arises throughout many areas of engineering. For instance, in the particular field of medical signal and image processing, this task has attracted special attention and even triggered new concepts and notions that have found applications in many other fields. This interest is mainly due to the fact that the medical data analysis pipeline is often carried out in challenging conditions, since one has to deal with noise, low contrast and undesirable transformations operated by acquisition systems. On the other hand, the concept of sparsity had a tremendous impact on data reconstruction and restoration in the last two decades. Sparsity stipulates that some signals and images have representations involving only a few non-zero coefficients. The present PhD dissertation introduces new constructions of sparsity priors for wavelets and total variation. These construction harness notions of generalized anisotropy that enables grouping variables into sub-sets having similar behaviour; this behaviour can be related to the regularity of the unknown function, the physical meaning of the variables or the observation model. We use these constructions for non-parametric estimation of multivariate functions. In the case of wavelet thresholding, we show the optimality of the procedure over usual functional spaces before presenting some applications on denoising of image sequence, spectral and hyperspectral data, incompressible flows and ultrasound images. Afterwards, we study the problem of retrieving activity patterns from functional Magnetic Resonance Imaging data without incorporating priors on the timing, durations and atlas-based spatial structure of the activation. We model this challenge as a spatio-temporal deconvolution problem. We propose the corresponding variational formulation and we adapt the generalized forward-backward splitting algorithm to solve it

Gliomas are the most common primary brain tumours and are associated with poor prognosis. Among them, diffuse gliomas – which include their most aggressive form glioblastoma (GBM) – are known to be highly infiltrative. The diagnosis and follow-up of gliomas rely on positron emission tomography (PET) and magnetic resonance imaging (MRI). However, these imaging techniques do not currently allow to assess the whole extent of such infiltrative tumours nor to anticipate their preferred invasion patterns, leading to sub-optimal treatment planning. Mathematical tumour growth modelling has been proposed to address this problem. Reaction-diffusion tumour growth models, which are probably the most commonly used for diffuse gliomas growth modelling, propose to capture the proliferation and migration of glioma cells by means of a partial differential equation. Although the potential of such models has been shown in many works for patient follow-up and therapy planning, only few limited clinical applications have seemed to emerge from these works. This thesis aims at revisiting reaction-diffusion tumour growth models using state-of-the-art medical imaging and data processing technologies, with the objective of integrating multi-parametric PET/MRI data to further personalise the model. Brain tissue segmentation on MR images is first addressed with the aim of defining a patient-specific domain to solve the model. A previously proposed method to derive a tumour cell diffusion tensor from the water diffusion tensor assessed by diffusion-tensor imaging (DTI) is then implemented to guide the anisotropic migration of tumour cells along white matter tracts. The use of dynamic [S-methyl-11C]methionine ([11C]MET) PET is also investigated to derive patient-specific proliferation potential maps for the model. These investigations lead to the development of a microscopic compartmental model for amino acid PET tracer transport in gliomas. Based on the compartmental model results, a novel methodology is proposed to extract parametric maps from dynamic [11C]MET PET data using principal component analysis (PCA). The problem of estimating the initial conditions of the model from MR images is then addressed by means of a translational MRI/histology study in a case of non-operated GBM. Numerical solving strategies based on the widely used finite difference and finite element methods are finally implemented and compared. All these developments are embedded within a common framework allowing to study glioma growth in silico and providing a solid basis for further research in this field. However, commonly accepted hypothesis relating the outlines of abnormalities visible on MRI to tumour cell density iso-contours have been invalidated by the translational study carried out, leaving opened the questions of the initialisation and the validation of the model. Furthermore, the analysis of the temporal evolution of real multi-treated glioma patients demonstrates the limitations of the formulated model. These latter statements highlight current obstacles to the clinical application of reaction-diffusion tumour growth models and pave the way to further improvements.<br>Les gliomes sont les tumeurs cérébrales primitives les plus communes et sont associés à un mauvais pronostic. Parmi ces derniers, les gliomes diffus – qui incluent la forme la plus agressive, le glioblastome (GBM) – sont connus pour être hautement infiltrants. Le diagnostic et le suivi des gliomes s'appuient sur la tomographie par émission de positons (TEP) ainsi que l'imagerie par résonance magnétique (IRM). Cependant, ces techniques d'imagerie ne permettent actuellement pas d'évaluer l'étendue totale de tumeurs aussi infiltrantes ni d'anticiper leurs schémas d'invasion préférentiels, conduisant à une planification sous-optimale du traitement. La modélisation mathématique de la croissance tumorale a été proposée pour répondre à ce problème. Les modèles de croissance tumorale de type réaction-diffusion, qui sont probablement les plus communément utilisés pour la modélisation de la croissance des gliomes diffus, proposent de capturer la prolifération et la migration des cellules tumorales au moyen d'une équation aux dérivées partielles. Bien que le potentiel de tels modèles ait été démontré dans de nombreux travaux pour le suivi des patients et la planification de thérapies, seules quelques applications cliniques restreintes semblent avoir émergé de ces derniers. Ce travail de thèse a pour but de revisiter les modèles de croissance tumorale de type réaction-diffusion en utilisant des technologies de pointe en imagerie médicale et traitement de données, avec pour objectif d'y intégrer des données TEP/IRM multi-paramétriques pour personnaliser davantage le modèle. Le problème de la segmentation des tissus cérébraux dans les images IRM est d'abord adressé, avec pour but de définir un domaine propre au patient pour la résolution du modèle. Une méthode proposée précédemment permettant de dériver un tenseur de diffusion tumoral à partir du tenseur de diffusion de l'eau évalué par imagerie DTI a ensuite été implémentée afin de guider la migration anisotrope des cellules tumorales le long des fibres de matière blanche. L'utilisation de l'imagerie TEP dynamique à la [S-méthyl-11C]méthionine ([11C]MET) est également investiguée pour la génération de cartes de potentiel prolifératif propre au patient afin de nourrir le modèle. Ces investigations ont mené au développement d'un modèle compartimental pour le transport des traceurs TEP dérivés des acides aminés dans les gliomes. Sur base des résultats du modèle compartimental, une nouvelle méthodologie est proposée utilisant l'analyse en composantes principales pour extraire des cartes paramétriques à partir de données TEP dynamiques à la [11C]MET. Le problème de l'estimation des conditions initiales du modèle à partir d'images IRM est ensuite adressé par le biais d'une étude translationelle combinant IRM et histologie menée sur un cas de GBM non-opéré. Différentes stratégies de résolution numérique basées sur les méthodes des différences et éléments finis sont finalement implémentées et comparées. Tous ces développements sont embarqués dans un framework commun permettant d'étudier in silico la croissance des gliomes et fournissant une base solide pour de futures recherches dans le domaine. Cependant, certaines hypothèses communément admises reliant les délimitations des anormalités visibles en IRM à des iso-contours de densité de cellules tumorales ont été invalidée par l'étude translationelle menée, laissant ouverte les questions de l'initialisation et de la validation du modèle. Par ailleurs, l'analyse de l'évolution temporelle de cas réels de gliomes multi-traités démontre les limitations du modèle. Ces dernières affirmations mettent en évidence les obstacles actuels à l'application clinique de tels modèles et ouvrent la voie à de nouvelles possibilités d'amélioration.<br>Doctorat en Sciences de l'ingénieur et technologie<br>info:eu-repo/semantics/nonPublished

The task of this thesis is to study the subject of perfusion analysis based on dynamic imaging with T2/T2* contrast. The focus was on the acquisition commonly used for DSC-MRI and especially in the acquisition pulse sequences that use images with different echo time, so called Multi-echo sequence. Principles of dynamic measurement by magnetic resonance imaging, the role of contrast agents and their influence on the relaxation times are described. It also describes the problems perfusion analysis, measurement and mathematical modeling parameters entering to the convolution dependency for getting perfusion parametersIn the experimental part is developed automatic algorithm to gain curves relaxation time T2 *. Next, the synthetic data are created and tested robustness estimate perfusion parameters against noise. In the next phase of work there are compared real scanned objects with using a conversion with T2 * and free of T2*. In the last phase of work is compared influence of length of used echo times on concentration curves and after perfusion analysis influence on resulting perfusion parameters.