High image fidelity in Magnetic Resonance Imaging (MRI) relies on precise magnetic field control of encoding gradient fields and background B0 magnetic fields. To ensure a high degree of accuracy in the spatial location of the proton spins and the resultant object geometry, conventional image encoding using linear gradient fields, as well as advanced techniques with non-linear encoding, requires field generating hardware capable of excellent field shaping capabilities and accuracy. Non-homogeneous B0 background fields in MR imaging cause faster relaxation, signal dropout, and geometry distortion, resulting in inferior image quality and reduced diagnostic accuracy. Besides manufacturing imperfections in the magnet and site conditions, the magnetic field inside the imaging object is not homogeneous due to the differences in geometries and magnetic properties of individual human tissues, which is recognized as the primary source of B0 variation in MRI. Considering the differences of B0 conditions across subjects, it is essential for MR imaging to utilize flexible B0 shimming techniques such as active shimming in order to produce a highly homogeneous B0 field. The control capability and optimized control strategy for these magnetic fields require the development of new hardware and methodologies. B0 background field generated by the magnet and the encoding gradient field from gradient coil are two critical pillars of MR imaging. Since the multi-coil array provides advanced shim capability and is proven to be capable of imaging encoding with a compact size, it is considered a perfect component as a combination of B0 shim coil and encoding gradient coil for an accessible head-only MR scanner. MR scanners like this type provide unique features that will enable researchers to develop new MRI methodologies and conduct research into the functionalities of the human brain through more natural human behaviors. Its clinical applications will be more accessible to the general population for disease screening and diagnosis due to its portability and low energy requirements. Since the multi-coil array has the advantage of smaller volume and wall thickness than the traditional gradient coil, its design and implementation is challenging because of its compact space, irregular curved shape of coil elements, mechanical reliability requirements during scan and good thermal control for long working periods. It was the challenges involved in the design and implementation of the multi-coil array that initiated the first project of my dissertation. In this project, we present 1) a novel molding method for the construction of resin-impregnated wire patterns with irregular curved shapes along with a microcontroller-driven motorized machine for automated coil construction, 2) the design and validation of a water-cooling system using multiple parallel pipes impregnated with thermal epoxy, 3) a quality-controlled procedure of building the multi-coil array employing the technique of vacuum resin infusion. A multi-coil array was fabricated successfully and evaluated in multiple sites and then integrated into the first-prototype of the accessible head-only MR scanner. The similar quality of experimental images from the fabricated multi-coil array compared to those from conventional gradient coils indicates that the multi-coil array can effectively shape fields for both image encoding and B0 shimming. Our lab has shown that multi-coil technology offers advanced shim capability when imaging the human brain, but it could potentially benefit the imaging of other organs like the heart. The MR imaging of the heart is subject to dark band artifacts or signal loss caused by B0 inhomogeneity, which can result in misinterpretation of lesions and a reduction in diagnostic accuracy. It has been demonstrated in a recent study that the use of multi-coil techniques can significantly reduce B0 inhomogeneity within the heart based on shim analysis using in vivo B0 maps. Multi-coil arrays are not a standard configuration in commercial scanners but are normally used for research, B0 shimming is typically implemented by using the commonly-installed spherical harmonic shim coils in the first, second, and potentially third orders. The development of multi-coil technology, more in-depth design of the coil structure and geometry as well as the optimal use of the current spherical harmonic shim technology require a thorough understanding of cardiac B0 conditions across subjects and at a population level. Since the in vivo cardiac B0 measurement is not a routine clinical protocol and dedicated in vivo measurement for a large sample size are extremely labor intensive and expensive, the lack of such B0 data is a long-standing problem, especially for the subject groups like pediatric or elderly patients who cannot undergo B0 map measurement with breath hold. This challenge could be resolved by the use of B0 simulation on the basis of structural images from different imaging modalities, assuming that the B0 distributions inside the human heart depends on the anatomical structures surrounding heart and across the entire body. The challenge and assumption led to my second project regarding B0 magnetic field simulation in the human heart. We proposed a novel B0 simulation approach based on chest-abdomen-pelvis structural CT images and validated it using in vivo acquired B0 maps in the heart from the same subjects. This B0 simulation approach was then applied to CT images from more than one thousand subjects and the resultant large set of simulated B0 maps were analyzed with different shim types for searching optimal shim solution based on popular spherical harmonic decomposition. The derived B0 conditions were also statistically analyzed for potential correlation and linear association with demographic parameters of these subjects for investigating potential population-based shim strategy. By the use of in vivo acquisition, we also investigated the B0 magnetic field variation across cardiac cycle and evaluated the impact of these variations on in vivo cardiac B0 shimming. The results of this study allow us to better understand the primary sources and characteristics of B0 distributions in the heart as well as pave the way for developing optimal B0 shim methods within heart in both subject-specific and population-based manners.

Magnetic Resonance Imaging (MR Imaging, or MRI) offers superior soft-tissue contrast compared to other medical imaging modalities. However, access to MRI across developing countries ranges from prohibitive to scarcely available. The lack of educational facilities and the excessive costs involved in imparting technical training have resulted in a lack of skilled human resources required to operate MRI systems in developing countries. While diagnostic medical imaging improves the utilization of facility-based rural health services and impacts management decisions, MRI requires technical expertise to set up the patient, acquire, visualize, and interpret data. The availability of such local expertise in underserved geographies is challenging. Inefficient workflows and usage of MRI result in challenges related to financial and temporal access in countries with higher scanner densities than the global average of 5.3 per million people. MRI is routinely employed for neuroimaging and, in particular, for dementia screening. Dementia affected 50 million people worldwide in 2018, with an estimated economic impact of US $1 trillion a year, and Alzheimer’s Disease (AD) accounts for up to 60–80% of dementia cases. However, AD-imaging using MRI is time-consuming, and protocol optimization to accelerate MR Imaging requires local expertise since each pulse sequence involves multiple configurable parameters that need optimization for acquisition time, image contrast, and image quality. The lack of this expertise contributes to the highly inefficient utilization of MRI services, diminishing their clinical value. Augmenting human capabilities can tackle these challenges and standardize the practice. Autonomous and time-efficient acquisition, reconstruction, and visualization schemes to maximize MRI hardware usage and solutions that reduce reliance on human operation of MRI systems could alleviate some of the challenges associated with the requirement/absence of skilled human resources. We first present a preliminary demonstration of AMRI that simplifies the end-to-end MRI workflow of registering the subject, setting up and invoking an imaging session, acquiring and reconstructing the data, and visualizing the images. Our initial implementation of AMRI separates the required intelligence and user interaction from the acquisition hardware. AMRI performs intelligent protocolling and intelligent slice planning. Intelligent protocolling optimizes contrast value while satisfying signal-to-noise ratio and acquisition time constraints. We acquired data from four healthy volunteers across three experiments that differed in acquisition time constraints. AMRI achieved comparable image quality across all experiments despite optimizing for acquisition duration, therefore indirectly optimizing for MR Value – a metric to quantify the value of MRI. We believe we have demonstrated the first Autonomous MRI of the brain. We also present preliminary results from a deep learning (DL) tool for generating first-read text-based radiological reports directly from input brain images. It can potentially alleviate the burden on radiologists who experience the seventh-highest levels of burnout among all physicians, according to a 2015 survey. Next, we accelerate the routine brain imaging protocol employed at the Columbia University Irving Medical Center and leverage DL methods to boost image quality via image-denoising. Since MR physics dictates that the volume of the object being imaged influences the amount of signal received, we also demonstrate subject-specific image-denoising. The accelerated protocol resulted in a factor of 1.94 gain in imaging throughput, translating to a 72.51% increase in MR Value. We also demonstrate that this accelerated protocol can potentially be employed for AD imaging. Finally, we present ArtifactID – a DL tool to identify Gibbs ringing in low-field (0.36 T) and high-field (1.5 T and 3.0 T) brain MRI. We train separate binary classification models for low-field and high-field data, and visual explanations are generated via the Grad-CAM explainable AI method to help develop trust in the models’ predictions. We also demonstrate detecting motion using an accelerometer in a low-field MRI scanner since low-field MRI is prone to artifacts. In conclusion, our novel contributions in this work include: i) a software framework to demonstrate an initial implementation of autonomous brain imaging; ii) an end-to-end framework that leverages intelligent protocolling and DL-based image-denoising that can potentially be employed for accelerated AD imaging; and iii) a DL-based tool for automated identification of Gibbs ringing artifacts that may interfere with diagnosis at the time of radiological reading. We envision AMRI augmenting human expertise to alleviate the challenges associated with the scarcity of skilled human resources and contributing to globally accessible MRI.

Characterizing the cortical representation of sweet and bitter tastes in awake behaving mice has been challenging due to the sheltered location of the gustatory cortex in the insula, which restricts optical and electrophysiological recordings, and the various functions of the insula, complicating conclusions about taste representation. To overcome these obstacles, we developed a brain-wide imaging paradigm that combines functional magnetic resonance imaging (fMRI) with optogenetic and taste receptor knock-out manipulations in mice. This approach allowed us the study the gustatopic map in awake, behaving mice, showing that sweet and bitter tastes are topographically segregated along the rostro-caudal axis of the gustatory cortex, with sweet represented rostrally and bitter caudally. Notably, this map is subject to plasticity following conditioned taste aversion (CTA) for sweet stimuli, resulting in a posterior shift of the sweet taste field—a phenomenon reversible after CTA extinction.

Contemporary understanding of human visual spatial attention rests on the hypothesis of a top-down control sending from cortical regions carrying higher-level functions to sensory regions. Evidence has been gathered through functional Magnetic Resonance Imaging (fMRI) experiments. The Frontal Eye Field (FEF) and IntraParietal Sulcus (IPS) are candidates proposed to form the frontoparietal attention network for top-down control. In this work we examined the influence patterns between frontoparietal network and Visual Occipital Cortex (VOC) using a statistical measure, Granger Causality (GC), with fMRI data acquired from subjects participated in a covert attention task. We found a directional asymmetry in GC between FEF/IPS and VOC, and further identified retinotopically specific control patterns in top-down GC. This work may lead to deeper understanding of goal-directed attention, as well as the application of GC to analyzing higher-level cognitive functions in healthy functioning human brain. / by Wei Tang. / Thesis (Ph.D.)--Florida Atlantic University, 2011. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web.

Functional magnetic resonance imaging (fMRI) as it exists, in its many forms and vari- ants, has revolutionized the fields of neurology and psychology by revealing functional differences non-invasively. Although blood oxygenation level dependent (BOLD) fMRI is used interchangeably with fMRI, it measures one single difference in a phys- iological measurement using a set sequence. As such, there are other established changes in the brain that relate to blood movement and capacity that can also be measured using MRI. One measure, exogenous steady state cerebral blood volume, uses a bolus routine contrast agent administered intravenously alongside a pair of high resolution ‘structural-like’ MRI images to provide detailed information within small cortical and subcortical structures. In this thesis I design a semi-automated algorithm to generate maps of steady state exogenous cerebral blood volume magnetic resonance imaging datasets. To do this I developed an algorithm and tested it on existing MRI scanning protocols. A series of automated pre-processing steps are developed and tested, including automated scan flagging for artifacts and requisite vascular segmentation. Then, a methodology is developed to create cerebral blood volume (CBV) region of interest (ROI) masks that can then be applied on an existing database to test known CBV dysfunction in a group of patients at high risk for psychosis. Finally, we develop an experiment to see if template based cerebral blood alterations co-registered with class segmentation maps have any positive predictive value in determining disease state in a well characterized cohort of five age-matched groups in an Alzheimer’s disease neuroimaging study.

Includes vita. / Title from PDF of title page. / Document formatted into pages; contains 222 pages. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: This dissertation presents a knowledge-guided expert system that is capable of applying routinesfor multispectral analysis, (un)supervised clustering, and basic image processing to automatically detect and segment brain tissue abnormalities, and then label glioblastoma-multiforme brain tumors in magnetic resonance volumes of the human brain. The magnetic resonance images used here consist of three feature images (T1-weighted, proton density, T2-weighted) and the system is designed to be independent of a particular scanning protocol. Separate, but contiguous 2D slices in the transaxial plane form a brain volume. This allows complete tumor volumes to be measured and if repeat scans are taken over time, the system may be used to monitor tumor response to past treatments and aid in the planning of future treatment. Furthermore, once processing begins, the system is completely unsupervised, thus avoiding the problems of human variability found in supervised segmentation efforts.Each slice is initially segmented by an unsupervised fuzzy c-means algorithm. The segmented image, along with its respective cluster centers, is then analyzed by a rule-based expert system which iteratively locates tissues of interest based on the hierarchy of cluster centers in feature space. Model-based recognition techniques analyze tissues of interest by searching for expected characteristics and comparing those found with previously defined qualitative models. Normal/abnormal classification is performed through a default reasoning method: if a significant model deviation is found, the slice is considered abnormal. Otherwise, the slice is considered normal. Tumor segmentation in abnormal slices begins with multispectral histogram analysis and thresholding to separate suspected tumor from the rest of the intra-cranial region. The tumor is then refined with a variant of seed growing, followed by spatial component analysis and a final thresholding step to remove non-tumor pixels.The knowledge used in this system was extracted from general principles of magnetic resonance imaging, the distributions of individual voxels and cluster centers in feature space, and anatomical information. Knowledge is used both for single slice processing and information propagation between slices. A standard rule-based expert system shell (CLIPS) was modified to include the multispectral analysis, clustering, and image processing tools.A total of sixty-three volume data sets from eight patients and seventeen volunteers (four with and thirteen without gadolinium enhancement) were acquired from a single magnetic resonance imaging system with slightly varying scanning protocols were available for processing. All volumes were processed for normal/abnormal classification. Tumor segmentation was performed on the abnormal slices and the results were compared with a radiologist-labeled ground truth' tumor volume and tumor segmentations created by applying supervised k-nearest neighbors, a partially supervised variant of the fuzzy c-means clustering algorithm, and a commercially available seed growing package. The results of the developed automatic system generally correspond well to ground truth, both on a per slice basis and more importantly in tracking total tumor volume during treatment over time. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.

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% 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.

The field of cognitive neuroscience has benefited greatly from multimodal investigations of the human brain, which integrate various tools and neuroimaging data to understand brain functions and guide treatments for brain disorders. In this dissertation, we present a series of studies that illustrate the use of multimodal approaches to investigate brain structure and function, brain connectivity, and neuromodulation effects. Firstly, we propose a novel landmark-guided region-based spatial normalization technique to accurately quantify brain morphology, which can improve the sensitivity and specificity of functional imaging studies. Subsequently, we shift the investigation to the characteristics of functional brain activity due to visual stimulations. Our findings reveal that the task-evoked positive blood-oxygen-level dependent (BOLD) response is accompanied by sustained negative BOLD responses in the visual cortex. These negative BOLD responses are likely generated through subcortical neuromodulatory systems with distributed ascending projections to the cortex. To further explore the cortico-subcortical relationship, we conduct a multimodal investigation that involves simultaneous data acquisition of pupillometry, electroencephalography (EEG), and functional magnetic resonance imaging (fMRI). This investigation aims to examine the connectivity of brain circuits involved in the cognitive processes of salient stimuli. Using pupillary response as a surrogate measure of activity in the locus coeruleus-norepinephrine system, we find that the pupillary response is associated with the reorganization of functional brain networks during salience processing. In addition, we propose a cortico-subcortical integrated network reorganization model with potential implications for understanding attentional processing and network switching. Lastly, we employ a multimodal investigation that involves concurrent transcranial magnetic stimulation (TMS), EEG, and fMRI to explore network perturbations and measurements of the propagation effects. In a preliminary exploration on brain-state dependency of TMS-induced effects, we find that the propagation of left dorsolateral prefrontal cortex TMS to regions in the lateral frontoparietal network might depend on the brain-state, as indexed by the EEG prefrontal alpha phase. Overall, the studies in this dissertation contribute to the understanding of the structural and functional characteristics of brain network systems, and may inform future investigations that use multimodal methodological approaches, such as pupillometry, brain connectivity, and neuromodulation tools. The work presented in this dissertation has potential implications for the development of efficient and personalized treatments for major depressive disorder, attention deficit hyperactivity disorder, and Alzheimer's disease.

Over 50 million people globally suffer from neurodegenerative disorders—a number that is steadily increasing as the general population ages. Yet, effective treatments for neurodegenerative disorders including Alzheimer’s disease (AD), Parkinson’s disease (PD), and Huntington’s disease (HD) remain limited, primarily due to the presence of a natural protective biological barrier lining cerebral blood vessels called the blood-brain barrier (BBB). The blood- brain barrier prevents passage of nearly 98% of small molecules from blood vessels to brain tissue, while most therapies designed for neurodegenerative disorders, such as gene therapies, are considered large-molecule drugs, making development of efficacious pharmacological treatments extremely challenging. Present strategies to bypass the BBB for drug delivery broadly fall into two categories: non-invasive but non-targeted methods, or targeted but invasive surgical procedures such as direct intracranial injection. Currently, the only method poised clinically to provide both non-invasive and targeted drug delivery to the brain is focused ultrasound (FUS). When combined with intravenously administered ultrasound contrast agents called microbubbles which oscillate within blood vessels in response to FUS pressure waves, FUS can safely and reversibly open the blood-brain barrier (BBB) in a highly targeted manner. This enhances drug delivery to brain regions affected by neurodegenerative disorders through a physical mechanism known as acoustic cavitation. A majority of FUS research to date has centered around development and clinical translation of stereotactic FUS guided by magnetic resonance imaging (MRI) for treatment monitoring, commonly referred to as MRgFUS. However, MRgFUS exhibits cost, accessibility, and portability barriers to implementation in medical centers globally. Alternatively, our group has developed cost-effective and accessible ultrasound-guided FUS (USgFUS) configurations, which have the potential to enable BBB opening and drug delivery treatment outside of an MRI with treatment guidance facilitated by neuro-navigation technology and cavitation monitoring. While most USgFUS systems developed prior to this dissertation achieve therapeutic opening of the BBB and cavitation monitoring with separate ultrasound transducers, this thesis focuses primarily on development and optimization of a single-transducer technique for both therapy and monitoring called theranostic ultrasound (ThUS). In Aim 1, we show that a repurposed diagnostic ultrasound array reprogrammed with focused imaging pulses can produce therapeutically relevant ultrasound energy through primate skulls, and can induce multi-site modulatory drug and gene delivery depending on the ThUS parameters applied. In Aims 2 and 3, we apply ThUS-mediated drug and gene delivery for pre-clinical neuroscience and therapeutic applications in PD, respectively. In Aim 2, we demonstrated non-invasive delivery of specialized genes and nanoparticles which together enable remote stimulation and recording of neuronal activity, a synergistic process which could enable remote brain-to-brain communication. In Aim 3, we leveraged ThUS-mediated gene therapy to restore degenerated neurons in a PD mouse model, achieving nearly 85% restoration of diseased dopaminergic neurons non-invasively. Finally, in Aim 4, we translated ThUS-mediated BBB opening to non-human primates (NHP) to determine initial feasibility of targeted gene expression facilitated by a low frequency, custom ThUS array. We demonstrated that both conventional USgFUS and ThUS configurations can safely induce targeted gene expression in brain regions implicated in PD in rhesus macaques, motivating translation of USgFUS for gene therapy in the clinic. The aims in this dissertation collectively underscore the growing number of pre-clinical applications which could benefit from ThUS technology, while propelling USgFUS methodologies as a whole to the brink of clinical translation for unprecedented access to efficacious non-invasive gene therapy for neurodegenerative disorders in the future.

<p align="justify">L'Imagerie fonctionnelle par Résonance Magnétique (IRMf) a connu un développement important depuis sa découverte au début des années 1990. Basée le plus souvent sur l'effet BOLD (Blood Oxygenation Level Dependent), cette technique permet d'obtenir de façon totalement non-invasive des cartes d'activation cérébrale, avec de meilleures résolutions spatiale et temporelle que les méthodes préexistantes telles que la tomographie par émission de positrons (TEP). Facilement praticable au moyen des imageurs par RMN disponible dans les hôpitaux, elle a mené à de nombreuses applications dans le domaine des neurosciences et de l'étude des pathologies cérébrales.</p><p><p align="justify">Il est maintenant bien établi que l'effet BOLD est dû à une augmentation de l'oxygénation du sang veineux dans les régions du cerveau où se produit l'activation neuronale, impliquant une diminution de la différence de susceptibilité magnétique entre le sang et les tissus environnants (la déoxyhémoglobine étant paramagnétique et l'oxyhémoglobine diamagnétique), et par conséquent un augmentation du signal si la méthode d'acquisition est sensible aux inhomogénéités de champ magnétique. Cependant, il reste encore de nombreuses inconnues quant aux mécanismes liant les variations d'oxygénation, de flux et de volume sanguin à l'augmentation de signal observée, et la dépendance du phénomène en des paramètres tels que l'intensité du champ, la résolution spatiale, et le type de séquence de RMN utilisée. La première partie de la thèse est donc consacrée à l'étude de l'effet BOLD, dans le cas particulier des contributions dues aux veines de drainage dans les séquences de type écho de gradient rendues sensibles au mouvement par l'ajout de gradients de champ. Le modèle développé montre que, contrairement au comportement suggéré par de précédentes publications, l'effet de ces gradients n'est pas une diminution monotone de la différence de signal lorsque l'intensité des gradients augmente. D'importantes oscillations sont produites par l'effet de phase dû au déplacement des spins du sang dans les gradients additionnels, et par la variation de cette phase suite à l'augmentation du flux sanguin. La validation expérimentale du modèle est réalisée au moyen de la séquence PRESTO (Principles of Echo-Shifting combined with a Train of Observations), c'est-à-dire une séquence en écho de gradient où des gradients supplémentaires permettent d'augmenter la sensibilité aux inhomogénéités de champ, et donc à l'effet BOLD. Un accord qualitatif avec la théorie est établi en montrant que la variation de signal observée peut augmenter lorsqu'on intensifie les gradients additionnels.</p><p><p align="justify">Un autre source de débat continuel dans le domaine de l'IRMf réside dans l'optimalisation des méthodes d'acquisition, au point de vue notamment de leur sensibilité à l'effet BOLD, leurs résolutions spatiale et temporelle, leur sensibilité à divers artefacts tels que la perte de signal dans les zones présentant des inhomogénéités de champ à grande échelle, et la contamination des cartes d'activation par les contributions des grosses veines, qui peuvent être distantes du lieu d'activation réel. Les séquences en écho de spin sont connues pour être moins sensibles à ces deux derniers problèmes, c'est pourquoi la deuxième partie de la thèse est consacrée à une nouvelle technique permettant de donner une pondération T2 plutôt que T2* aux images. Le principe de base de la méthode n'est pas neuf, puisqu'il s'agit de la « Préparation T2 » (T2prep), qui consiste à atténuer l'aimantation longitudinale différemment selon la valeur du temps de relaxation T2, mais il n’avait jamais été appliqué à l’IRMf. Ses avantages par rapport à d’autres méthodes hybrides T2 et T2* sont principalement le gain en résolution temporelle et en dissipation d’énergie électromagnétique dans les tissus. Le contraste généré par ces séquences est étudié au moyen de solutions stationnaires des équations de Bloch. Des prédictions sont faites quant au contraste BOLD, sur base de ces solutions stationnaires et d’une description simplifiée de l’effet BOLD en termes de variations de T2 et T2*. Une méthode est proposée pour rendre le signal constant au travers du train d’impulsions en faisant varier l’angle de bascule d’une impulsion à l’autre, ce qui permet de diminuer le flou dans les images. Des expériences in vitro montrent un accord quantitatif excellent avec les prédictions théoriques quant à l’intensité des signaux mesurés, aussi bien dans le cas de l’angle constant que pour la série d’angles variables. Des expériences d’activation du cortex visuel démontrent la faisabilité de l’IRMf au moyen de séquences T2prep, et confirment les prédictions théoriques quant à la variation de signal causée par l’activation.</p><p><p align="justify"> La troisième partie de la thèse constitue la suite logique des deux premières, puisqu’elle est consacrée à une extension du principe de déplacement d’écho (echo-shifting) aux séquences en écho de spin à l’état stationnaire, ce qui permet d’obtenir une pondération T2 et T2* importante tout en maintenant un temps de répétition court, et donc une bonne résolution temporelle. Une analyse théorique approfondie de la formation du signal dans de telles séquences est présentée. Elle est basée en partie sur la technique de résolution des équations de Bloch utilisée dans la deuxième partie, qui consiste à calculer l’aimantation d’état stationnaire en fonction des angles de précession dans le plan transverse, puis à intégrer sur les isochromats pour obtenir le signal résultant d’un voxel (volume element). Le problème est aussi envisagé sous l’angle des « trajectoires de cohérence », c’est-à-dire la subdivision du signal en composantes plus ou moins déphasées, par l’effet combiné des impulsions RF, des gradients appliqués et des inhomogénéités du champ magnétique principal. Cette approche permet d’interpréter l’intensité du signal dans les séquences à écho déplacé comme le résultat d’interférences destructives entre diverses composantes physiquement interprétables. Elle permet de comprendre comment la variation de la phase de l’impulsion d’excitation (RF-spoiling) élimine ces interférences. Des expériences in vitro montrent un accord quantitatif excellent avec les calculs théoriques, et la faisabilité de la méthode in vivo est établie. Il n’est pas encore possible de conclure quant à l’applicabilité de la nouvelle méthode dans le cadre de l’IRMf, mais l’approche théorique proposée a en tout cas permis de revoir en profondeur les mécanismes de formation du signal pour l’ensemble des méthodes à écho déplacé, puisque le cas de l’écho de gradient s’avère complètement similaire au cas de l’écho de spin.</p><p><p align="justify">La thèse évolue donc progressivement de la modélisation de l’effet BOLD vers la conception de séquences, permettant ainsi d’aborder deux aspects fondamentaux de la physique de l’IRMf.</p><p> / Doctorat en sciences appliquées / info:eu-repo/semantics/nonPublished