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The work described in this thesis deals mainly with aspects related to two- and three-dimensional NMR imaging. A detailed discussion on frequency-selective excitation using amplitude modulated rf pulses in relation to slice selection in NMR imaging has been presented. This includes the analysis and implementation of the method as well as illustrative experimental results. Several radiofrequency probe designs suitable for high field NMR imaging have been experimentally evaluated and their modification and construction are also described. The comparative results obtained indicate the merits and demerits of different designs and provide necessary guidelines for selecting the most suitable design depending on the application. Practical aspects of two- and three-dimensional imaging have been discussed and NMR images of several intact systems have been presented. Experimental methods which enable slice selection in the presence of chemically shifted species and two-dimensional chemical shift resolved imaging have "been described and illustrated using phantoms. The use of three-dimensional chemical shift resolved imaging as a potential method to map the pH and temperature distribution within an object has also been demonstrated. A preliminary investigation of the application of ³¹P NMR spectroscopy to study the biochemical transformations of the rat kidney during periods of ischemia and reperfusion has been presented. / Science, Faculty of / Chemistry, Department of / Graduate

Photodynamic therapy (PDT) and Magnetic resonance imaging (MRI) are two techniques used in therapeutic and diagnostic purpose respectively. PDT can selectively kill the cancer cells by utilizing light and photosensitizer, while MRI provides invasive imaging on our interior bodies. If these two techniques combine, the probe can act as both PDT and MRI agent at the same time. This theranostic agents can bring great efficiency in the cancer treatment. In this project, a porphyrin-cyclen gadolinium based dual functions bio-probe, PZnGdL, is designed for diagnostic and photodynamic therapeutic functions. PZnGdL demonstrated a great T1 signal enhancement for MRI, in which its T1 relaxivity is 15.06 mM-1s-1 (at 1.4T, 37oC). The T1 relaxivity is five-fold higher than the clinically approved MRI contrasting agent Gd-DOTA, (2.92 at 1.4T, 37oC). Furthermore, PZnGdL exhibits low dark toxicity and high photocytotoxicity. Therefore, its photodynamic therapeutic index (PDI) in HeLa cells is as high as 1348 upon 1 J/cm2 light irradiation. Results from the present study show that PZnGdL is an effective photodynamic therapy agent as well as a safe and promising MRI contrasting agent.

The brain activities are characterized by spontaneous and persistent irregular fluctuations in space and time. Criticality theory from statistical physics has been proposed as a principle to explain the variability in normal brain spontaneous activity and has suggested the functional benefits of variability, such as maximized dynamic range of response to stimuli and information capacity. In parallel, the brains show variability in other aspects, such as the structural heterogeneity across brain regions, the intra-individual variability across experimental trials, and the behavior difference across groups and individuals. The associations between the variability of spontaneous activities and these different types of structural, intra and inter-individual variabilities remain elusive. My doctoral study thus aimed to bridge the brain variability and the above-mentioned variations based on criticality theory and analysis of empirical data. As a preparatory analysis, we first collected evidence to prove criticality in human functional magnetic resonance imaging (fMRI) data. The advanced statistical criteria were used to exclude potential artefacts that can induce power-law scaling without the mechanism of criticality. In the first part of the study, we addressed methodological issue and tested whether several measures of either spatial or temporal complexity due to experimental limitations could be reliable proxy of spatiotemporal variability (related to criticality) in vivo. The high spatiotemporal resolutions of whole-cortex optical voltage imaging in mice brain during the waking up from anesthesia enabled simultaneous investigation of functional connectivity (FC), Multi-Scale Entropy (MSE, measure of temporal variability), Regional Entropy (RE, quantity of spatiotemporal variability) and the interdependency among them under different brain states. The results suggested that MSE and FC could be effective measures to capture spatiotemporal variability under limitation of imaging modalities applicable to human subjects. This study also lays methodological basis for the third study in this thesis. In the second study, we explored the interaction between spontaneous activity and evoked activity from mice brain imaging under whisker stimulus. The whisker stimulus will first evoke the local activation in sensory cortex and then trigger whole-cortex activity with variable patterns in different experimental trials. This trial-to-trial variability in the cortical evoked component was then attributed to the changes of ongoing activity state at stimulus onset. The study links ongoing activity variability and evoked activity variability, which further consolidates the association between ongoing activity and brain functions. In the third study, we measured the signal variability of the whole brain from resting state fMRI, and developed the multivariate pattern of cortical entropy, called entropy profile, as reliable and interpretable biomarker of individual difference in cognitive ability. We showed that the whole cortical entropy profile from resting- state fMRI is a robust personalized measure. We tested the predictive power for general and specific cognitive abilities based on cortical entropy profiles with out- of-sample prediction. Furthermore, we revealed the anatomical features underlying cross-region and cross-individual variations in cortical entropy profiles. This study provides new potential biomarker based on brain spontaneous variability which could benefit the applications in psychology and psychiatry studies. The whole study laid a foundation for brain criticality-/variability-based studies and applications and broadened our understanding of the associations between neural structures, functional dynamics and cognitive ability

The brain activities are characterized by spontaneous and persistent irregular fluctuations in space and time. Criticality theory from statistical physics has been proposed as a principle to explain the variability in normal brain spontaneous activity and has suggested the functional benefits of variability, such as maximized dynamic range of response to stimuli and information capacity. In parallel, the brains show variability in other aspects, such as the structural heterogeneity across brain regions, the intra-individual variability across experimental trials, and the behavior difference across groups and individuals. The associations between the variability of spontaneous activities and these different types of structural, intra and inter-individual variabilities remain elusive. My doctoral study thus aimed to bridge the brain variability and the above-mentioned variations based on criticality theory and analysis of empirical data. As a preparatory analysis, we first collected evidence to prove criticality in human functional magnetic resonance imaging (fMRI) data. The advanced statistical criteria were used to exclude potential artefacts that can induce power-law scaling without the mechanism of criticality. In the first part of the study, we addressed methodological issue and tested whether several measures of either spatial or temporal complexity due to experimental limitations could be reliable proxy of spatiotemporal variability (related to criticality) in vivo. The high spatiotemporal resolutions of whole-cortex optical voltage imaging in mice brain during the waking up from anesthesia enabled simultaneous investigation of functional connectivity (FC), Multi-Scale Entropy (MSE, measure of temporal variability), Regional Entropy (RE, quantity of spatiotemporal variability) and the interdependency among them under different brain states. The results suggested that MSE and FC could be effective measures to capture spatiotemporal variability under limitation of imaging modalities applicable to human subjects. This study also lays methodological basis for the third study in this thesis. In the second study, we explored the interaction between spontaneous activity and evoked activity from mice brain imaging under whisker stimulus. The whisker stimulus will first evoke the local activation in sensory cortex and then trigger whole-cortex activity with variable patterns in different experimental trials. This trial-to-trial variability in the cortical evoked component was then attributed to the changes of ongoing activity state at stimulus onset. The study links ongoing activity variability and evoked activity variability, which further consolidates the association between ongoing activity and brain functions. In the third study, we measured the signal variability of the whole brain from resting state fMRI, and developed the multivariate pattern of cortical entropy, called entropy profile, as reliable and interpretable biomarker of individual difference in cognitive ability. We showed that the whole cortical entropy profile from resting- state fMRI is a robust personalized measure. We tested the predictive power for general and specific cognitive abilities based on cortical entropy profiles with out- of-sample prediction. Furthermore, we revealed the anatomical features underlying cross-region and cross-individual variations in cortical entropy profiles. This study provides new potential biomarker based on brain spontaneous variability which could benefit the applications in psychology and psychiatry studies. The whole study laid a foundation for brain criticality-/variability-based studies and applications and broadened our understanding of the associations between neural structures, functional dynamics and cognitive ability

Cette thèse a pour objectif de parvenir à une estimation automatisée des contours du ventricule gauche sur des images IRM en coupes petit-axe, nécessitant un minimum d’interventions de la part de l’utilisateur. En s’appuyant sur une approche semi-automatique récemment développée, une méthode entièrement automatique est proposée, reposant sur la localisation des structures cardiaques et la création d’une région d’intérêt autour du ventricule gauche, puis la segmentation de sa cavité. L’algorithme a été développé en prenant en compte des connaissances anatomiques et fonctionnelles sur le cœur : les caractéristiques temporelles du battement cardiaque, la pseudo-circularité des coupes petit-axe du ventricule gauche, la continuité 3D qui ont été combinées à l’intensité dans les images. La segmentation utilise une approche fondée sur les contours actifs combinée à un filtrage morphologique qui améliore la robustesse de la segmentation vis-à-vis des hétérogénéités au sein de la cavité. Le travail réalisé avec le groupe MedIEval (MedicalImaging Evaluation) a permis de comparer les deux méthodes avec 6 autres méthodes, dont 3 tracés d'experts. Une classification par une approche d’évaluation sans référence a été appliquée à la fraction d’éjection estimée par les 8 méthodes. Enfin, la méthode de segmentation proposée a été utilisée systématiquement dans une étude de recherche clinique combinant l’étude de la contraction régionale et la quantification de la transmuralité de l’infarctus du myocarde. Ces travaux ouvrent des perspectives, sur l’automatisation de la segmentation du ventricule droit et l’estimation d’une forme mutuelle robuste à partir de plusieurs segmentations. / The aim of this work is to perform an automated segmentation of the Left Ventricle on short-axis cardiac MR images with as few user interactions as possible. Based on a recently developed semi-automated segmentation method, a fully automated segmentation method is proposed that includes three main steps: the heart localization, the definition of a region of interest around the left ventricle, and finally its segmentation. The algorithm developed here takes into account anatomic and functional a priori information such as the temporal features of the heartbeat, the pseudo-circular shape of the LV, and the 3D continuity, combined with the image intensity features. The segmentation process is achieved using deformable models combined with morphological filters, which improve the model performances when dealing with heterogeneous gray levels within the cavity. The work achieved within the MedIEval group (Medical Imaging Evaluation) allowed to compare both proposed methods with 6 other methods, including 3 manual delineations by experts. In particular, an approach for ranking segmentation methods without using a gold standard was applied to the ejection fractions estimated by the 8 methods. Finally, the proposed segmentation method was used in a clinical research work about the regional contraction and thequantification of the myocardial infarction extent.Future work includes the automated segmentation of the right ventricle as well as the estimation of a robust mutual shape from several segmentation methods.

Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) method that resolves structures by three-dimensional measurement of water movement, and has shown to benefit studies of anisotropic tissues such as brain and skeletal muscle. While many studies have used DTI to non-invasively study static tissue architecture, little attempt has been made to use this technique to temporally characterize muscle during post-exercise recovery. Thus, the goal of this work was to use DTI to study the timecourse of changes in skeletal muscle following exercise. The first study was performed to test stability of DTI eigenvectors (ε1 ε2 ε3), and to determine how time-expensive parameters such as increased number of diffusion directions (NDD) or signal averages (NSA) improve vector stability. The ε2 vector was found to have more directional variability than ε1, and showed less improvement than ε1 with increased NDD or NSA. Furthermore, decreasing directional variability of ε1 was correlated with increasing NDD, not NSA (p<0.0008), while decreased variability of ε2 was correlated with increasing NSA, not NDD (p<0.0005). The variation in ε2 indicated that combining the corresponding minor eigenvalues into a measure of Radial Diffusivity is more robust than analyzing λ2 and λ3 eigenvalues separately. The second study tested the use of DTI to characterize temporal calf muscle changes following a mild in-bore dorsiflexion-eversion exercise. DTI volumes were acquired before and immediately after exercise. Anterior tibialis (ATIB), extensor digitorum longus (EDL), and peroneus longus (PER) showed significantly-elevated mean diffusivity (MD) post-exercise, while soleus (SOL) and lateral gastrocnemius (LG) did not (p<0.0001). The EDL showed greater initial MD increase and remained significantly elevated across more time points than ATIB or PER (p<0.05 to p=7.41x10−10). Significant signal increases were observed in post-exercise EDL b=0s/mm2 volumes (S0) relative to other muscles across the majority of timepoints (p<0.01 to p<0.001). The notable differences of EDL temporal MD and S0 relative to ATIB and PER may be related to the physiology of the increased Type-II fiber content in this muscle. The third and final study investigated the feasibility of a ’sliding window’ multiple-timescale temporal DTI approach, intended to acquire data with high temporal resolution and ongoing structural representation. Continuous diffusion data was acquired in the calf before and after four plantarflexion tasks, which varied by number of flexions (10 or 60), and weight load (10% or 40% of individual max). Apparent diffusion coefficient (ADC) and S0 were calculated from 3-direction subunits, while 15-direction subunits produced DTI measures such as mean diffusivity (MD). Four different post-exercise temporal patterns were observed for ADC, S0, and MD amongst the measured muscles: ’elevated-decline’, ’latent peak’, ’sub-to-peak’, and ’horizontal’. The 10-flex 10% condition elicited ’elevated-decline’ in active muscles, particularly SOLlat. Exercise of greater intensity produced ’latent peak’ and ’sub-to-peak’ patterns, with peak height related to greater workload. The 10-flex 40% trial produced a ’sub-to-peak’ pattern across all subjects only in the LG and MG, but ’latent-peak’ in these muscles 60-flex 40%. The specificity of temporal diffusion patterns according to muscle and task indicate that this technique could be beneficial to future studies of muscle function. These experiments have demonstrated the limits of DTI in the study of skeletal muscle, yet established a basis for future investigation of muscle dynamics using temporal diffusion methods. / Dissertation / Doctor of Philosophy (PhD)

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