635 research outputs found

    Simultaneous Multiparametric and Multidimensional Cardiovascular Magnetic Resonance Imaging

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    Novel 129Xe Magnetic Resonance Imaging and Spectroscopy Measurements of Pulmonary Gas-Exchange

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    Gas-exchange is the primary function of the lungs and involves removing carbon dioxide from the body and exchanging it within the alveoli for inhaled oxygen. Several different pulmonary, cardiac and cardiovascular abnormalities have negative effects on pulmonary gas-exchange. Unfortunately, clinical tests do not always pinpoint the problem; sensitive and specific measurements are needed to probe the individual components participating in gas-exchange for a better understanding of pathophysiology, disease progression and response to therapy. In vivo Xenon-129 gas-exchange magnetic resonance imaging (129Xe gas-exchange MRI) has the potential to overcome these challenges. When participants inhale hyperpolarized 129Xe gas, it has different MR spectral properties as a gas, as it diffuses through the alveolar membrane and as it binds to red-blood-cells. 129Xe MR spectroscopy and imaging provides a way to tease out the different anatomic components of gas-exchange simultaneously and provides spatial information about where abnormalities may occur. In this thesis, I developed and applied 129Xe MR spectroscopy and imaging to measure gas-exchange in the lungs alongside other clinical and imaging measurements. I measured 129Xe gas-exchange in asymptomatic congenital heart disease and in prospective, controlled studies of long-COVID. I also developed mathematical tools to model 129Xe MR signals during acquisition and reconstruction. The insights gained from my work underscore the potential for 129Xe gas-exchange MRI biomarkers towards a better understanding of cardiopulmonary disease. My work also provides a way to generate a deeper imaging and physiologic understanding of gas-exchange in vivo in healthy participants and patients with chronic lung and heart disease

    Ovarian hormones shape brain structure, function, and chemistry: A neuropsychiatric framework for female brain health

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    There are robust sex differences in brain anatomy, function, as well as neuropsychiatric and neurodegenerative disease risk (1-6), with women approximately twice as likely to suffer from a depressive illness as well as Alzheimer’s Disease. Disruptions in ovarian hormones likely play a role in such disproportionate disease prevalence, given that ovarian hormones serve as key regulators of brain functional and structural plasticity and undergo major fluctuations across the female lifespan (7-9). From a clinical perspective, there is a wellreported increase in depression susceptibility and initial evidence for cognitive impairment or decline during hormonal transition states, such as the postpartum period and perimenopause (9-14). What remains unknown, however, is the underlying mechanism of how fluctuations in ovarian hormones interact with other biological factors to influence brain structure, function, and chemistry. While this line of research has translational relevance for over half the population, neuroscience is notably guilty of female participant exclusion in research studies, with the male brain implicitly treated as the default model and only a minority of basic and clinical neuroscience studies including a female sample (15-18). Female underrepresentation in neuroscience directly limits opportunities for basic scientific discovery; and without basic knowledge of the biological underpinnings of sex differences, we cannot address critical sexdriven differences in pathology. Thus, my doctoral thesis aims to deliberately investigate the influence of sex and ovarian hormones on brain states in health as well as in vulnerability to depression and cognitive impairment:Table of Contents List of Abbreviations ..................................................................................................................... i List of Figures .............................................................................................................................. ii Acknowledgements .....................................................................................................................iii 1 INTRODUCTION .....................................................................................................................1 1.1 Lifespan approach: Sex, hormones, and metabolic risk factors for cognitive health .......3 1.2 Reproductive years: Healthy models of ovarian hormones, serotonin, and the brain ......4 1.2.1 Ovarian hormones and brain structure across the menstrual cycle ........................4 1.2.2 Serotonergic modulation and brain function in oral contraceptive users .................6 1.3 Neuropsychiatric risk models: Reproductive subtypes of depression ...............................8 1.3.1 Hormonal transition states and brain chemistry measured by PET imaging ...........8 1.3.2 Serotonin transporter binding across the menstrual cycle in PMDD patients .......10 2 PUBLICATIONS ....................................................................................................................12 2.1 Publication 1: Association of estradiol and visceral fat with structural brain networks and memory performance in adults .................................................................................13 2.2 Publication 2: Longitudinal 7T MRI reveals volumetric changes in subregions of human medial temporal lobe to sex hormone fluctuations ..............................................28 2.3 Publication 3: One-week escitalopram intake alters the excitation-inhibition balance in the healthy female brain ...............................................................................................51 2.4 Publication 4: Using positron emission tomography to investigate hormone-mediated neurochemical changes across the female lifespan: implications for depression ..........65 2.5 Publication 5: Increase in serotonin transporter binding across the menstrual cycle in patients with premenstrual dysphoric disorder: a case-control longitudinal neuro- receptor ligand PET imaging study ..................................................................................82 3 SUMMARY ...........................................................................................................................100 References ..............................................................................................................................107 Supplementary Publications ...................................................................................................114 Author Contributions to Publication 1 .....................................................................................184 Author Contributions to Publication 2 .....................................................................................186 Author Contributions to Publication 3 .....................................................................................188 Author Contributions to Publication 4 .....................................................................................190 Author Contributions to Publication 5 .....................................................................................191 Declaration of Authenticity ......................................................................................................193 Curriculum Vitae ......................................................................................................................194 List of Publications ................................................................................................................195 List of Talks and Posters ......................................................................................................19

    Hemodynamic Quantifications By Contrast-Enhanced Ultrasound:From In-Vitro Modelling To Clinical Validation

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    Development and Validation of Mechatronic Systems for Image-Guided Needle Interventions and Point-of-Care Breast Cancer Screening with Ultrasound (2D and 3D) and Positron Emission Mammography

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    The successful intervention of breast cancer relies on effective early detection and definitive diagnosis. While conventional screening mammography has substantially reduced breast cancer-related mortalities, substantial challenges persist in women with dense breasts. Additionally, complex interrelated risk factors and healthcare disparities contribute to breast cancer-related inequities, which restrict accessibility, impose cost constraints, and reduce inclusivity to high-quality healthcare. These limitations predominantly stem from the inadequate sensitivity and clinical utility of currently available approaches in increased-risk populations, including those with dense breasts, underserved and vulnerable populations. This PhD dissertation aims to describe the development and validation of alternative, cost-effective, robust, and high-resolution systems for point-of-care (POC) breast cancer screening and image-guided needle interventions. Specifically, 2D and 3D ultrasound (US) and positron emission mammography (PEM) were employed to improve detection, independent of breast density, in conjunction with mechatronic and automated approaches for accurate image acquisition and precise interventional workflow. First, a mechatronic guidance system for US-guided biopsy under high-resolution PEM localization was developed to improve spatial sampling of early-stage breast cancers. Validation and phantom studies showed accurate needle positioning and 3D spatial sampling under simulated PEM localization. Subsequently, a whole-breast spatially-tracked 3DUS system for point-of-care screening was developed, optimized, and validated within a clinically-relevant workspace and healthy volunteer studies. To improve robust image acquisition and adaptability to diverse patient populations, an alternative, cost-effective, portable, and patient-dedicated 3D automated breast (AB) US system for point-of-care screening was developed. Validation showed accurate geometric reconstruction, feasible clinical workflow, and proof-of-concept utility across healthy volunteers and acquisition conditions. Lastly, an orthogonal acquisition and 3D complementary breast (CB) US generation approach were described and experimentally validated to improve spatial resolution uniformity by recovering poor out-of-plane resolution. These systems developed and described throughout this dissertation show promise as alternative, cost-effective, robust, and high-resolution approaches for improving early detection and definitive diagnosis. Consequently, these contributions may advance breast cancer-related equities and improve outcomes in increased-risk populations and limited-resource settings

    Hemodynamic Quantifications By Contrast-Enhanced Ultrasound:From In-Vitro Modelling To Clinical Validation

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    Classification and Segmentation of Galactic Structuresin Large Multi-spectral Images

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    Extensive and exhaustive cataloguing of astronomical objects is imperative for studies seeking to understand mechanisms which drive the universe. Such cataloguing tasks can be tedious, time consuming and demand a high level of domain specific knowledge. Past astronomical imaging surveys have been catalogued through mostly manual effort. Immi-nent imaging surveys, however, will produce a magnitude of data that cannot be feasibly processed through manual cataloguing. Furthermore, these surveys will capture objects fainter than the night sky, termed low surface brightness objects, and at unprecedented spatial resolution owing to advancements in astronomical imaging. In this thesis, we in-vestigate the use of deep learning to automate cataloguing processes, such as detection, classification and segmentation of objects. A common theme throughout this work is the adaptation of machine learning methods to challenges specific to the domain of low surface brightness imaging.We begin with creating an annotated dataset of structures in low surface brightness images. To facilitate supervised learning in neural networks, a dataset comprised of input and corresponding ground truth target labels is required. An online tool is presented, allowing astronomers to classify and draw over objects in large multi-spectral images. A dataset produced using the tool is then detailed, containing 227 low surface brightness images from the MATLAS survey and labels made by four annotators. We then present a method for synthesising images of galactic cirrus which appear similar to MATLAS images, allowing pretraining of neural networks.A method for integrating sensitivity to orientation in convolutional neural networks is then presented. Objects in astronomical images can present in any given orientation, and thus the ability for neural networks to handle rotations is desirable. We modify con-volutional filters with sets of Gabor filters with different orientations. These orientations are learned alongside network parameters during backpropagation, allowing exact optimal orientations to be captured. The method is validated extensively on multiple datasets and use cases.We propose an attention based neural network architecture to process global contami-nants in large images. Performing analysis of low surface brightness images requires plenty of contextual information and local textual patterns. As a result, a network for processing low surface brightness images should ideally be able to accommodate large high resolu-tion images without compromising on either local or global features. We utilise attention to capture long range dependencies, and propose an efficient attention operator which significantly reduces computational cost, allowing the input of large images. We also use Gabor filters to build an attention mechanism to better capture long range orientational patterns. These techniques are validated on the task of cirrus segmentation in MAT-LAS images, and cloud segmentation on the SWIMSEG database, where state of the art performance is achieved.Following, cirrus segmentation in MATLAS images is further investigated, and a com-prehensive study is performed on the task. We discuss challenges associated with cirrus segmentation and low surface brightness images in general, and present several tech-niques to accommodate them. A novel loss function is proposed to facilitate training of the segmentation model on probabilistic targets. Results are presented on the annotated MATLAS images, with extensive ablation studies and a final benchmark to test the limits of the detailed segmentation pipeline.Finally, we develop a pipeline for multi-class segmentation of galactic structures and surrounding contaminants. Techniques of previous chapters are combined with a popu-lar instance segmentation architecture to create a neural network capable of segmenting localised objects and extended amorphous regions. The process of data preparation for training instance segmentation models is thoroughly detailed. The method is tested on segmentation of five object classes in MATLAS images. We find that unifying the tasks of galactic structure segmentation and contaminant segmentation improves model perfor-mance in comparison to isolating each task
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