Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 27 for “"Brain MRI"”.
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Multispectral segmentation of whole-brain MRI
Magnetic Resonance Imaging (MRI) is a widely used medical technology for diagnosis and detection of various tissue abnormalities, tumor detection, and in evaluation of either residual or recurrent tumors. This thesis work exploits MRI information acquired on brain tumor structure and physiological …
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Learning Deformable Templates for Brain MRI
… maps. We demonstrate our method on a large 3D brain MRI dataset. This is particularly relevant in medical image analysis where templates are difficult to build. We show that this framework can learn sharp templates representative of the population. These templates are representative of the …
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Deep Learning Domain Adaptation in Brain MRI: Investigating Motion Mitigation in Adult and Neonatal Scans
Magnetic Resonance Imaging (MRI) is a vital non-invasive tool for high-resolution brain imaging, yet its long acquisition time frequently results in patient movement leading to motion artifacts, particularly among non-compliant patients like newborns. While Deep Learning (DL) models have emerged as …
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Examining the Association between Brain MRI Measures at 7 Tesla and cognition following COVID-19 Infection
… resonance imaging measures within subcortical brain structures of the limbic system were related to neurological, respiratory, psychiatric, and gastric symptoms experienced during the acute phase of illness. Cognitive and neuropsychological evaluations were performed in 45 participants who …
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3D spherical harmonic invariant features for sensitive and robust quantitative shape and function analysis in brain MRI
… and function in magnetic resonance imaging (MRI) of the brain is proposed. First, an efficient method to compute invariant spherical harmonics (SPHARM) based feature representation for real valued 3D functions was developed. This method addressed previous limitations of obtaining unique …
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Machine Learning Methods for Personalized Treatment Response Characterization Using Clinical Care Brain MRI and Non-Imaging Data in Multiple Sclerosis
… new DMT using baseline clinical data, especially brain magnetic resonance imaging (MRI). Two RRMS cohorts (2/3 women) at 169 and 75 participants were examined, used for training and semi-external testing, respectively. Six key clinical variables and common brain MRI sequences: T1-weighted, …
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Spatiotemporal Encoding Methods for Brain Magnetic Resonance Imaging
Magnetic resonance imaging (MRI) is a widely used non-invasive imaging technology for both clinical diagnosis and neuroscientific research. However, the imaging sensitivity and specificity of brain MRI are limited by the well-known technical challenge of MRI acquisition—low image encoding …
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Exploring the relationship between multimodal magnetic resonance neuroimaging and cognitive outcomes in children: applying machine learning algorithms to brain MRI features to predict cognitive scores and performance categories of children living with and without HIV
… Magnetic resonance (MR) imaging (MRI) is a versatile tool that can be used to measure a broad range of brain tissue properties giving rise to cognitive functions. For example, structural MRI (sMRI) can quantify brain volumes and other morphometrics, diffusion tensor imaging (DTI) …
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The Effect of Selective Hypothermia on Stroke Volume
… reperfusion, the animals were sacrificed. Brain MRI and histology were evaluated blinded to the intervention. In a series of animals, the mean temperature achieved was 26.5C. Mean time from start of perfusion to moderate hypothermia (< 30 C) was 25.4 minutes. Mean stroke volumes were …
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Online, low-latency decision making for Fetal Magnetic Resonance Imaging with machine learning
Fetal Magnetic Resonance Imaging (MRI) with T2-weighted Half-Fourier-Acquisition Single-Shot Turbo-Spin-Echo (HASTE) sequence plays an important role in diagnosing brain abnormality. However, the quality of HASTE images routinely suffer from fetal motion which leads to image artifacts, incomplete …
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Saliency Mapping in Convolutional Neural Networks to Determine Brain Age Trajectories
Brain Age (BA) estimation via Deep Learning has become a strong and reliable bio-marker for brain health, but the black-box nature of Neural Networks does not easily allow insight into the causal features of brain ageing. In this work, a ResNet model was trained as a BA regressor on T1 structural …
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Bayesian level sets and texture models for image segmentation and classification with application to non-invasive stem cell monitoring
… also validated on other applications, including brain MRI, retinal lesions, and wildlife images.
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Quantitative Analysis of Normal-Appearing White Matter In Pediatric Phenylketonuria
… varying degrees of severity. The analysis of brain structure in children with PKU using magnetic resonance imaging (MRI) has been limited by the reliance on qualitative data to characterize white matter abnormalities. In addition, the few brain MRI studies that have used a quantitative …
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Normalization of Cerebral Blood Flow, Neurochemicals, and White Matter Integrity After Kidney Transplantation
… kidney transplantation (KT), we examined these brain abnormalities pre-to post-KT to identify potential reversibility in ESKD-associated brain abnormalities.Methods: We measured the effects of KT on CBF assessed by arterial spin labeling, cerebral neurochemical concentrations (N-acetylaspartate, …
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First-In-DOg HISTotripsy for Intracranial Tumors Trial: The FIDOHIST Study
Objective: Brain tumors represent some of the most treatment refractory cancers, and there is a clinical need for additional treatments for these tumors. Domesticated dogs are the only other mammalian species which commonly develop spontaneous brain tumors, making them an ideal model for …
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Intercomparison of medical image segmentation algorithms
Magnetic Resonance Imaging (MRI) is one of the most widely-used high quality imaging techniques, especially for brain imaging, compared to other techniques such as computed tomography and x-rays, mainly because it possesses better soft tissue contrast resolution. There are several stages involved …
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Studio dei ritmi circadiani in pazienti in stato vegetativo
… for 4 hours from 11.30 p.m. to 3.30 a.m.). Brain MRI, Level of Cognitive Functioning Scale (LCF) and Disability Rating Scale (DRS) were assessed just before polysomnography. Results: In all patients LCF and DRS confirmed vegetative state. All patients showed a sleep-wake cycle. All patients …
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Deep Learning for Semi-Automated Brain Claustrum Segmentation on Magnetic Resonance (MR) Images
… task, especially for Magnetic Resonance Images (MRI). In reality, it is a time- consuming procedure that requires trained biomedical experts to manually segment or annotate such MRI datasets. The need for automated segmentation or annotation is what motivates our work. In this thesis, we propose …
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Multiphoton Parallel Transmit MRI for Flip Angle Mitigation Without SAR Concerns
High-field magnetic resonance imaging (MRI) excitation performed using a standard birdcage volume coil suffers from a flip angle inhomogeneity problem. For example, in 7 T brain MRI, such an excitation has a flip angle as much as three-fold higher in the center of the head than near the periphery. …
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