1,490 research outputs found
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
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The role of HG in the analysis of temporal iteration and interaural correlation
Improving the clinico-radiological association in neurological diseases
Despite the key role of magnetic resonance imaging (MRI) in the diagnosis and monitoring of multiple sclerosis (MS) and cerebral small vessel disease (SVD), the association between clinical and radiological disease manifestations is often only moderate, limiting the use of MRI-derived markers in the clinical routine or as endpoints in clinical trials. In the projects conducted as part of this thesis, we addressed this clinico-radiological gap using two different approaches. Lesion-symptom association: In two voxel-based lesion-symptom mapping studies, we aimed at strengthening lesion-symptom associations by identifying strategic lesion locations. Lesion mapping was performed in two large cohorts: a dataset of 2348 relapsing-remitting MS patients, and a population-based cohort of 1017 elderly subjects. T2-weighted lesion masks were anatomically aligned and a voxel-based statistical approach to relate lesion location to different clinical rating scales was implemented. In the MS lesion mapping, significant associations between white matter (WM) lesion location and several clinical scores were found in periventricular areas. Such lesion clusters appear to be associated with impairment of different physical and cognitive abilities, probably because they affect commissural and long projection fibers. In the SVD lesion mapping, the same WM fibers and the caudate nucleus were identified to significantly relate to the subjectsâ cerebrovascular risk profiles, while no other locations were found to be associated with cognitive impairment. Atrophy-symptom association: With the construction of an anatomical physical phantom, we aimed at addressing reliability and robustness of atrophy-symptom associations through the provision of a âground truthâ for atrophy quantification. The built phantom prototype is composed of agar gels doped with MRI and computed tomography (CT) contrast agents, which realistically mimic T1 relaxation times of WM and grey matter (GM) and showing distinguishable attenuation coefficients using CT. Moreover, due to the design of anatomically simulated molds, both WM and GM are characterized by shapes comparable to the human counterpart. In a proof-of-principle study, the designed phantom was used to validate automatic brain tissue quantification by two popular software tools, where âground truthâ volumes were derived from high-resolution CT scans. In general, results from the same software yielded reliable and robust results across scans, while results across software were highly variable reaching volume differences of up to 8%
Imaging Pain And Brain Plasticity: A Longitudinal Structural Imaging Study
Chronic musculoskeletal pain is a leading cause of disability worldwide yet the mechanisms of chronification and neural responses to effective treatment remain elusive. Non-invasive imaging techniques are useful for investigating brain alterations associated with health and disease. Thus the overall goal of this dissertation was to investigate the white (WM) and grey matter (GM) structural differences in patients with musculoskeletal pain before and after psychotherapeutic intervention: cognitive behavioral therapy (CBT). To aid in the interpretation of clinical findings, we used a novel porcine model of low back pain-like pathophysiology and developed a post-mortem, in situ, neuroimaging approach to facilitate translational investigation.
The first objective of this dissertation (Chapter 2) was to identify structural brain alterations in chronic pain patients compared to healthy controls. To achieve this, we examined GM volume and diffusivity as well as WM metrics of complexity, density, and connectivity. Consistent with the literature, we observed robust differences in GM volume across a number of brain regions in chronic pain patients, however, findings of increased GM volume in several regions are in contrast to previous reports. We also identified WM changes, with pain patients exhibiting reduced WM density in tracts that project to descending pain modulatory regions as well as increased connectivity to default mode network structures, and bidirectional alterations in complexity. These findings may reflect network level dysfunction in patients with chronic pain.
The second aim (Chapter 3) was to investigate reversibility or neuroplasticity of structural alterations in the chronic pain brain following CBT compared to an active control group. Longitudinal evaluation was carried out at baseline, following 11-week intervention, and a four-month follow-up. Similarly, we conducted structural brain assessments including GM morphometry and WM complexity and connectivity. We did not observe GM volumetric or WM connectivity changes, but we did discover differences in WM complexity after therapy and at follow-up visits.
To facilitate mechanistic investigation of pain related brain changes, we used a novel porcine model of low back pain-like pathophysiology (Chapter 6). This model replicates hallmarks of chronic pain, such as soft tissue injury and movement alteration. We also developed a novel protocol to perform translational post-mortem, in situ, neuroimaging in our porcine model to reproduce WM and GM findings observed in humans, followed by a unique perfusion and immersion fixation protocol to enable histological assessment (Chapter 4).
In conclusion, our clinical data suggest robust structural brain alterations in patients with chronic pain as compared to healthy individuals and in response to therapeutic intervention. However, the mechanism of these brain changes remains unknown. Therefore, we propose to use a porcine model of musculoskeletal pain with a novel neuroimaging protocol to promote mechanistic investigation and expand our interpretation of clinical findings
Ensemble tractography
Fiber tractography uses diffusion MRI to estimate the trajectory and cortical projection zones of white matter fascicles in the living human brain. There are many different tractography algorithms and each requires the user to set several parameters, such as curvature threshold. Choosing a single algorithm with a specific parameters sets poses two challenges. First, different algorithms and parameter values produce different results. Second, the optimal choice of algorithm and parameter value may differ between different white matter regions or different fascicles, subjects, and acquisition parameters. We propose using ensemble methods to reduce algorithm and parameter dependencies. To do so we separate the processes of fascicle generation and evaluation. Specifically, we analyze the value of creating optimized connectomes by systematically combining candidate fascicles from an ensemble of algorithms (deterministic and probabilistic) and sweeping through key parameters (curvature and stopping criterion). The ensemble approach leads to optimized connectomes that provide better cross-validatedprediction error of the diffusion MRI data than optimized connectomes generated using the singlealgorithms or parameter set. Furthermore, the ensemble approach produces connectomes that contain both short- and long-range fascicles, whereas single-parameter connectomes are biased towards one or the other. In summary, a systematic ensemble tractography approach can produce connectomes that are superior to standard single parameter estimates both for predicting the diffusion measurements and estimating white matter fascicles.Fil: Takemura, Hiromasa. University of Stanford; Estados Unidos. Osaka University; JapĂłnFil: Caiafa, CĂ©sar Federico. Provincia de Buenos Aires. GobernaciĂłn. ComisiĂłn de Investigaciones CientĂficas. Instituto Argentino de RadioastronomĂa. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - La Plata. Instituto Argentino de RadioastronomĂa; ArgentinaFil: Wandell, Brian A.. University of Stanford; Estados UnidosFil: Pestilli, Franco. Indiana University; Estados Unido
Building connectomes using diffusion MRI: why, how and but
Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically-relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments
Development of a simulation platform for the evaluation of PET neuroimaging protocols in epilepsy
Monte Carlo simulation of PET studies is a reference tool for the evaluation
and standardization of PET protocols. However, current Monte Carlo software codes require a high degree of
knowledge in physics, mathematics and programming languages, in addition to a high cost of time and
computational resources. These drawbacks make their use difficult for a large part of the scientific community. In
order to overcome these limitations, a free and an efficient web-based platform was designed, implemented and
validated for the simulation of realistic brain PET studies, and specifically employed for the generation of a wellvalidated
large database of brain FDG-PET studies of patients with refractory epilepsy
Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods
Radiomics converts medical images into mineable data via a high-throughput extraction of quantitative features used for clinical decision support. However, these radiomic features are susceptible to variation across scanners, acquisition protocols, and reconstruction settings. Various investigations have assessed the reproducibility and validation of radiomic features across these discrepancies. In this narrative review, we combine systematic keyword searches with prior domain knowledge to discuss various harmonization solutions to make the radiomic features more reproducible across various scanners and protocol settings. Different harmonization solutions are discussed and divided into two main categories: image domain and feature domain. The image domain category comprises methods such as the standardization of image acquisition, post-processing of raw sensor-level image data, data augmentation techniques, and style transfer. The feature domain category consists of methods such as the identification of reproducible features and normalization techniques such as statistical normalization, intensity harmonization, ComBat and its derivatives, and normalization using deep learning. We also reflect upon the importance of deep learning solutions for addressing variability across multi-centric radiomic studies especially using generative adversarial networks (GANs), neural style transfer (NST) techniques, or a combination of both. We cover a broader range of methods especially GANs and NST methods in more detail than previous reviews
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