Advanced Imaging Analysis for Predicting Tumor Response and Improving Contour Delineation Uncertainty

ADVANCED IMAGING ANALYSIS FOR PREDICTING TUMOR RESPONSE AND IMPROVING CONTOUR DELINEATION UNCERTAINTY By Rebecca Nichole Mahon, MS A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University. Virginia Commonwealth University, 2018 Major Director: Dr. Elisabeth Weiss, Professor, Department of Radiation Oncology Radiomics, an advanced form of imaging analysis, is a growing field of interest in medicine. Radiomics seeks to extract quantitative information from images through use of computer vision techniques to assist in improving treatment. Early prediction of treatment response is one way of improving overall patient care. This work seeks to explore the feasibility of building predictive models from radiomic texture features extracted from magnetic resonance (MR) and computed tomography (CT) images of lung cancer patients. First, repeatable primary tumor texture features from each imaging modality were identified to ensure a sufficient number of repeatable features existed for model development. Then a workflow was developed to build models to predict overall survival and local control using single modality and multi-modality radiomics features. The workflow was also applied to normal tissue contours as a control study. Multiple significant models were identified for the single modality MR- and CT-based models, while the multi-modality models were promising indicating exploration with a larger cohort is warranted. Another way advances in imaging analysis can be leveraged is in improving accuracy of contours. Unfortunately, the tumor can be close in appearance to normal tissue on medical images creating high uncertainty in the tumor boundary. As the entire defined target is treated, providing physicians with additional information when delineating the target volume can improve the accuracy of the contour and potentially reduce the amount of normal tissue incorporated into the contour. Convolution neural networks were developed and trained to identify the tumor interface with normal tissue and for one network to identify the tumor location. A mock tool was presented using the output of the network to provide the physician with the uncertainty in prediction of the interface type and the probability of the contour delineation uncertainty exceeding 5mm for the top three predictions

Integrated Structural And Functional Biomarkers For Neurodegeneration

Alzheimer\u27s Disease consists of a complex cascade of pathological processes, leading to the death of cortical neurons and development of dementia. Because it is impossible to regenerate neurons that have already died, a thorough understanding of the earlier stages of the disease, before significant neuronal death has occurred, is critical for developing disease-modifying therapies. The various components of Alzheimer\u27s Disease pathophysiology necessitate a variety of measurement techniques. Image-based measurements known as biomarkers can be used to assess cortical thinning and cerebral blood flow, but non-imaging characteristics such as performance on cognitive tests and age are also important determinants of risk of Alzheimer\u27s Disease. Incorporating the various imaging and non-imaging sources of information into a scientifically interpretable and statistically sound model is challenging. In this thesis, I present a method to include imaging data in standard regression analyses in a data-driven and anatomically interpretable manner. I also introduce a technique for disentangling the effect of cortical structure from blood flow, enabling a clearer picture of the signal carried by cerebral blood flow beyond the confounding effects of anatomical structure. In addition to these technical developments in multi-modal image analysis, I show the results of two clinically-oriented studies focusing on the relative importance of various biomarkers for predicting presence of Alzheimer\u27s Disease pathology in the earliest stages of disease. In the first, I present evidence that white matter hyperintensities, a marker of small vessel disease, are more highly associated with Alzheimer\u27s Disease pathology than current mainstream imaging biomarkers in elderly control patients. In the second, I show that once Alzheimer\u27s Disease has progressed to the point of noticeable cognitive decline, cognitive tests are as predictive of presence of Alzheimer\u27s pathology as standard imaging biomarkers. Taken together, these studies demonstrate that the relative importance of biomarkers and imaging modalities changes over the course of disease progression, and sophisticated data-driven methods for combining a variety of modalities is likely to lead to greater biological insight into the disease process than a single modality

IRM fonctionnelle chez le rat (défis méthodologiques)

L'imagerie par résonance magnétique fonctionnelle (IRMf ) permet de détecter sur le cerveau entier des activations neuronales en réponse à un stimulus, par le biais de l'observation des modifications hémodynamiques occasionnées. En particulier, l'IRMf est un outil de choix pour l'étude des mécanismes de la stimulation cérébrale profonde et de la stimulation du nerf vague qui sont encore mal connus. Cependant, cette technique n'est pas facilement utilisable chez l'homme en raison des problèmes de sécurité vis-à-vis de l'action des champs magnétiques intenses utilisés en IRM au niveau des électrodes implantées. Les développements méthodologiques chez l'animal sont donc nécessaires. L'objectif principal de cette thèse est l'étude des mécanismes à distance de la stimulation du système nerveux central et périphérique par IRMf chez le rat. Nous présentons dans un premier temps les séquences IRM rapides utilisées en IRMf, comme l'Echo-Planar Imaging multishot, permettant d'imager le cerveau entier en 1 à 2 secondes seulement, ainsi que les différents problèmes posés par l'utilisation de ces séquences, comme les artefacts de susceptibilité magnétique. Le couplage des séquences développées durant cette thèse avec des mesures électrophysiologiques a notamment permis l'imagerie des réseaux épileptiques chez le rat. Dans un second temps, nous développons les problèmes posés par la préparation animale, particulière en IRMf de par le fait que le couplage neurovasculaire doit être préservé sous anesthésie afin de préserver les activations neuronales. Après comparaison avec les anesthésies à l'isoflurane et la kétamine, nous avons déduit que la médétomidine constituait un anesthésique de choix pour l'IRMf du rongeur, et précisons le protocole de préparation animale utilisé pour l'imagerie. De plus, les électrodes utilisées en stimulation intracérébrale induisent des artefacts importants en imagerie, et des électrodes constituées de matériaux amagnétiques sont nécessaires. Nous expliquons pourquoi nous avons choisi des électrodes en carbone, et présentons le protocole de fabrication utilisé. Nous avons ensuite validé ces développements méthodologiques par des expériences d'IRMf de challenges hypercapniques et de stimulation de la patte chez le rat. Puis nous avons conduit une étude IRMf approfondie des mécanismes d'action de la stimulation du nerf vague, en s'intéressant à la distinction entre activations neuronales et effets cardiovasculaires confondants par modélisation causale dynamique. Nous présentons aussi des résultats en IRMf de la stimulation électrique intracérébrale chez le rat. Plusieurs cibles ont été stimulées (noyau géniculé dorso-latéral, gyrus dentelé, striatum et thalamus), et des activations ont été obtenues à distance de l'électrode, conformément aux connaissances actuelles sur les connexions neuroanatomiques de ces noyaux. Ainsi, nous avons mis au point et validé l'IRMf du rat et son application à la stimulation électrique du système nerveux périphérique et central.Functional magnetic resonance imaging (fMRI) can detect neuronal activations in the entire brain, in response to a stimulus, through the observation of subsequent hemodynamic changes. In particular, fMRI is a good tool for studying the mechanisms of deep brain stimulation and vagus nerve stimulation, which are still poorly understood. However, this technique is not readily usable in humans because of safety concerns towards the action of the strong magnetic fields used in MRI on implanted electrodes. Indeed, methodological developments in animals are needed. The main goal of this thesis is to study the mechanisms of central and peripheral nervous system stimulation in rats by fMRI. First, fast MRI sequences used in fMRI are exposed, such as multishot Echo-Planar Imaging, allowing to image the entire brain in a couple of seconds. Various imaging problems posed by these sequences, such as magnetic susceptibility artifacts, are also presented. These sequences, developed during this thesis, associated with electrophysiological measurements, allowed imaging of epileptic networks in the rat. Secondly, animal preparation is developped, as it is peculiar in fMRI : neuronal activations, as well as neurovascular coupling, must be preserved under anesthesia. Compared to anesthesia by isoflurane and ketamine, it was concluded that medetomidine was an anesthetic of choice for fMRI of the rodent, and the protocol used for animal preparation for imaging is specified. Furthermore, the electrodes used in deep brain stimulation induce significant artifacts in MRI images, and electrodes made of amagnetic materials are needed. Our choice of carbon electrodes is explained, and the manufacturing protocol used is exposed. These methodological developments were then validated in fMRI experiments of hypercapnic challenges and forepaw stimulation. Finally, an fMRI experiment studying mechanisms of action of vagus nerve stimulation was conducted, focusing on the distinction between neuronal activations and confounding cardiovascular effects by dynamic causal modeling. Also, results on fMRI of deep brain stimulation in rats are presented. Several targets were stimulated (dorsolateral geniculate nucleus, dentate gyrus, striatum and thalamus), and activations were obtained at a distance from the electrode. Results were in accordance with current knowledge on neuroanatomical connections of these nuclei. Thus, we developed and validated fMRI of the rat and its application to electrical stimulation of peripheral and central nervous system.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods