The political history of U.S. commercial remote sensing began in 1984 when the U.S. government first attempted to commercialize its civil earth observation satellite system " Landsat. Since then, the high technology of earth imaging satellite systems has generated intense debates and policy conflicts, primarily centered on U.S. government concerns over the national security and foreign policy implications of high-resolution commercial satellite systems. Conversely, proponents of commercial observation satellites have urged U.S. policymakers to recognize the scientific and socio-economic utility of commercial remote sensing and thus craft and implement regulatory regimes that allow for a greater degree of information openness and transparency in using earth observation satellite imagery. This dissertation traces and analyzes that tumultuous political history and examines the policy issues and social construction of commercial remote sensing to determine the role of knowledge in the effective crafting and execution of commercial remote sensing laws and policies. Although individual and organizational perspectives, interests, missions, and cultures play a significant role in the social construction of commercial observation satellite systems and programs, the problem of insufficient knowledge of the myriad dimensions and complex nature of commercial remote sensing is a little studied but important component of this social construction process. Knowledge gaps concerning commercial remote sensing extend to various dimensions of the subject matter, such as the global, economic, technical, and legal/policy aspects. Numerous examples of knowledge voids are examined to suggest a connection between deficient knowledge and divergent policy perceptions as they relate to commercial remote sensing. Relevant knowledge voids are then structurally categorized to demonstrate the vastness and complexity of commercial remote sensing policy issues and to offer recommendations on how to fill such knowledge gaps to effect increased collaboration between the US government and the U.S. commercial remote sensing industry. Finally, the dissertation offers suggestions for future STS studies on policy issues, particularly those that focus on the global dimensions of commercial remote sensing or on applying the knowledge gap concept advanced by this dissertation to other areas of science and technology policymaking. / Ph. D.

Cette thèse est dédiée à améliorer la compréhension neuro-scientifique à l'aide d'imagerie par résonance magnétique de diffusion (IRMd). Nous nous concentrons sur la modélisation du signal de diffusion et l'estimation par IRMd des biomarqueurs liés à la microstructure, appelé «Microstructure Imaging». Cette thèse est organisée en trois parties. Dans partie I nous commençons par la base de l'IRMd et un aperçu de l'anisotropie en diffusion. Puis nous examinons la plupart des modèles de microstructure utilisant PGSE, en mettant l'accent sur leurs hypothèses et limites, suivi par une validation par l'histologie de la moelle épinière de leur estimation. La partie II présente nos contributions à l'imagerie en 3D et à l’estimation de microstructure. Nous proposons une régularisation laplacienne de la base fonctionnelle MAP, ce qui nous permet d'estimer de façon robuste les indices d'espace q liés au tissu. Nous appliquons cette approche aux données du Human Connectome Project, où nous l'utilisons comme prétraitement pour d'autres modèles de microstructure. Enfin, nous comparons les biomarqueurs dans une étude ex-vivo de rats Alzheimer à différents âges. La partie III présente nos contributions au représentation de l’espace qt - variant sur l'espace q 3D et le temps de diffusion. Nous présentons une approche initiale qui se concentre sur l'estimation du diamètre de l'axone depuis l'espace qt. Nous terminons avec notre approche finale, où nous proposons une nouvelle base fonctionnelle régularisée pour représenter de façon robuste le signal qt, appelé qt-IRMd. Ce qui permet l'estimation des indices d’espace q dépendants du temps, quantifiant la dépendance temporelle du signal IRMd. / This thesis is dedicated to furthering neuroscientific understanding of the human brain using diffusion-sensitized Magnetic Resonance Imaging (dMRI). Within dMRI, we focus on the estimation and interpretation of microstructure-related markers, often referred to as ``Microstructure Imaging''. This thesis is organized in three parts. Part I focuses on understanding the state-of-the-art in Microstructure Imaging. We start with the basic of diffusion MRI and a brief overview of diffusion anisotropy. We then review and compare most state-of-the-art microstructure models in PGSE-based Microstructure Imaging, emphasizing model assumptions and limitations, as well as validating them using spinal cord data with registered ground truth histology. In Part II we present our contributions to 3D q-space imaging and microstructure recovery. We propose closed-form Laplacian regularization for the recent MAP functional basis, allowing robust estimation of tissue-related q-space indices. We also apply this approach to Human Connectome Project data, where we use it as a preprocessing for other microstructure models. Finally, we compare tissue biomarkers in a ex-vivo study of Alzheimer rats at different ages. In Part III, we present our contributions to representing the qt-space - varying over 3D q-space and diffusion time. We present an initial approach that focuses on 3D axon diameter estimation from the qt-space. We end with our final approach, where we propose a novel, regularized functional basis to represent the qt-signal, which we call qt-dMRI. Our approach allows for the estimation of time-dependent q-space indices, which quantify the time-dependence of the diffusion signal.