The tumor microenvironment is a highly complex system, with variations through space and time that are determined by the interplay of normal and cancerous cells, physiological phenomena, and treatments that can dramatically change the structural or biological dynamics underlying the emergent behaviors. Quantitatively and reliably imaging the microenvironment represents an opportunity to develop diagnostic and prognostic assessment of cancer patients, enabling a fuller understanding of the tumor's evolution, and response to treatments. Multispectral optoacoustic tomography (MSOT), a novel imaging modality, has the potential to reveal the spatiotemporal dynamics of oxygenation at high resolution through the use of multiplexed laser light and has shown promise in advancing both clinical and pre-clinical research. Nevertheless, current methods of analysis often fail to yield sensible data, and are prone to artifacts and quantitative errors that preclude the effective use of this imaging method for diagnostic or prognostic imaging and that add difficulties in downstream analyses. In this work, I developed a battery of methods and tools that bridge the gap of MSOT's theoretical capabilities and the practical realities of its usage. These include a transparent and open-source toolbox for image reconstruction and analysis along with its deployment to a cloud-based workflow service managed by the University of Texas Southwestern Medical Center at Dallas' BioHPC; a simple and scalable method to address spectral aliasing and improve the time resolution and signal-to-noise ratio of dynamic MSOT data; a method to extract quantitative breathing parameters from tomographic imaging data; and a model scheme of the systemic physiology that determines the response to gas-breathing challenges. These developments have laid the groundwork for more rigorous investigations using MSOT for preclinical imaging research.