Abstract
<jats:p>Four-dimensional dynamic contrast-enhanced breast computed tomography (4D DCE-bCT) is an emerging imaging modality for breast cancer assessment with the potential to support personalized treatment strategies. Current imaging methods remain limited in accurately evaluating tumor response, highlighting the need for techniques that combine high spatial and temporal resolution with reliable quantitative information. This thesis presents the technical development and experimental validation of 4D DCE-bCT to address these challenges. The work introduces a deep learning-based scatter correction method, compares first- and second-generation breast CT systems demonstrating improvements in resolution and dose efficiency, and develops optimized acquisition and reconstruction strategies to enhance iodine contrast visibility while enabling low-dose dynamic imaging. Experimental validation using a dynamic perfusion phantom confirms the accuracy and temporal consistency of iodine concentration measurements. By addressing key technical barriers in acquisition, reconstruction, and quantitative validation, this thesis advances 4D DCE-bCT toward clinical application, supporting improved functional imaging and more precise monitoring of treatment response in breast cancer care.</jats:p>