Task-Based Assessment and Optimization of Digital Breast Tomosynthesis

Digital breast tomosynthesis (DBT) is a new technology for breast cancer screening that promises to complement mammography or supersede it to become the standard for breast imaging. DBT involves taking multiple images in order to synthesize a new image that represents a slice through the breast volume -- hence the term tomosynthesis. The primary advantage of this paradigm is that it can reduce the amount of overlapping anatomy in the data, leading to improved visualization of potentially-cancerous findings. The difficulty in DBT is quantifying the advantages of the technology and determining the optimal conditions for its clinical use. This dissertation describes a virtual trial framework for assessing and optimizing DBT technology for the specific task of detecting small, low-contrast masses in the breast. It addresses each component of the imaging chain to some degree, from the patients/phantoms to the imaging hardware to the model observers used to measure signal detectability. The main focus, however, is on quantifying tradeoffs between three key parameters that affect image quality: (1) scan angle, (2) number of projections, and (3) exposure. We show that in low-density breast phantoms, detectability generally increases with both scan angle and number of projections in the anatomical-variability-limited (high-exposure) regime. We also investigate how breast density affects the optimal DBT scan parameters. We show task-specific results that support using an adaptive paradigm in DBT, where the imaging system reconfigures itself in response to information about the patient's breast density. The virtual framework described in this dissertation provides a platform for further investigations of image quality in 3D breast imaging