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.

In X-Ray CT, there is always a desire to maintain the image quality while reducing the radiation dose. Recently several dose reduction approaches in both software and hardware have been developed to achieve the goal of making radiation as low as possible. Thus, the assessment of image quality becomes an important factor for routine quality control of medical X-Ray devices. In this work, task-based image quality measurements using model observers were used to evaluate the performance of X-Ray CT systems. To evaluate the dose reduction ability, detection tasks as well as combined detection and estimation tasks were considered. In detection tasks and combined detection and estimation tasks, the channelized Hotelling observer (CHO) and channelized scanning linear observer (CSLO) (with Dense Difference of Gauss channels) were employed respectively. They were used to evaluate the dose reduction capability of the iterative reconstruction algorithm developed by GE compared to the traditional reconstruction algorithm, filtered backprojection (FBP). Additionally, CHO and CSLO were also used for optimization of CT protocols. Our methods were also applied to Cardiac CT systems for temporal resolution evaluations. Two reconstruction algorithms, FBP and the motion correction algorithm, Snapshot Freeze (SSF), operated at two heart-beating rates with two reconstruction windows were quantitatively evaluated using task-based measurements. Finally, due to the huge demand of data acquisitions in the conventional channelized model observers, a proposed High-Dose-Signal-LOOL CHO/CSLO (HL-CHO/CSLO) that could efficiently reduce the data requirement has also been investigated in the pure detection, and combined detection and estimation task. In all studies, the practicality and the use of real data is emphasized. The results of all these studies demonstrate the usefulness of the task-based measurements of image quality in X-Ray CT imaging.