Purpose: The purpose of this dissertation is to evaluate the image quality of clinical Full Field Digital Mammography (FFDM) systems. This is done by evaluating image acquisition performance of clinical FFDM in a comprehensive way that accounts for scatter, focal spot un-sharpness, detector blur and anti-scatter grid performance using an anthropomorphic phantom. Additionally we intend to provide a limited evaluation of the effects that image processing in clinical FFDM has in signal detectability.

Methodology: We explored different strategies and a variety of mathematical model observers in order to evaluate the performance of clinical FFDM systems under different conditions. To evaluate image acquisition performance, we tested a system-model-based Hotelling observer (SMHO) model on a bench-top system using a uniform anthropomorphic phantom for an signal known exactly background known exactly (SKE/BKE) task. We then applied this concept on two clinical FFDM systems to compare their performance. In a limited study to evaluate the effects of image processing in the detectability of FFDM, we implemented the channelized Hotelling observer (CHO) model on clinically realistic images of an anatomical phantom for an SKE/BKE task.

Results: Even though the two systems use different detection technologies, there was no significant difference between their image acquisition performances quantified by the Contrast-Detail (CD) curves. We applied the CHO model to investigate the image processing algorithms used in GE Senographe DS FFDM system. For the particular SKE/BKE task with rotationally symmetric signals, the image processing tends to contribute to a non-significant reduction of system detectability.

Conclusion: We provided a complete description of FFDM system performance including the image acquisition chain and post-acquisition image processing. We demonstrated the simplicity and effectiveness of both the MFHO and CHO methods in a clinical setting.