Optimizing lens-coupled digital radiographic imaging systems based on model observers' performance

Recent advances in model observers that predict human perceptual performance now make it possible to optimize medical imaging systems for human task performance. We illustrate the procedure by considering the design of a lens for use in an optically coupled digital mammography system. The channelized Hotelling observer is used to model human performance, and the channels chosen are differences of Gaussians (DOGs). The task is detection of a lesion at a random but known location in a clustered lumpy background mimicking breast tissue. The entire system is simulated with a Monte Carlo application according to the physics principles, but the main system component under study is the lens that couples a fluorescent screen to a CCD detector. The bigger the aperture is, the larger the portion of light is coupled to the CDD, but the more severe the aberrations are, so the worse the image blur is. So when changing the stop size, the signal (lesion) detectability of human observers associated with this task also changes. The SNR of the channelized Hotelling observer is used to quantify this detectability. In this paper, plots of channelized Hotelling SNR vs. signal location for various lens apertures and working distances are presented. These plots thus illustrate the tradeoff between coupling efficiency and blur in a task-based manner. In this way, the channelized Hotelling SNR is used as a merit function for lens design.