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

Liying Chen; Harrison H. Barrett

doi:10.1117/12.480331

22 May 2003 Optimizing lens-coupled digital radiographic imaging systems based on model observers' performance

Liying Chen, Harrison H. Barrett

Proceedings Volume 5034, Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment; (2003) https://doi.org/10.1117/12.480331
Event: Medical Imaging 2003, 2003, San Diego, California, United States

Abstract

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 chose 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 blue 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 between coupling efficiency and blur in a task-based manner. In this way he channelized Hotelling SNR is used as a merit function for lens design.

Citation Download Citation

Liying Chen and Harrison H. Barrett "Optimizing lens-coupled digital radiographic imaging systems based on model observers' performance", Proc. SPIE 5034, Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment, (22 May 2003); https://doi.org/10.1117/12.480331

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