Tests of projection and reconstruction domain equivalence for a feature-driven model observer

Howard C. Gifford; Zohreh Karbaschi

doi:10.1117/12.2512900

5 March 2019 Tests of projection and reconstruction domain equivalence for a feature-driven model observer

Howard C. Gifford, Zohreh Karbaschi

Proceedings Volume 10952, Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment; 109520B (2019) https://doi.org/10.1117/12.2512900
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Nonlinear visual-search (VS) observers have shown an ability to model humans for realistic detection, localization, and classification tasks with tomographic reconstructions. As reconstruction studies test the joint effects of data acquisition and postprocessing, applying observers for similar tasks in the projection domain is also of interest. To help investigate what information a non-ideal model can provide in this role, we have developed an analytical method for assessing acquisition quality in the reconstruction domain. This approach is most useful for assessing acquisition quality based on lesion-localization tasks. Observer studies with simulated Ga-67 SPECT data were conducted to test our method. The data were acquired for different numbers of projection angles. The results illustrate how image reconstruction processes can improve on acquisition quality as measured for an anthropomorphic model observer.

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Howard C. Gifford and Zohreh Karbaschi "Tests of projection and reconstruction domain equivalence for a feature-driven model observer", Proc. SPIE 10952, Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, 109520B (5 March 2019); https://doi.org/10.1117/12.2512900

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KEYWORDS

Expectation maximization algorithms

Reconstruction algorithms

Data acquisition

Feature extraction

Image processing

Image quality

Data modeling

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