Sparsity-driven ideal observer for computed medical imaging systems

Kun Wang, Yang Lou, Matthew A Kupinski, Mark A. Anastasio

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

The Bayesian ideal observer (IO) has been widely advocated to guide hardware optimization. However, except for special cases, computation of the IO test statistic is computationally burdensome and requires an appropriate stochastic object model that may be difficult to determine in practice. Modern reconstruction methods, referred to as sparse reconstruction methods, exploit the fact that objects of interest typically possess sparse representations and have proven to be highly effective at reconstructing images from under-sampled measurement data. Moreover, in computed imaging approaches that employ compressive sensing concepts, imaging hardware and image reconstruction are innately coupled technologies. In this work, we propose a sparsity-driven IO (SD-IO) to guide the optimization of data acquisition parameters for modern computed imaging systems. The SD-IO employs a variational Bayesian inference method to estimate the posterior distribution and calculates an approximate likelihood ratio analytically as its test statistic. Since it assumes knowledge of low-level statistical properties of the object that are related to sparsity, the SD-IO exploits the same statistical information regarding the object that is utilized by highly effective sparse image reconstruction methods. Preliminary simulation results are presented to demonstrate the feasibility of the SD-IO calculation.

Original languageEnglish (US)
Title of host publicationProgress in Biomedical Optics and Imaging - Proceedings of SPIE
PublisherSPIE
Volume9416
ISBN (Print)9781628415063
DOIs
Publication statusPublished - 2015
EventMedical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment - Orlando, United States
Duration: Feb 25 2015Feb 26 2015

Other

OtherMedical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment
CountryUnited States
CityOrlando
Period2/25/152/26/15

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ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

Wang, K., Lou, Y., Kupinski, M. A., & Anastasio, M. A. (2015). Sparsity-driven ideal observer for computed medical imaging systems. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE (Vol. 9416). [94161C] SPIE. https://doi.org/10.1117/12.2082316