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    Task-based data-acquisition optimization for sparse image reconstruction systems

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    Author
    Chen, Yujia
    Lou, Yang
    Kupinski, Matthew A.
    Anastasio, Mark A.
    Affiliation
    Univ Arizona, Ctr Opt Sci
    Issue Date
    2017-03-10
    Keywords
    Numerial observers
    imaging system optimization
    
    Metadata
    Show full item record
    Publisher
    SPIE-INT SOC OPTICAL ENGINEERING
    Citation
    Yujia Chen ; Yang Lou ; Matthew A. Kupinski and Mark A. Anastasio " Task-based data-acquisition optimization for sparse image reconstruction systems ", Proc. SPIE 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment, 101360Z (March 10, 2017); doi:10.1117/12.2255536; http://dx.doi.org/10.1117/12.2255536
    Journal
    MEDICAL IMAGING 2017: IMAGE PERCEPTION, OBSERVER PERFORMANCE, AND TECHNOLOGY ASSESSMENT
    Rights
    © 2017 SPIE.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Conventional wisdom dictates that imaging hardware should be optimized by use of an ideal observer (TO) that exploits full statistical knowledge of the class of objects to be imaged, without consideration of the reconstruction method to be employed. However, accurate and tractable models of the complete object statistics are often difficult to determine in practice. Moreover, in imaging systems that employ compressive sensing concepts, imaging hardware and (sparse) image reconstruction are innately coupled technologies. We have previously proposed a sparsity-driven ideal observer (SDIO) that can be employed to optimize hardware by use of a stochastic object model that describes object sparsity. The SDIO and sparse reconstruction method can therefore be "matched" in the sense that they both utilize the same statistical information regarding the class of objects to be imaged. To efficiently compute SDIO performance, the posterior distribution is estimated by use of computational tools developed recently for variational Bayesian inference. Subsequently, the SDIO test statistic can be computed semi-analytically. The advantages of employing the SDIO instead of a Hotelling observer are systematically demonstrated in case studies in which magnetic resonance imaging (MRI) data acquisition schemes are optimized for signal detection tasks.
    ISSN
    0277-786X
    DOI
    10.1117/12.2255536
    Version
    Final published version
    Sponsors
    NIH [EB02016802, EB02060401]
    Additional Links
    http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2255536
    ae974a485f413a2113503eed53cd6c53
    10.1117/12.2255536
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