Approximate statistical properties of reconstructed images using model-based bootstrapping

Jun Ma*

*Corresponding author for this work

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

    Abstract

    In tomographic image reconstructions, it is often necessary to compute certain statistics of a reconstructed image. These quantities can be used, for example, to analyze the noise properties of a reconstruction. This paper introduces the model-based bootstrapping method to approximate mean and variance-covariance matrix of a reconstruction. This approximation is versatile and can be implemented in different tomographic reconstructions, such as emission and transmission tomographies. This paper also considers the possibility of computational load reduction by a simultaneous multiplicative iterative econstruction algorithm.

    Original languageEnglish
    Title of host publicationProceedings of the 6th International Symposium on Image and Signal Processing and Analysis, ISPA 2009
    EditorsPeter Zinterhof, Sven Loncaric, Andreas Uhl, Alberto Carini
    Place of PublicationPiscataway, NJ
    PublisherInstitute of Electrical and Electronics Engineers (IEEE)
    Pages145-149
    Number of pages5
    ISBN (Electronic)9789531841344
    ISBN (Print)9789531841351
    Publication statusPublished - 2009
    Event6th International Symposium on Image and Signal Processing and Analysis, ISPA 2009 - Salzburg, Austria
    Duration: 16 Sep 200918 Sep 2009

    Other

    Other6th International Symposium on Image and Signal Processing and Analysis, ISPA 2009
    CountryAustria
    CitySalzburg
    Period16/09/0918/09/09

    Keywords

    • Emission and transmission tomography
    • Generalized linear model
    • Model-based bootstrapping
    • Simultaneous reconstructions

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