Ensuring convergence in total-variation-based reconstruction for accurate microcalcification imaging in breast X-ray CT

Jakob Heide Jørgensen, Emil Y. Sidky, Xiaochuan Pan

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    Abstract

    Breast X-ray CT imaging is being considered in screening as an extension to mammography. As a large fraction of the population will be exposed to radiation, low·dose imaging is essential. Iterative image reconstruction based on solving an optimization problem, such as Total-Variation minimization, shows potential for reconstruction from sparse-view data. For iterative methods it is important to ensure convergence to an accurate solution, since important diagnostic image features, such as presence of microcalcifications indicating breast cancer, may not be visible in a non-converged reconstruction, and this can have clinical significance. To prevent excessively long computational times, which is a practical concern for the large image arrays in CT, it is desirable to keep the number of iterations low, while still ensuring a sufficiently accurate reconstruction for the specific imaging task. This motivates the study of accurate convergence criteria for iterative image reconstruction. In simulation studies with a realistic breast phantom with microcalcifications we investigate the issue of ensuring sufficiently converged solution for reliable reconstruction. Our results show that it can be challenging to ensure a sufficiently accurate microcalcification reconstruction, when using standard convergence criteria. In particular, the gray level of the small microcalcifications may not have converged long after the background tissue is reconstructed uniformly. We propose the use of the individual objective function gradient components to better monitor possible regions of nonconverged variables. For microcalcifications we find empirically a large correlation between nonzero gradient components and nonconverged variables, which occur precisely within the microcalcifications. This supports our claim that gradient components can be used to ensure convergence to a sufficiently accurate reconstruction.
    Original languageEnglish
    Title of host publication2011 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)
    PublisherIEEE
    Publication date2011
    Pages2640-2643
    ISBN (Print)978-1-4673-0118-3
    DOIs
    Publication statusPublished - 2011
    EventIEEE Nuclear Science Symposium and Medical Imaging Conference - Valencia, Spain
    Duration: 23 Oct 201129 Oct 2011

    Conference

    ConferenceIEEE Nuclear Science Symposium and Medical Imaging Conference
    Country/TerritorySpain
    CityValencia
    Period23/10/201129/10/2011

    Keywords

    • Total variation
    • Algorithm convergence
    • Breast CT
    • Compressed sensing
    • X-ray CT

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