Evaluation of SparseCT on patient data using realistic undersampling models

Baiyu Chen, Matthew Muckley, Aaron Sodickson, Thomas O'Donnell, Florian Knoll, Daniel Sodickson, Ricardo Otazo

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

    Abstract

    Compressed sensing (CS) requires undersampled projection data, but CT x-ray tubes cannot be pulsed quickly enough to achieve reduced-view undersampling. We propose an alternative within-view undersampling strategy, named SparseCT, as a practical CS technique to reduce CT radiation dose. SparseCT uses a multi-slit collimator (MSC) to interrupt the x-ray beam, thus acquiring undersampled projection data directly. This study evaluated the feasibility of SparseCT via simulations using a standardized patient dataset. Because the projection data in the dataset are fully sampled, we retrospectively undersample the projection data to simulate SparseCT acquisitions in three steps. First, two photon distributions were simulated, representing the cases with and without the MSC. Second, by comparing the two distributions, detector regions with more than 80% of x-ray blocked by the MSC were identified and the corresponding projection data were not used. Third, noise was inserted into the rest of the projection data to account for the increase in quantum noise due to reduced flux (partial MSC blockage). The undersampled projection data were then reconstructed iteratively using a penalized weighted least squares cost function with the conjugate gradient algorithm. The image reconstruction problem promotes sparsity in the solution and incorporates the undersampling model. Weighting factors were applied to the projection data during the reconstruction to account for the noise variation in the undersampled projection. Compared to images acquired with reduced tube current (provided in the standardized patient dataset), SparseCT undersampling presented less image noise while preserving pathologies and fine structures such as vessels in the reconstructed images.

    Original languageEnglish (US)
    Title of host publicationMedical Imaging 2018
    Subtitle of host publicationPhysics of Medical Imaging
    EditorsTaly Gilat Schmidt, Guang-Hong Chen, Joseph Y. Lo
    PublisherSPIE
    ISBN (Electronic)9781510616356
    DOIs
    StatePublished - Jan 1 2018
    EventMedical Imaging 2018: Physics of Medical Imaging - Houston, United States
    Duration: Feb 12 2018Feb 15 2018

    Publication series

    NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
    Volume10573
    ISSN (Print)1605-7422

    Conference

    ConferenceMedical Imaging 2018: Physics of Medical Imaging
    CountryUnited States
    CityHouston
    Period2/12/182/15/18

    Keywords

    • Computed tomography (CT)
    • compressed sensing (CS)
    • penumbra
    • sparseCT
    • undersampling

    ASJC Scopus subject areas

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

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  • Cite this

    Chen, B., Muckley, M., Sodickson, A., O'Donnell, T., Knoll, F., Sodickson, D., & Otazo, R. (2018). Evaluation of SparseCT on patient data using realistic undersampling models. In T. G. Schmidt, G-H. Chen, & J. Y. Lo (Eds.), Medical Imaging 2018: Physics of Medical Imaging [1057342] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10573). SPIE. https://doi.org/10.1117/12.2294243