Variational Deep Learning for Low-Dose Computed Tomography

Erich Kobler, Matthew Muckley, Baiyu Chen, Florian Knoll, Kerstin Hammernik, Thomas Pock, Daniel Sodickson, Ricardo Otazo

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

    Abstract

    In this work, we propose a learning-based variational network (VN) approach for reconstruction of low-dose 3D computed tomography data. We focus on two methods to decrease the radiation dose: (1) x-ray tube current reduction, which reduces the signal-to-noise ratio, and (2) x-ray beam interruption, which undersamples data and results in images with aliasing artifacts. While the learned VN denoises the current-reduced images in the first case, it reconstructs the undersampled data in the second case. Different VNs for denoising and reconstruction are trained on a single clinical 3D abdominal data set. The VNs are compared against state-of-the-art model-based denoising and sparse reconstruction techniques on a different clinical abdominal 3D data set with 4-fold dose reduction. Our results suggest that the proposed VNs enable higher radiation dose reductions and/or increase the image quality for a given dose.

    Original languageEnglish (US)
    Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages6687-6691
    Number of pages5
    ISBN (Print)9781538646588
    DOIs
    StatePublished - Sep 10 2018
    Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
    Duration: Apr 15 2018Apr 20 2018

    Publication series

    NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    Volume2018-April
    ISSN (Print)1520-6149

    Other

    Other2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
    CountryCanada
    CityCalgary
    Period4/15/184/20/18

    Keywords

    • Compressed sensing
    • Computed tomography
    • Machine learning
    • Medical imaging
    • Variational networks

    ASJC Scopus subject areas

    • Software
    • Signal Processing
    • Electrical and Electronic Engineering

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  • Cite this

    Kobler, E., Muckley, M., Chen, B., Knoll, F., Hammernik, K., Pock, T., Sodickson, D., & Otazo, R. (2018). Variational Deep Learning for Low-Dose Computed Tomography. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (pp. 6687-6691). [8462312] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2018-April). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2018.8462312