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    Super-Resolution and Sparse View CT Reconstruction

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    Type
    Conference Paper
    Authors
    Zang, Guangming cc
    Aly, Mohamed
    Idoughi, Ramzi
    Wonka, Peter cc
    Heidrich, Wolfgang cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2018-10-06
    Online Publication Date
    2018-10-06
    Print Publication Date
    2018
    Permanent link to this record
    http://hdl.handle.net/10754/628903
    
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    Abstract
    We present a flexible framework for robust computed tomography (CT) reconstruction with a specific emphasis on recovering thin 1D and 2D manifolds embedded in 3D volumes. To reconstruct such structures at resolutions below the Nyquist limit of the CT image sensor, we devise a new 3D structure tensor prior, which can be incorporated as a regularizer into more traditional proximal optimization methods for CT reconstruction. As a second, smaller contribution, we also show that when using such a proximal reconstruction framework, it is beneficial to employ the Simultaneous Algebraic Reconstruction Technique (SART) instead of the commonly used Conjugate Gradient (CG) method in the solution of the data term proximal operator. We show empirically that CG often does not converge to the global optimum for tomography problem even though the underlying problem is convex. We demonstrate that using SART provides better reconstruction results in sparse-view settings using fewer projection images. We provide extensive experimental results for both contributions on both simulated and real data. Moreover, our code will also be made publicly available.
    Citation
    Zang, G., Aly, M., Idoughi, R., Wonka, P., & Heidrich, W. (2018). Super-Resolution and Sparse View CT Reconstruction. Lecture Notes in Computer Science, 145–161. doi:10.1007/978-3-030-01270-0_9
    Sponsors
    This work was supported by KAUST as part of VCC Center Competitive Funding
    Conference/Event name
    European Conference on Computer Vision (ECCV)
    DOI
    10.1007/978-3-030-01270-0_9
    Additional Links
    https://vccimaging.org/Publications/Zang2018SuperResolutionCT/
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-030-01270-0_9
    Scopus Count
    Collections
    Conference Papers; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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