Sampling conditions for gradient-magnitude sparsity based image reconstruction algorithms

Emil Y. Sidky, Jakob Heide Jørgensen, Xiaochuan Pan

    Research output: Contribution to journalConference articleResearchpeer-review


    We seek to characterize the sampling conditions for iterative image reconstruction exploiting gradient-magnitude sparsity. We seek the number of views necessary for accurate image reconstruction by constrained, total variation (TV) minimization, which is the optimization problem suggested in the compressive sensing (CS) community for this type of sparsity. The preliminary finding here, based on simulations using images of realistic sparsity levels, is that necessary sampling can go as low as N/4 views for an NxN pixel array. This work sets the stage for fixed-exposure studies where the number of projections is balanced against the X-ray intensity per projection.
    Original languageEnglish
    JournalProceedings of SPIE - The International Society for Optical Engineering
    Pages (from-to)8313-116
    Publication statusPublished - 2012
    EventSPIE Medical Imaging 2012: Ultrasonic Imaging, Tomography, and Therapy - San Diego, United States
    Duration: 5 Feb 20126 Feb 2012
    Conference number: 8320


    ConferenceSPIE Medical Imaging 2012
    Country/TerritoryUnited States
    CitySan Diego


    Dive into the research topics of 'Sampling conditions for gradient-magnitude sparsity based image reconstruction algorithms'. Together they form a unique fingerprint.

    Cite this