Compressed sensing for body MRI

Li Feng, Thomas Benkert, Kai Tobias Block, Daniel K. Sodickson, Ricardo Otazo, Hersh Chandarana

    Research output: Contribution to journalReview article

    Abstract

    The introduction of compressed sensing for increasing imaging speed in magnetic resonance imaging (MRI) has raised significant interest among researchers and clinicians, and has initiated a large body of research across multiple clinical applications over the last decade. Compressed sensing aims to reconstruct unaliased images from fewer measurements than are traditionally required in MRI by exploiting image compressibility or sparsity. Moreover, appropriate combinations of compressed sensing with previously introduced fast imaging approaches, such as parallel imaging, have demonstrated further improved performance. The advent of compressed sensing marks the prelude to a new era of rapid MRI, where the focus of data acquisition has changed from sampling based on the nominal number of voxels and/or frames to sampling based on the desired information content. This article presents a brief overview of the application of compressed sensing techniques in body MRI, where imaging speed is crucial due to the presence of respiratory motion along with stringent constraints on spatial and temporal resolution. The first section provides an overview of the basic compressed sensing methodology, including the notion of sparsity, incoherence, and nonlinear reconstruction. The second section reviews state-of-the-art compressed sensing techniques that have been demonstrated for various clinical body MRI applications. In the final section, the article discusses current challenges and future opportunities. Level of Evidence: 5. J. Magn. Reson. Imaging 2017;45:966–987.

    Original languageEnglish (US)
    Pages (from-to)966-987
    Number of pages22
    JournalJournal of Magnetic Resonance Imaging
    Volume45
    Issue number4
    DOIs
    StatePublished - Apr 1 2017

    Keywords

    • MRI
    • body imaging
    • compressed sensing
    • rapid imaging
    • sparsity

    ASJC Scopus subject areas

    • Radiology Nuclear Medicine and imaging

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