Combination of compressed sensing and parallel imaging for highly accelerated first-pass cardiac perfusion MRI

Ricardo Otazo, Daniel Kim, Leon Axel, Daniel K. Sodickson

    Research output: Contribution to journalArticle

    Abstract

    First-pass cardiac perfusion MRI is a natural candidate for compressed sensing acceleration since its representation in the combined temporal Fourier and spatial domain is sparse and the required incoherence can be effectively accomplished by k-t random undersampling. However, the required number of samples in practice (three to five times the number of sparse coefficients) limits the acceleration for compressed sensing alone. Parallel imaging may also be used to accelerate cardiac perfusion MRI, with acceleration factors ultimately limited by noise amplification. In this work, compressed sensing and parallel imaging are combined by merging the k-t SPARSE technique with sensitivity encoding (SENSE) reconstruction to substantially increase the acceleration rate for perfusion imaging. We also present a new theoretical framework for understanding the combination of k-t SPARSE with SENSE based on distributed compressed sensing theory. This framework, which identifies parallel imaging as a distributed multisensor implementation of compressed sensing, enables an estimate of feasible acceleration for the combined approach. We demonstrate feasibility of 8-fold acceleration in vivo with whole-heart coverage and high spatial and temporal resolution using standard coil arrays. The method is relatively insensitive to respiratory motion artifacts and presents similar temporal fidelity and image quality when compared to Generalized autocalibrating partially parallel acquisitions (GRAPPA) with 2-fold acceleration.

    Original languageEnglish (US)
    Pages (from-to)767-776
    Number of pages10
    JournalMagnetic resonance in medicine
    Volume64
    Issue number3
    DOIs
    StatePublished - Sep 2010

    Keywords

    • Cardiac perfusion
    • Compressed sensing
    • Dynamic imaging
    • Joint sparsity
    • Parallel imaging

    ASJC Scopus subject areas

    • Radiology Nuclear Medicine and imaging

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