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

Magnetic resonance in medicine, 2010-09, Vol.64 (3), p.767-776 [Peer Reviewed Journal]

Copyright © 2010 Wiley‐Liss, Inc. ;2010 Wiley-Liss, Inc. ;ISSN: 0740-3194 ;ISSN: 1522-2594 ;EISSN: 1522-2594 ;DOI: 10.1002/mrm.22463 ;PMID: 20535813

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  • Title:
    Combination of compressed sensing and parallel imaging for highly accelerated first-pass cardiac perfusion MRI
  • Author: Otazo, Ricardo ; Kim, Daniel ; Axel, Leon ; Sodickson, Daniel K.
  • Subjects: Adult ; Algorithms ; Cardiac Imaging Techniques - methods ; cardiac perfusion ; compressed sensing ; Data Compression - methods ; dynamic imaging ; Humans ; Image Enhancement - methods ; Image Interpretation, Computer-Assisted - methods ; Myocardial Perfusion Imaging - methods ; parallel imaging: joint sparsity ; Pattern Recognition, Automated - methods ; Reproducibility of Results ; Sensitivity and Specificity
  • Is Part Of: Magnetic resonance in medicine, 2010-09, Vol.64 (3), p.767-776
  • Description: 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. Magn Reson Med, 2010. © 2010 Wiley‐Liss, Inc.
  • Publisher: Hoboken: Wiley Subscription Services, Inc., A Wiley Company
  • Language: English
  • Identifier: ISSN: 0740-3194
    ISSN: 1522-2594
    EISSN: 1522-2594
    DOI: 10.1002/mrm.22463
    PMID: 20535813
  • Source: MEDLINE
    Alma/SFX Local Collection
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