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Rapid compositional mapping of knee cartilage with compressed sensing MRI
Author(s) -
Zibetti Marcelo V. W.,
Baboli Rahman,
Chang Gregory,
Otazo Ricardo,
Regatte Ravinder R.
Publication year - 2018
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.26274
Subject(s) - computer science , knee cartilage , compressed sensing , cartilage , high resolution , data acquisition , image resolution , artificial intelligence , computer vision , articular cartilage , biomedical engineering , osteoarthritis , medicine , geology , remote sensing , pathology , anatomy , alternative medicine , operating system
More than a decade after the introduction of compressed sensing (CS) in MRI, researchers are still working on ways to translate it into different research and clinical applications. The greatest advantage of CS in MRI is the reduced amount of k ‐space data needed to reconstruct images, which can be exploited to reduce scan time or to improve spatial resolution and volumetric coverage. Efficient data acquisition using CS is extremely important for compositional mapping of the musculoskeletal system in general and knee cartilage mapping techniques in particular. High‐resolution quantitative information about tissue biochemical composition could be obtained in just a few minutes using CS MRI. However, in order to make this goal a reality, some issues still need to be addressed. In this article we review the current state of the art of CS methods for rapid compositional mapping of knee cartilage. Specifically, data acquisition strategies, image reconstruction algorithms, and data fitting models are discussed. Different CS studies for T 2 and T 1ρ mapping of knee cartilage are reviewed, with illustrative results. Future directions, opportunities, and challenges of rapid compositional mapping techniques are also discussed. Level of Evidence: 4 Technical Efficacy: Stage 6 J. Magn. Reson. Imaging 2018;47:1185–1198.