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Cited by 18Advanced MRI Techniques and ApplicationsSparse and Compressive Sensing TechniquesNMR spectroscopy and applications

Compressed sensing in quantitative determination of GAG concentration in cartilage by microscopic MRI

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Wang, Nian, Badar, Farid, Xia, Yang·2018·Magnetic Resonance in Medicine
DOI 10.1002/mrm.26973PMC PMC5843514
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Abstract

Purpose: To evaluate the potentials of compressed sensing (CS) in MRI quantification of glycosaminoglycan (GAG) concentration in articular cartilage at microscopic resolution. Methods: T 1‐weighted 2D experiments of cartilage were fully sampled in k‐space with five inversion times at 17.6 μm resolution. These fully sampled k‐space data were re‐processed, by undersampling at various 1D and 2D CS undersampling factors (UFs). The undersampled data were reconstructed individually into 2D images using nonlinear reconstruction, which were used to calculate 2D maps of T 1 and GAG concentration. The values of T 1 and GAG in cartilage were evaluated at different UFs (up to 16, which used 6.25% of the data). K‐space sampling pattern and zonal variations were also investigated. Results: Using 2D variable density sampling pattern, the T 1 images at UFs up to eight preserved major visual information and produced negligible artifacts. The GAG concentration remained accurate for different sub‐tissue zones at various UFs. The variation of the mean GAG concentration through the whole tissue depth was 1.20%, compared to the fully sampled results. The maximum variation was 2.24% in the deep zone of cartilage. Using 1D variable density sampling pattern, the quantitative T 1 mapping and GAG concentration at UFs up to 4 showed negligible variations. Conclusion: This study demonstrates that CS could be beneficial in microscopic MRI (µMRI) studies of cartilage by acquiring less data, without losing significant accuracy in the quantification of GAG concentration. Magn Reson Med 79:3163–3171, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

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  • Compressed sensing is an accelerated imaging method that enables reconstruction of undersampled data by exploiting image sparsity.

    “Compressed sensing (CS) is an accelerated imaging method, which enables reconstruction of under sampled data by exploiting image sparsity (Wang et al. 2018b; Lustig et al. 2007).”

    From Neurite orientation dispersion and density imaging of mouse brain microstructure (2019) · cites this paper as Wang et al. 2018b

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References 51 · Cited by 18

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Authors
Wang, N., Badar, F., & Xia, Y.
Affiliations
  • Duke University (US)
  • Oakland University (US)
Corresponding author
Xia, Yang
ORCID
Venue
Magnetic Resonance in Medicine
Publisher
Wiley
Published
2017-10-30
Volume
79
Issue
6
Pages
3163-3171
ISSN
  • 0740-3194 (ISSN-L)
  • 1522-2594
DOI
10.1002/mrm.26973
PMC
PMC5843514
OpenAlex
W2765289989
Cited by
18
References
51
Citations by year
2024: 3 · 2022: 2 · 2021: 3 · 2020: 3 · 2019: 3 · 2018: 4
FWCI
2.10
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • Magnetic Resonance in Medicine (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
NIH ((PI: Xia) R01 AR052353, R01 AR069047)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
  • Physical Sciences › Engineering › Computational Mechanics › Sparse and Compressive Sensing Techniques
  • Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › NMR spectroscopy and applications
Keywords
compressed sensing, articular cartilage, T1 mapping, k-space undersampling, variable density sampling, nonlinear image reconstruction, cartilage depth
MeSH terms
Animals, Cartilage, Articular, Dogs, Glycosaminoglycans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Microscopy, Anisotropy

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