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

Published

Journal Article

PURPOSE: To evaluate the potentials of compressed sensing (CS) in MRI quantification of glycosaminoglycan (GAG) concentration in articular cartilage at microscopic resolution. METHODS: T1 -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 T1 and GAG concentration. The values of T1 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 T1 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 T1 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.

Full Text

Duke Authors

Cited Authors

  • Wang, N; Badar, F; Xia, Y

Published Date

  • June 2018

Published In

Volume / Issue

  • 79 / 6

Start / End Page

  • 3163 - 3171

PubMed ID

  • 29083096

Pubmed Central ID

  • 29083096

Electronic International Standard Serial Number (EISSN)

  • 1522-2594

Digital Object Identifier (DOI)

  • 10.1002/mrm.26973

Language

  • eng

Conference Location

  • United States