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Compressed sensing acceleration of biexponential 3D‐T 1ρ relaxation mapping of knee cartilage
Author(s) -
Zibetti Marcelo V. W.,
Sharafi Azadeh,
Otazo Ricardo,
Regatte Ravinder R
Publication year - 2019
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.27416
Subject(s) - imaging phantom , singular value decomposition , cartilage , exponential function , knee cartilage , knee joint , relaxation (psychology) , principal component analysis , nuclear magnetic resonance , mathematics , osteoarthritis , biomedical engineering , algorithm , physics , mathematical analysis , articular cartilage , statistics , optics , anatomy , medicine , psychology , social psychology , alternative medicine , surgery , pathology
Purpose Use compressed sensing (CS) for 3D biexponential spin‐lattice relaxation time in the rotating frame (T 1ρ ) mapping of knee cartilage, reducing the total scan time and maintaining the quality of estimated biexponential T 1ρ parameters (short and long relaxation times and corresponding fractions) comparable to fully sampled scans. Methods Fully sampled 3D‐T 1ρ ‐weighted data sets were retrospectively undersampled by factors 2–10. CS reconstruction using 12 different sparsifying transforms were compared for biexponential T 1ρ ‐mapping of knee cartilage, including temporal and spatial wavelets and finite differences, dictionary from principal component analysis (PCA), k‐means singular value decomposition (K‐SVD), exponential decay models, and also low rank and low rank plus sparse models. Synthetic phantom ( N = 6) and in vivo human knee cartilage data sets ( N = 7) were included in the experiments. Spatial filtering before biexponential T 1ρ parameter estimation was also tested. Results Most CS methods performed satisfactorily for an acceleration factor (AF) of 2, with relative median normalized absolute deviation (MNAD) around 10%. Some sparsifying transforms, such as low rank with spatial finite difference (L + S SFD), spatiotemporal finite difference (STFD), and exponential dictionaries (EXP) significantly improved this performance, reaching MNAD below 15% with AF up to 10, when spatial filtering was used. Conclusion Accelerating biexponential 3D‐T 1ρ mapping of knee cartilage with CS is feasible. The best results were obtained by STFD, EXP, and L + S SFD regularizers combined with spatial prefiltering. These 3 CS methods performed satisfactorily on synthetic phantom as well as in vivo knee cartilage for AFs up to 10, with median error below 15%.