Accelerating 3D-T1ρ mapping of cartilage using compressed sensing with different sparse and low rank models

Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine
Marcelo V W ZibettiRavinder R Regatte

Abstract

To evaluate the feasibility of using compressed sensing (CS) to accelerate 3D-T1ρ mapping of cartilage and to reduce total scan times without degrading the estimation of T1ρ relaxation times. Fully sampled 3D-T1ρ datasets were retrospectively undersampled by factors 2-10. CS reconstruction using 12 different sparsifying transforms were compared, including finite differences, temporal and spatial wavelets, learned transforms using principal component analysis (PCA) and K-means singular value decomposition (K-SVD), explicit exponential models, low rank and low rank plus sparse models. Spatial filtering prior to T1ρ parameter estimation was also tested. Synthetic phantom (n = 6) and in vivo human knee cartilage datasets (n = 7) were included. Most CS methods performed satisfactorily for an acceleration factor (AF) of 2, with relative T1ρ error lower than 4.5%. Some sparsifying transforms, such as spatiotemporal finite difference (STFD), exponential dictionaries (EXP) and low rank combined with spatial finite difference (L+S SFD) significantly improved this performance, reaching average relative T1ρ error below 6.5% on T1ρ relaxation times with AF up to 10, when spatial filtering was used before T1ρ fitting, at the expense of smoot...Continue Reading

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Citations

Aug 18, 2018·Physics in Medicine and Biology·Yanjie ZhuDong Liang
Sep 20, 2018·Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine·Marceo V W ZibettiRavinder R Regatte
Oct 9, 2018·Journal of Magnetic Resonance Imaging : JMRI·Marcelo V W ZibettiRavinder R Regatte
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Oct 1, 2021·Scientific Reports·Marcelo V W ZibettiRavinder R Regatte

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