Accelerating 3D-T

PURPOSE: To evaluate the feasibility of using Compressed Sensing (CS) to accelerate 3D-T(1ρ) mapping of cartilage and to reduce total scan times without degrading the estimation of T(1ρ) relaxation times. METHODS: Fully-sampled 3D-T(1ρ) datasets were retrospectively undersampled by factors 2–10. CS reconstruction using twelve 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 T(1ρ) parameter estimation was also tested. Synthetic phantom (n=6) and in vivo human knee cartilage datasets (n=7) were included. RESULTS: Most CS methods performed satisfactorily for an acceleration factor (AF) of 2, with relative T(1ρ) error lower than 4.5%. Some sparsifying transforms, such as spatio-temporal finite difference (STFD), exponential dictionaries (EXP) and low rank combined with spatial finite difference (L+S SFD) significantly improved this performance, reaching average relative T(1ρ) error below 6.5% on T(1ρ) relaxation times with AF up to 10, when spatial filtering prior to T(1ρ) fitting was utilized, at the expense of smoothing the T(1ρ) maps. The STFD achieved 5.1% error at AF=10 with spatial filtering prior to T(1ρ) fitting. CONCLUSION: Accelerating 3D-T(1ρ) mapping of cartilage with CS is feasible up to AF of 10 when using STFD, EXP or L+S SFD regularizers. These three best CS methods performed satisfactorily on synthetic phantom and in vivo knee cartilage for AFs up to 10, with T(1ρ) error of 6.5%.