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PURPOSE: To compare different optimization approaches for choosing the spin-lock times (TSLs), in spin-lattice relaxation time in the rotating frame (T(1ρ)) mapping. METHODS: Optimization criteria for TSLs based on Cramér-Rao lower bounds (CRLB) are compared with matched sampling-fitting (MSF) approaches for T(1ρ) mapping on synthetic data, model phantoms, and knee cartilage. The MSF approaches are optimized using robust methods for noisy cost functions. The MSF approaches assume that optimal TSLs depend on the chosen fitting method. An iterative non-linear least squares (NLS) and artificial neural networks (ANN) are tested as two possible T(1ρ) fitting methods for MSF approaches. RESULTS: All optimized criteria were better than non-optimized ones. However, we observe that a modified CRLB and an MSF based on the mean of the normalized absolute error (MNAE) were more robust optimization approaches, performing well in all tested cases. The optimized TSLs obtained the best performance with synthetic data (3.5–8.0% error), model phantoms (1.5–2.8% error), and healthy volunteers (7.7–21.1% error), showing stable and improved quality results, comparing to non-optimized approaches (4.2–13.3% error on synthetic data, 2.1–6.2% error on model phantoms, 9.8–27.8% error on healthy volunteers). CONCLUSION: A modified CRLB and the MSF based on MNAE are robust optimization approaches for choosing TSLs in T(1ρ) mapping. All optimized criteria allowed good results even using rapid scans with 2 TSLs when a complex-valued fitting is done with iterative NLS or ANN.

Purpose To implement a novel technique for simultaneous, quantitative multiparametric mapping of the knee articular cartilage. Methods A novel MR fingerprinting pulse sequence is proposed and implemented for simultaneous measurements of proton density, T1 , T2, and T1 ρ relaxation times at 3T. The repeatability and reproducibility of the proposed technique were assessed in model phantoms. Institutional review board-approved MR fingerprinting imaging sequence was performed on healthy volunteers and patients with mild knee osteoarthritis. The Wilcoxon test was used to compare healthy controls and patients. The intra- and intersubject repeatability were assessed with coefficient of variation and the RMS coefficient of variation, respectively RESULTS: The Bland-Altman plots demonstrated an average difference of 4.67 ms, -0.09 ms, and 0.05 ms between 2 scans in the same scanner; and 9.68 ms, 0.29 ms, and -0.72 ms between the scans acquired on 2 different scanners for T1 , T2 , and T1 ρ , respectively. The in vivo knee study showed excellent repeatability with RMS coefficient of variation less than 3%, 6%, and 5% for T1 , T2 , and T1 ρ , respectively. The Wilcoxon test showed a significant difference between control and mild osteoarthritis patients for T1 (P = .04), T2 (P = .01), and T1 ρ (P = .02) relaxation time in medial tibial cartilage, as well as for T2 relaxation time (P = .02) in medial femoral cartilage. Conclusion The proposed MRF sequence is fast and can simultaneously measure the T1 , T2 , T1 ρ , and B 1 + maps in a single scan. It is able to discriminate between mild osteoarthritis patients and healthy volunteers.

PURPOSE To develop a 3D sequence for T1ρ relaxation mapping using radial volumetric encoding (3D-T1ρ -RAVE) and to evaluate the multi relaxation components in the liver of healthy controls and chronic liver disease (CLD) patients. METHODS Fat saturation and T1ρ preparation modules were followed by a train of gradient-echo acquisitions and T1 restoration delay. The series of T1ρ -weighted images were fitted using mono-exponential, bi-exponential, and stretched-exponential models. The repeatability and reproducibility of the proposed technique were evaluated on National Institute of Standards and Technology phantom by calculating the coefficient of variation between test-retest scans on the same scanner and between two different 3T scanners, respectively. Mann-Whitney U-test was performed to assess differences in T1ρ components among patients (n = 3) and a control group (n = 10). RESULTS The phantom study showed an error of 8.9% and 11.5% in mono T2 relaxation time measurement relative to the reference on 2 different scanners. The coefficient of variation for test-retest scans performed on the same scanner was 5.7% and 2.4% for scans performed on 2 scanners. The comparison between healthy controls and CLD patients showed a significant difference (P < .05) in mono relaxation time (P = .002), stretched-exponential relaxation parameter (P = .04). The Akaike information criteria C criterion showed 2.53 ± 0.9% (2.3 ± 0.3% for CLD) of the voxels are bi-exponential while in 65.3 ± 5.8% (81.2 ± 0.06% for CLD) of the liver voxels, the stretched-exponential model was preferred. CONCLUSION The 3D-T1ρ -RAVE sequence allows volumetric, multicomponent T1ρ assessment of the liver during free breathing and can distinguish between healthy volunteers and CLD patients.

Purpose To use golden-angle radial sampling and compressed sensing (CS) for accelerating mono- and biexponential 3D spin-lattice relaxation time in the rotating frame (T1ρ ) mapping of knee cartilage. Methods Golden-angle radial stack-of-stars and Cartesian 3D-T1ρ -weighted knee cartilage datasets (n = 12) were retrospectively undersampled by acceleration factors (AFs) 2-10. CS-based reconstruction using 8 different sparsifying transforms were compared for mono- and biexponential T1ρ -mapping of knee cartilage, including spatio-temporal finite differences, wavelets, dictionary from principal component analysis, and exponential decay models, and also low rank and low rank plus sparse models (L+S). Complex-valued fitting was used and Marchenko-Pastur principal component analysis filtering also tested. Results Most CS methods performed well for an AF of 2, with relative median normalized absolute deviation below 10% for monoexponential and biexponential mapping. For monoexponential mapping, radial sampling obtained a median normalized absolute deviation below 10% up to AF of 10, while Cartesian obtained this level of error only up to AF of 4. Radial sampling was also better with biexponential T1ρ mapping, with median normalized absolute deviation below 10% up to AF of 6. Conclusion Golden-angle radial acquisitions combined with CS outperformed Cartesian acquisitions for 3D-T1ρ mapping of knee cartilage, being it is a good alternative to Cartesian sampling for reducing scan time and/or improving image and mapping quality. The methods exponential decay models, spatio-temporal finite differences, and low rank obtained the best results for radial sampling patterns.

Purpose Use compressed sensing (CS) for 3D biexponential spin-lattice relaxation time in the rotating frame (T1ρ ) mapping of knee cartilage, reducing the total scan time and maintaining the quality of estimated biexponential T1ρ parameters (short and long relaxation times and corresponding fractions) comparable to fully sampled scans. Methods Fully sampled 3D-T1ρ -weighted data sets were retrospectively undersampled by factors 2-10. CS reconstruction using 12 different sparsifying transforms were compared for biexponential T1ρ -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 T1ρ 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-T1ρ 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%.

Purpose 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. Methods 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. Results 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 smoothing the T1ρ maps. The STFD achieved 5.1% error at AF = 10 with spatial filtering prior to T1ρ fitting. Conclusion Accelerating 3D-T1ρ 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 T1ρ error of 6.5%.

PURPOSE: To implement a novel technique for simultaneous, quantitative multiparametric mapping of the knee articular cartilage. METHODS: A novel MR fingerprinting pulse sequence is proposed and implemented for simultaneous measurements of proton density, T(1), T(2,) and T (1) (ρ) relaxation times at 3T. The repeatability and reproducibility of the proposed technique were assessed in model phantoms. Institutional review board‐approved MR fingerprinting imaging sequence was performed on healthy volunteers and patients with mild knee osteoarthritis. The Wilcoxon test was used to compare healthy controls and patients. The intra‐ and intersubject repeatability were assessed with coefficient of variation and the RMS coefficient of variation, respectively RESULTS: The Bland‐Altman plots demonstrated an average difference of 4.67 ms, −0.09 ms, and 0.05 ms between 2 scans in the same scanner; and 9.68 ms, 0.29 ms, and −0.72 ms between the scans acquired on 2 different scanners for T(1), T(2), and T (1) (ρ), respectively. The in vivo knee study showed excellent repeatability with RMS coefficient of variation less than 3%, 6%, and 5% for T(1), T(2), and T (1) (ρ), respectively. The Wilcoxon test showed a significant difference between control and mild osteoarthritis patients for T(1) (P = .04), T(2) (P = .01), and T (1) (ρ) (P = .02) relaxation time in medial tibial cartilage, as well as for T(2) relaxation time (P = .02) in medial femoral cartilage. CONCLUSION: The proposed MRF sequence is fast and can simultaneously measure the T(1), T(2), T (1) (ρ), and [Formula: see text] maps in a single scan. It is able to discriminate between mild osteoarthritis patients and healthy volunteers.

PURPOSE: To develop a 3D sequence for T(1ρ) relaxation mapping using radial volumetric encoding (3D-T(1ρ)-RAVE) and to evaluate the multi relaxation components in the liver of healthy controls and chronic liver disease (CLD) patients. METHODS: Fat saturation and T(1ρ) preparation modules were followed by a train of gradient-echo acquisitions and T(1) restoration delay. The series of T(1ρ)-weighted images were fitted using mono-exponential (ME), bi-exponential (BE), and stretched-exponential (SE) models. The repeatability and reproducibility of the proposed technique were evaluated on NIST phantom by calculating the coefficient of variation (CV) between test-retest scans on the same scanner and between two different 3T scanners, respectively. Mann–Whitney-U test was performed to assess differences in T(1ρ) components amongst patients (n=3) and a control group (n=10). RESULTS: The phantom study showed an error of 8.9% and 11.5% in mono T(2) relaxation time measurement relative to the reference on two different scanners. The CV for test-retest scans performed on the same scanner was 5.7% and 2.4% for scans performed on two scanners. The comparison between healthy controls and CLD patients showed a significant difference (p

PURPOSE: Use Compressed Sensing (CS) for three dimensional (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 datasets were retrospectively undersampled by factors 2–10. CS reconstruction using twelve 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), and exponential decay models, and also low rank and low rank plus sparse models. Synthetic phantom (n=6) and in vivo human knee cartilage datasets (n=7) were included in the experiments. Spatial filtering prior to 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 pre-filtering. These three CS methods performed satisfactorily on synthetic phantom as well as in vivo knee cartilage for AFs up to 10, with median error below 15%.

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%.