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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.

Quantitative MRI can detect early biochemical changes in cartilage; however, the conventional techniques only measure one parameter (e.g., Tsub1/sub, Tsub2/sub, and Tsub1ρ/sub) at a time while also being comparatively slow. We implemented a 3D magnetic resonance fingerprinting (3D-MRF) technique for simultaneous, volumetric mapping of Tsub1/sub, Tsub2/sub, and Tsub1ρ/subin knee articular cartilage in under 9 min. It is evaluated on 11 healthy volunteers (mean age: 53 ± 9 years), five mild knee osteoarthritis (OA) patients (Kellgren-Lawrence (KL) score: 2, mean age: 60 ± 4 years), and the National Institute of Standards and Technology (NIST)/International Society for Magnetic Resonance in Medicine (ISMRM) system phantom. Proton density image, and Tsub1/sub, Tsub2,/subTsub1ρ/subrelaxation times, and Bsub1/subsup+/supwere estimated in the NIST/ISMRM system phantom as well as in the human knee medial and lateral femur, medial and lateral tibia, and patellar cartilage. The repeatability and reproducibility of the proposed technique were assessed in the phantom using analysis of the Bland-Altman plots. The intrasubject repeatability was assessed with the coefficient of variation (CV) and root mean square CV (rmsCV). The Mann-Whitney U test was used to assess the difference between healthy subjects and mild knee OA patients. The Bland-Altman plots in the NIST/ISMRM phantom demonstrated an average difference of 0.001% ± 015%, 1.2% ± 7.1%, and 0.47% ± 3% between two scans from the same 3-T scanner (repeatability), and 0.002% ± 015%, 0.62% ± 10.5%, and 0.97% ± 14% between the scans acquired on two different 3-T scanners (reproducibility) for Tsub1/sub, Tsub2/sub, and Tsub1ρ/sub, respectively. The in vivo knee study showed excellent repeatability with rmsCV less than 1%, 2%, and 1% for Tsub1/sub, Tsub2/sub, and Tsub1ρ/sub, respectively. Tsub1ρ/subrelaxation time in the mild knee OA patients was significantly higher (p lt; 0.05) than in healthy subjects. The proposed 3D-MRF sequence is fast, reproducible, robust to Bsub1/subsup+/supinhomogeneity, and can simultaneously measure the Tsub1/sub, Tsub2/sub, Tsub1ρ/sub, and Bsub1/subsup+/supvolumetric maps of the knee joint in a single scan within a clinically feasible scan time.

Quantitative MRI can detect early biochemical changes in cartilage, but its bilateral use in clinical routines is challenging. The aim of this prospective study was to demonstrate the feasibility of magnetic resonance fingerprinting for bilateral simultaneous T(1), T(2), and T(1ρ) mapping of the hip joint. The study population consisted of six healthy volunteers with no known trauma or pain in the hip. Monoexponential T(1), T(2), and T(1ρ) relaxation components were assessed in femoral lateral, superolateral, and superomedial, and inferior, as well as acetabular, superolateral, and superomedial subregions in left and right hip cartilage. Aligned ranked nonparametric factorial analysis was used to assess the side’s impact on the subregions. Kruskal–Wallis and Wilcoxon tests were used to compare subregions, and coefficient of variation to assess repeatability. Global averages of T(1) (676.0 ± 45.4 and 687.6 ± 44.5 ms), T(2) (22.5 ± 2.6 and 22.1 ± 2.5 ms), and T(1ρ) (38.2 ± 5.5 and 38.2 ± 5.5 ms) were measured in the left and right hip, and articular cartilage, respectively. The Kruskal–Wallis test showed a significant difference between different subregions’ relaxation times regardless of the hip side (p < 0.001 for T(1), p = 0.012 for T(2), and p < 0.001 for T(lρ)). The Wilcoxon test showed that T(1) of femoral layers was significantly (p < 0.003) higher than that for acetabular cartilage. The experiments showed excellent repeatability with CV(rms) of 1%, 2%, and 4% for T(1), T(2), and T(1ρ,) respectively. It was concluded that bilateral T(1), T(2), and T(1ρ) relaxation times, as well as B(1)(+) maps, can be acquired simultaneously from hip joints using the proposed MRF sequence.

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: To develop a novel MR-fingerprinting pulse sequence that is insensitive to B(1)(+) and B(0) imperfections for simultaneous T(1), T(2,) and T(1ρ) relaxation mapping. METHODS: We implemented a totally-balanced-spin-lock (TB-SL) module to encode T(1ρ) relaxation into an existing MRF framework that encoded T(1) and T(2). The spin-lock module utilized two 180° pulses with compensatory phases to reduce T(1ρ) sensitivity to B(1) and B(0) inhomogeneities. We compared T(1ρ) measured using TB-SL MRF in Bloch simulations, model agar phantoms, and in-vivo experiments to those with a self-compensated spin-lock preparation module (SC-SL). TB-SL MRF repeatability was evaluated in maps acquired in the lower leg skeletal muscle of 12 diabetic peripheral neuropathy patients scanned two times each during visits separated by ~30 days. RESULTS: The phantom relaxation times measured with TB-SL and SC-SL MRF were in good agreement with reference values in regions with low B(1) inhomogeneities. Compared to SC-SL, TB-SL MRF showed in experiments greater robustness against severe B(1) inhomogeneities and in Bloch simulations greater robustness against B(1) and B(0). We measured with TB-SL MRF average of T(1)=950.1±28.7ms, T(2)=26.0±1.2ms, and T(1ρ)=31.7±3.2ms in skeletal muscle across patients. Bland-Altman analysis demonstrated low bias between TB-SL and SC-SL MRF and between TB-SL MRF maps acquired in two visits. The coefficient of variation was less than 3% for all measurements. CONCLUSION: The proposed TB-SL MRF sequence is fast and insensitive to B(1)(+) and B(0) imperfections. It can simultaneously map T(1), T(2), T(1ρ), and B(1)(+) in a single scan and could potentially be used to study muscle composition.

In this study we use undersampled MRI acquisition methods to obtain accelerated 3D mono and biexponential spin–lattice relaxation time in the rotating frame (T1ρ) mapping of knee cartilage, reducing the usual long scan time. We compare the accelerated T1ρ maps obtained by deep learning-based variational network (VN) and compressed sensing (CS). Both methods were compared with spatial (S) and spatio-temporal (ST) filters. Complex-valued fitting was used for T1ρ parameters estimation. We tested with seven in vivo and six synthetic datasets, with acceleration factors (AF) from 2 to 10. Median normalized absolute deviation (MNAD), analysis of variance (ANOVA), and coefficient of variation (CV) were used for analysis. The methods CS-ST, VN-S, and VN-ST performed well for accelerating monoexponential T1ρ mapping, with MNAD around 5% for AF = 2, which increases almost linearly with the AF to an MNAD of 13% for AF = 8, with all methods. For biexponential mapping, the VN-ST was the best method starting with MNAD of 7.4% for AF = 2 and reaching MNAD of 13.1% for AF = 8. The VN was able to produce 3D-T1ρ mapping of knee cartilage with lower error than CS. The best results were obtained by VN-ST, improving CS-ST method by nearly 7.5%.

IntroductionThe purpose of this study was to characterize using MRI the effects of a 10-week supervised exercise program on lower extremity skeletal muscle composition, nerve microarchitecture, and metabolic function in individuals with diabetic peripheral neuropathy (DPN).Research design and methodsTwenty participants with DPN completed a longitudinal trial consisting of a 30-day control period, during which subjects made no change to their lifestyle, followed by a 10-week intervention program that included three supervised aerobic and resistance exercise sessions per week targeting the upper and lower extremities. The participants’ midcalves were scanned with multinuclear MRI two times prior to intervention (baseline1 and baseline2) and once following intervention to measure relaxation times (T1, T1ρ, and T2), phosphocreatine recovery, fat fraction, and diffusion parameters.ResultsThere were no changes between baseline1 and baseline2 MRI metrics (p>0.2). Significant changes (p2 and postintervention MRI metrics were: gastrocnemius medialis (GM) T1 –2.3%±3.0% and soleus T2 –3.2%±3.1%. Trends toward significant changes (0.052 and postintervention MRI metrics were: calf adipose infiltration –2.6%±6.4%, GM T1ρ –4.1%±7.7%, GM T2 –3.5%±6.4%, and gastrocnemius lateral T2 −4.6±7.4%. Insignificant changes were observed in gastrocnemius phosphocreatine recovery rate constant (p>0.3) and tibial nerve fractional anisotropy (p>0.6) and apparent diffusion coefficient (p>0.4).ConclusionsThe 10-week supervised exercise intervention program successfully reduced adiposity and altered resting tissue properties in the lower leg in DPN. Gastrocnemius mitochondrial oxidative capacity and tibial nerve microarchitecture changes were not observed, either due to lack of response to therapy or to lack of measurement sensitivity.

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

BACKGROUND: In addition to the articular cartilage, osteoarthritis (OA) affects several other tissues such as tendons, ligaments, and subchondral bone. T(1ρ) relaxation study of these short T(2) tissues may provide a more comprehensive evaluation of OA. PURPOSE: To develop a 3D spin-lattice relaxation in the rotating frame (T(1ρ)) prepared zero echo time (ZTE)-based pointwise encoding time reduction with radial acquisition (3D-T(1ρ)-PETRA) sequence for relaxation mapping of semisolid short-T(2) tissues on a clinical 3 T scanner. STUDY TYPE: Prospective. POPULATION: Phantom, two bovine whole knee joint and Achilles tendon specimens, 10 healthy volunteers with no known inflammation, trauma or pain in the knee or ankle. FIELD STRENGTH/SEQUENCE: A customized PETRA sequence to acquire fat-suppressed 3D T(1ρ)-weighted images tissues with semisolid short [Formula: see text] relaxation times in the knee and ankle joints at 3 T. ASSESSMENT: Mono- and biexponential T(1ρ) relaxation components were assessed in the patellar tendon (PT), anterior cruciate ligament (ACL), posterior cruciate ligament (PCL), and Achilles tendon (AT). STATISTICAL TESTS: Kruskal–Wallis with post-hoc Dunn’s test for multiple pairwise comparisons. RESULTS: Phantom and ex vivo studies showed the feasibility of T(1ρ) relaxation mapping using the proposed 3D-T(1ρ)-PETRA sequence. The in vivo study demonstrated an averaged mono-T(1ρ) relaxation of (median [IQR]) 15.9 [14.5] msec, 23.6 [9.4] msec, 17.4 [7.4] msec, and 5.8 [10.2] msec in the PT, ACL, PCL, and AT, respectively. The bicomponent analysis showed the short and long components (with their relative fractions) of 0.65 [1.0] msec (46.9 [15.3]%) and 37.3 [18.4] msec (53.1 [15.3]%) for PT, 1.7 [2.1] msec (42.5 [12.5]%) and 43.7 [17.8] msec (57.5 [12.5]%) for ACL, and 1.2 [1.9] msec (42.6 [14.0]%) and 27.7 [14.7] msec (57.3 [14.0]%) for PCL and 0.4 [0.02] msec (58.8 [13.3]%/) and 31.3 [10.8] msec (41.2 [13.3]%) for AT. Statistically significant (P≤ 0.05) differences were observed in the mono- and biexponential relaxation between several regions. DATA CONCLUSION: The 3D-T(1ρ)-PETRA sequence allows volumetric, isotropic (0.78 × 0.78 × 0.78 mm), biexponential T(1ρ) assessment with corresponding fractions of the tissues with semisolid short [Formula: see text].

BACKGROUND: Measuring T(1ρ) in the knee menisci can potentially be used as noninvasive biomarkers in detecting early-stage osteoarthritis (OA). PURPOSE: To demonstrate the feasibility of biexponential T(1ρ) relaxation mapping of human knee menisci. STUDY TYPE: Prospective. POPULATION: Eight healthy volunteers with no known inflammation, trauma, or pain in the knee and three symptomatic subjects with early knee OA. FIELD STRENGTH/SEQUENCE: Customized Turbo-FLASH sequence to acquire 3D-T(1ρ)-weighted images on a 3 T MRI scanner. ASSESSMENT: T(1ρ) relaxation values were assessed in 11 meniscal regions of interest (ROIs) using monoexponential and biexponential models. STATISTICAL TESTS: Nonparametric rank-sum tests, Kruskal–Wallis test, and coefficient of variation. RESULTS: The mean monoexponential T(1ρ) relaxation in the lateral menisci were 28.05 ± 4.2 msec and 37.06 ± 10.64 msec for healthy subjects and early knee OA patients, respectively, while the short and long components were 8.07 ± 0.5 msec and 72.35 ± 3.2 msec for healthy subjects and 2.63 ± 2.99 msec and 55.27 ± 24.76 msec for early knee OA patients, respectively. The mean monoexponential T(1ρ) relaxation in the medial menisci were 34.30 ± 3.8 msec and 37.26 ± 11.38 msec for healthy and OA patients, respectively, while the short and long components were 7.76 ± 0.7 msec and 72.19± 4.2 msec for healthy subjects and 3.06 ± 3.24 msec and 55.27 ± 24.59 msec for OA patients, respectively. Statistically significant (P≤ 0.05) differences were observed in the monoexponential relaxation between some of the ROIs. The T(1ρ,short) was significantly lower (P = 0.02) in the patients than controls. The rmsCV% ranges were 1.51–16.6%, 3.59–14.3%, and 4.91–15.6% for T(1ρ)-mono, T(1ρ)-short, and T(1ρ)-long, respectively. DATA CONCLUSION: Our results showed that in all ROIs, T(1ρ) relaxation times of outer zones (red zones) were less than inner zones (white zones). Monoexponential T(1ρ) was increased in medial, lateral, and body menisci of early OA while the biexponential numbers were decreased in early OA patients. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 1

BACKGROUND: The progressive loss of hyaline articular cartilage due to osteoarthritis (OA) changes the functional and biochemical properties of cartilage. Measuring the T1ρ along with the morphological assessment can potentially be used as noninvasive biomarkers in detecting early-stage OA. To correlate the biochemical and morphological data, submillimeter isotropic resolution for both studies is required. PURPOSE: To implement a high spatial resolution 3D-isotropic-MRI sequence for simultaneous assessment of morphological and biexponential T(1ρ) relaxometry of human knee cartilage in-vivo. STUDY TYPE: Prospective. POPULATION: Ten healthy volunteers with no known inflammation, trauma or pain in the knee. FIELD STRENGTH/SEQUENCE: Standard FLASH sequence and customized Turbo-FLASH sequence to acquire 3D-isotropic-T(1ρ)-weighted images on a 3T MRI scanner. ASSESSMENT: The mean volume and thickness along with mono and biexponential T1ρ relaxations were assessed in the articular cartilage of 10 healthy volunteers. STATISTICAL TESTS: Non-parametric rank-sum tests. Bland-Altman analysis and coefficient of variation RESULTS: The mean monoexponential T(1ρ) relaxation was 40.7±4.8ms while the long and short components were 58.2±3.9ms and 6.5±0.6ms, respectively. The mean fractions of long and short T(1ρ) relaxation components were 63.7±5.9% and 36.3±5.9%, respectively. Statistically significant (p ≤0.03) differences were observed in the monoexponential and long components between some of the ROIs. No gender differences between biexponential components were observed (p>0.05). Mean cartilage volume and thickness were 25.9±6.4cm(3) and 2.2±0.7mm, respectively. Cartilage volume (p=0.01) and thickness (p=0.03) were significantly higher in male than female participants across all ROIs. Bland-Altman analysis showed agreement between two morphological methods with limits of agreement between −1000mm(3) and +1100mm(3) for volume, and −0.78mm and +0.46mm for thickness, respectively. DATA CONCLUSION: Simultaneous assessment of morphological and multicomponent T1ρ relaxation of knee joint with 0.7mm×0.7mm×0.7mm isotropic spatial resolution is demonstrated in-vivo. Comparison with a standard method showed that the proposed technique is suitable for assessing the volume and thickness of articular cartilage

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%.

Detection of multiple relaxation pools using MRI is useful in a number of neuro-pathologies including multiple sclerosis (MS), Alzheimer’s, and stroke. In this study we evaluate the feasibility of using T1ρ imaging for the detection of bi-exponential decays in the human brain. A prospective T1ρ imaging study was performed on model relaxation phantoms (eggs) and 7 healthy volunteers. The data was fitted using a single pool and a 2-pool model to estimate mono- and bi-exponential T1ρ maps, respectively. Bi-exponential decays were identified in the gray matter (GM) and white matter (WM) of the brain with 40.5% of GM, and 65.1% of WM pixels showing two T1ρ relaxation pools (significance level P 2 based contrast regarding the in vivo micro-environment in the brain.

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%.

The goal of this paper was to evaluate the possibility of bi-component T1ρ and T2 relaxation mapping of human skeletal muscle at 3 T in clinically feasible scan times. T1ρ- and T2-weighted images of calf muscle were acquired using a modified 3D-SPGR sequence on a standard 3 T clinical MRI scanner. The mono- and biexponential models were fitted pixel-wise to the series of T1ρ and T2 weighted images. The biexponential decay of T1ρ and T2 relaxations was detected in ~30% and ~40% of the pixels across all volunteers, respectively. Monoexponential and bi-exponential short and long T1ρ relaxation times were estimated to be 26.9 ms, 4.6 ms (fraction 22%) and 33.2 ms (fraction: 78%), respectively. Similarly, the mono- and bi-exponential short and long T2 relaxation times were 24.7 ms, 4.2 ms (fraction 15%) and 30.4 ms (fraction 85%) respectively. The experiments had good repeatability with RMSCV 60%. This approach could potentially be used in exercise intervention studies or in studies of inflammatory myopathies or muscle fibrosis, permitting greater sensitivity and specificity via measurement of different water compartments and their fractions.

In this study, we demonstrated the feasibility of in-vivo biexponential T1ρ relaxation mapping of knee articular cartilage of 8 healthy volunteers using 3T MRI in clinically feasible scan times. Our preliminary results show that the biexponential model may better represents the T1ρ relaxation decay in articular cartilage. It is possible distinguish between tightly bound and loosely bound water compartments associated with macromolecules in the articular cartilage.

Visualization of small blood vessels feeding tumor sites provides important information on the tumors and their microenvironment. This information plays an important role in targeted drug therapies using magnetic gradients. However, capabilities of current clinical imaging modalities may be insufficient to resolve complex microvascular networks. The purpose of this study is to map the vascular network, 3D, based on the magnetic susceptibility contrast.Magnetic particles induce an inhomogeneity in the MRI's magnetic field in an order much larger than their real size. This is an approach to compensate the spatial resolution insufficiency of a clinical MR scanner. Micron-sized agglomerations of magnetite nanoparticles were injected in a 3D phantom vascular network, and a fast multislice, multiacquisition MR sequence was applied to track the agglomerations along their trajectories. The experiment was performed twice for two different imaging planes: coronal and transversal. The susceptibility artifact in the images indicated the presence and the position of the agglomerations. The calculated positions through multiple images were assembled to build up the 3D distribution of the vascular network.The calculated points were compared with the centerline of the channels, extracted from the 3D reference image, to determine the absolute measurement error. The mean error was measured to be approximately half of the pixel's size. It was found that the positioning error on the axis perpendicular to the imaging slice was nearly twice as high as on the imaging plane axes due to the slice thickness. In order to compensate for the lack of resolution on the perpendicular axis, the reconstruction was performed using a combination of coronal and transversal data. The combination of the coordinates led to a significant decrease in the mean measurement error at each segment in the vascular network (p0.001).A method for 3D reconstruction of a microvascular network based on the susceptibility contrast in MRI and using a clinical scanner and a commercial receiver coil was proposed. The method presents a novel approach for reconstruction of vascular networks using the susceptibility effect. The proposed method may be applied to resolve vascular networks at a micrometric scale.

Recently, Ultra-wide band signals have become attractive for their particular advantage of having high spatial resolution and good penetration ability which makes them suitable in medical applications. One of these applications is wireless detection of heart rate and respiration rate. Two hypothesis of static environment and fixed patient are considered in the method presented in previous literatures which are not valid for long term monitoring of ambulant patients. In this article, a new method to detect the respiration rate of a moving target is presented. The first algorithm is applied to the simulated and experimental data for detecting respiration rate of a fixed target. Then, the second algorithm is developed to detect respiration rate of a moving target. The proposed algorithm uses correlation for body movement cancellation, and then detects the respiration rate based on energy in frequency domain. The results of algorithm prove an accuracy of 98.4 and 97% in simulated and experimental data, respectively.