Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review.

Magnetic resonance imaging (MRI) stands as a vital medical imaging technique, renowned for its ability to offer high-resolution images of the human body with remarkable soft-tissue contrast. This enables healthcare professionals to gain valuable insights into various aspects of the human body, including morphology, structural integrity, and physiological processes. Quantitative imaging provides compositional measurements of the human body, but, currently, either it takes a long scan time or is limited to low spatial resolutions. Undersampled k-space data acquisitions have significantly helped to reduce MRI scan time, while compressed sensing (CS) and deep learning (DL) reconstructions have mitigated the associated undersampling artifacts. Alternatively, magnetic resonance fingerprinting (MRF) provides an efficient and versatile framework to acquire and quantify multiple tissue properties simultaneously from a single fast MRI scan. The MRF framework involves four key aspects: (1) pulse sequence design; (2) rapid (undersampled) data acquisition; (3) encoding of tissue properties in MR signal evolutions or fingerprints; and (4) simultaneous recovery of multiple quantitative spatial maps. This paper provides an extensive literature review of the MRF framework, addressing the trends associated with these four key aspects. There are specific challenges in MRF for all ranges of magnetic field strengths and all body parts, which can present opportunities for further investigation. We aim to review the best practices in each key aspect of MRF, as well as for different applications, such as cardiac, brain, and musculoskeletal imaging, among others. A comprehensive review of these applications will enable us to assess future trends and their implications for the translation of MRF into these biomedical imaging applications.

Characterization of Age-Related and Sex-Related Differences of Relaxation Parameters in the Intervertebral Disc Using MR-Fingerprinting.

BACKGROUND: Multiparameter characterization using MR fingerprinting (MRF) can quantify multiple relaxation parameters of intervertebral disc (IVD) simultaneously. These parameters may vary by age and sex. PURPOSE: To investigate age- and sex-related differences in the relaxation parameters of the IVD of the lumbar spine using a multiparameter MRF technique. STUDY TYPE: Prospective. SUBJECTS: 17 healthy subjects (8 male; mean age = 34 ± 10 years, range 20-60 years). FIELD STRENGTH/SEQUENCE: 3D-MRF sequence for simultaneous acquisition of proton density, T1 , T2 , and T1ρ maps at 3.0T. ASSESSMENT: Global mean T1 , T2 , and T1ρ of all lumbar IVDs and mean T1 , T2 , and T1ρ of each individual IVD (L1-L5) were measured. Gray level co-occurrence matrix was used to quantify textural features (median, contrast, correlation, energy, and homogeneity) from T1 , T2 , and T1ρ maps. STATISTICAL TESTS: Spearman rank correlations (R) evaluated the association between age and T1 , T2 , and T1ρ of IVD. Mann-Whitney U-tests evaluated differences between males and females in T1 , T2 , and T1ρ of IVD. Statistical significance was defined as P-value <0.05. RESULTS: There was a significant negative correlation between age and global mean values of all IVDs for T1 (R = -0.637), T2 (R = -0.509), and T1ρ (R = -0.726). For individual IVDs, there was a significant negative correlation between age and mean T1 at all IVD segments (R range = -0.530 to -0.708), between age and mean T2 at L2-L3, L3-L4, and L4-L5 (R range = -0.493 to 0.640), and between age and mean T1ρ at all segments except L1-L2 (R range = -0.632 to -0.763). There were no significant differences between sexes in global mean T1 , T2, and T1ρ (P-value = 0.23-0.76) The texture features with the highest significant correlations with age for all IVDs were global T1ρ mean (R = -0.726), T1 energy (R = -0.681), and T1 contrast (R = 0.709). CONCLUSION: This study showed that the 3D-MRF technique has potential to characterize age-related differences in T1 , T2, or T1ρ of IVD in healthy subjects. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

Repeatability of Quantitative Knee Cartilage T1 , T2 , and T1ρ Mapping With 3D-MRI Fingerprinting.

BACKGROUND: Three-dimensional MR fingerprinting (3D-MRF) techniques have been recently described for simultaneous multiparametric mapping of knee cartilage. However, investigation of repeatability remains limited. PURPOSE: To assess the intra-day and inter-day repeatabilities of knee cartilage T1 , T2 , and T1ρ maps using a 3D-MRF sequence for simultaneous measurement. STUDY TYPE: Prospective. SUBJECTS: Fourteen healthy subjects (35.4 ± 9.3 years, eight males), scanned on Day 1 and Day 7. FIELD STRENGTH/SEQUENCE: 3 T/3D-MRF, T1 , T2 , and T1ρ maps. ASSESSMENT: The acquisition of 3D-MRF cartilage (simultaneous acquisition of T1 , T2 , and T1ρ maps) were acquired using a dictionary pattern-matching approach. Conventional cartilage T1 , T2 , and T1ρ maps were acquired using variable flip angles and a modified 3D-Turbo-Flash sequence with different echo and spin-lock times, respectively, and were fitted using mono-exponential models. Each sequence was acquired on Day 1 and Day 7 with two scans on each day. STATISTICAL TESTS: The mean and SD for cartilage T1 , T2 , and T1ρ were calculated in five manually segmented regions of interest (ROIs), including lateral femur, lateral tibia, medial femur, medial tibia, and patella cartilages. Intra-subject and inter-subject repeatabilities were assessed using coefficient of variation (CV) and intra-class correlation coefficient (ICC), respectively, on the same day and among different days. Regression and Bland-Altman analysis were performed to compare maps between the conventional and 3D-MRF sequences. RESULTS: The CV in all ROIs was lower than 7.4%, 8.4%, and 7.5% and the ICC was higher than 0.56, 0.51, and 0.52 for cartilage T1 , T2 , and T1ρ , respectively. The MRF results had a good agreement with the conventional methods with a linear regression slope >0.61 and R2 > 0.59. CONCLUSION: The 3D-MRF sequence had high intra-subject and inter-subject repeatabilities for simultaneously measuring knee cartilage T1 , T2 , and T1ρ with good agreement with conventional sequences. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.

Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review.

Magnetic Resonance Imaging (MRI) is an essential medical imaging modality that provides excellent soft-tissue contrast and high-resolution images of the human body, allowing us to understand detailed information on morphology, structural integrity, and physiologic processes. However, MRI exams usually require lengthy acquisition times. Methods such as parallel MRI and Compressive Sensing (CS) have significantly reduced the MRI acquisition time by acquiring less data through undersampling k-space. The state-of-the-art of fast MRI has recently been redefined by integrating Deep Learning (DL) models with these undersampled approaches. This Systematic Literature Review (SLR) comprehensively analyzes deep MRI reconstruction models, emphasizing the key elements of recently proposed methods and highlighting their strengths and weaknesses. This SLR involves searching and selecting relevant studies from various databases, including Web of Science and Scopus, followed by a rigorous screening and data extraction process using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. It focuses on various techniques, such as residual learning, image representation using encoders and decoders, data-consistency layers, unrolled networks, learned activations, attention modules, plug-and-play priors, diffusion models, and Bayesian methods. This SLR also discusses the use of loss functions and training with adversarial networks to enhance deep MRI reconstruction methods. Moreover, we explore various MRI reconstruction applications, including non-Cartesian reconstruction, super-resolution, dynamic MRI, joint learning of reconstruction with coil sensitivity and sampling, quantitative mapping, and MR fingerprinting. This paper also addresses research questions, provides insights for future directions, and emphasizes robust generalization and artifact handling. Therefore, this SLR serves as a valuable resource for advancing fast MRI, guiding research and development efforts of MRI reconstruction for better image quality and faster data acquisition.

Age and gender differences in lumbar intervertebral disk strain using mechanical loading magnetic resonance imaging.

The objective of the current study was to investigate age- and gender-related differences in lumbar intervertebral disk (IVD) strain with the use of static mechanical loading and continuous three-dimensional (3D) golden-angle radial sparse parallel (GRASP) MRI. A continuous 3D-GRASP stack-of-stars trajectory of the lumbar spine was performed on a 3-T scanner with static mechanical loading. Compressed sensing reconstruction, motion deformation maps, and Lagrangian strain maps during loading and recovery in the X-, Y-, and Z-directions were calculated for segmented IVD segments from L1/L2 to L5/S1. Mean IVD height was measured at rest. Spearman coefficients were used to evaluate the associations between age and global IVD height and global IVD strain. Mann-Whitney tests were used to compare global IVD height and global IVD strain in males and females. The prospective study enrolled 20 healthy human volunteers (10 males, 10 females; age 34.6 ± 11.4 [mean ± SD], range 22-56 years). Significant increases in compressive strain were observed with age, as evidenced by negative correlations between age and global IVD strain during loading (ρ = -0.76, p = 0.0046) and recovery (ρ = -0.68, p = 0.0251) in the loading X-direction. There was no significant correlation between age and global IVD height, global IVD strain during loading and recovery in the Y-direction, and global IVD strain during loading and recovery in the Z-direction. There were no significant differences between males and females in global IVD height and global IVD strain during loading and recovery in the X-, Y-, and Z-directions. It was concluded that our study demonstrated the significant role aging plays in internal dynamic strains in the lumbar IVD during loading and recovery. Older healthy individuals have reduced IVD stiffness and greater IVD compression during static mechanical loading of the lumbar spine. The GRASP-MRI technique demonstrates the feasibility to identify changes in IVD mechanical properties with early IVD degeneration due to aging.

Optimizing variable flip angles in magnetization-prepared gradient-echo sequences for efficient 3D-T1ρ mapping.

PURPOSE: To optimize the choice of the flip angles of magnetization-prepared gradient-echo sequences for improved accuracy, precision, and speed of 3D-T1ρ mapping. METHODS: We propose a new optimization approach for finding variable flip-angle values that improve magnetization-prepared gradient-echo sequences used for 3D-T1ρ mapping. This new approach can improve the accuracy and SNR, while reducing filtering effects. We demonstrate the concept in the three different versions of the magnetization-prepared gradient-echo sequences that are typically used for 3D-T1ρ mapping and evaluate their performance in model agarose phantoms (n = 4) and healthy volunteers (n = 5) for knee joint imaging. We also tested the optimization with sequence parameters targeting faster acquisitions. RESULTS: Our results show that optimized variable flip angle can improve the accuracy and the precision of the sequences, seen as a reduction of the mean of normalized absolute difference from about 5%-6% to 3%-4% in model phantoms and from 15%-16% to 11%-13% in the knee joint, and improving SNR from about 12-28 to 22-32 in agarose phantoms and about 7-14 to 13-17 in healthy volunteers. The optimization can also compensate for the loss in quality caused by making the sequence faster. This results in sequence configurations that acquire more data per unit of time with SNR and mean of normalized absolute difference measurements close to its slower versions. CONCLUSION: The optimization of the variable flip angle can be used to increase accuracy and precision, and to improve the speed of the typical imaging sequences used for quantitative 3D-T1ρ mapping of the knee joint.

Updates on Compositional MRI Mapping of the Cartilage: Emerging Techniques and Applications.

Osteoarthritis (OA) is a widely occurring degenerative joint disease that is severely debilitating and causes significant socioeconomic burdens to society. Magnetic resonance imaging (MRI) is the preferred imaging modality for the morphological evaluation of cartilage due to its excellent soft tissue contrast and high spatial resolution. However, its utilization typically involves subjective qualitative assessment of cartilage. Compositional MRI, which refers to the quantitative characterization of cartilage using a variety of MRI methods, can provide important information regarding underlying compositional and ultrastructural changes that occur during early OA. Cartilage compositional MRI could serve as early imaging biomarkers for the objective evaluation of cartilage and help drive diagnostics, disease characterization, and response to novel therapies. This review will summarize current and ongoing state-of-the-art cartilage compositional MRI techniques and highlight emerging methods for cartilage compositional MRI including MR fingerprinting, compressed sensing, multiexponential relaxometry, improved and robust radio-frequency pulse sequences, and deep learning-based acquisition, reconstruction, and segmentation. The review will also briefly discuss the current challenges and future directions for adopting these emerging cartilage compositional MRI techniques for use in clinical practice and translational OA research studies. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Three-dimensional multi-parameter brain mapping using MR fingerprinting.

The purpose of this study was to develop and test a 3D multi-parameter MR fingerprinting (MRF) method for brain imaging applications. The subject cohort included 5 healthy volunteers, repeatability tests done on 2 healthy volunteers and tested on two multiple sclerosis (MS) patients. A 3D-MRF imaging technique capable of quantifying T1, T2 and T1ρ was used. The imaging sequence was tested in standardized phantoms and 3D-MRF brain imaging with multiple shots (1, 2 and 4) in healthy human volunteers and MS patients. Quantitative parametric maps for T1, T2, T1ρ, were generated. Mean gray matter (GM) and white matter (WM) ROIs were compared for each mapping technique, Bland-Altman plots and intra-class correlation coefficient (ICC) were used to assess repeatability and Student T-tests were used to compare results in MS patients. Standardized phantom studies demonstrated excellent agreement with reference T1/T2/T1ρ mapping techniques. This study demonstrates that the 3D-MRF technique is able to simultaneously quantify T1, T2 and T1ρ for tissue property characterization in a clinically feasible scan time. This multi-parametric approach offers increased potential to detect and differentiate brain lesions and to better test imaging biomarker hypotheses for several neurological diseases, including MS.

Data-driven optimization of sampling patterns for MR brain T1ρ mapping.

PURPOSE: The goal of this study was to apply a fast data-driven optimization algorithm, called bias-accelerated subset selection, for MR brain T1ρ mapping to generate optimized sampling patterns (SPs) for compressed sensing reconstruction of brain 3D-T1ρ MRI. METHODS: Five healthy volunteers were recruited, and fully sampled Cartesian 3D-T1ρ MRIs were obtained. Variable density (VD) and Poisson disc (PD) undersampling was used as the input to SP optimization process. The reconstruction used 3 compressed sensing methods: spatiotemporal finite differences, low-rank plus sparse with spatial finite differences, and low rank. The performance of images and T1ρ maps using PD-SP and VD-SP and their optimized sampling patterns (PD-OSP and VD-OSP) were compared to the fully sampled reference using normalized root mean square error (NRMSE). RESULTS: The VD-OSP with spatiotemporal finite differences reconstruction (NRMSE = 0.078) and the PD-OSP with spatiotemporal finite differences reconstruction (NRMSE = 0.079) at the highest acceleration factors (AF = 30) showed the largest improvement compared to the respective nonoptimized SPs (VD NRMSE = 0.087 and PD NRMSE = 0.149). Prospective undersampling was tested at AF = 4, with VD-OSP NRMSE = 0.057 versus PD-OSP NRMSE = 0.060, with optimized sampling performing better that input PD or VD sampling. For brain T1ρ mapping, the VD-OSP with low rank reconstruction for AFs <10 and VD-OSP with spatiotemporal finite differences for AFs >10 perform better. CONCLUSIONS: The study demonstrated that the appropriate use of data-driven optimized sampling and suitable compressed sensing reconstruction technique can be employed to potentially accelerate 3D T1ρ mapping for brain imaging applications.

Age-Dependent Changes in Knee Cartilage T1 , T2 , and T1p Simultaneously Measured Using MRI Fingerprinting.

BACKGROUND: Magnetic resonance fingerprinting (MRF) techniques have been recently described for simultaneous multiparameter cartilage mapping of the knee although investigation of their ability to detect early cartilage degeneration remains limited. PURPOSE: To investigate age-dependent changes in knee cartilage T1 , T2 , and T1p relaxation times measured using a three-dimensional (3D) MRF sequence in healthy volunteers. STUDY TYPE: Prospective. SUBJECTS: The study group consisted of 24 healthy asymptomatic human volunteers (15 males with mean age 34.9 ± 14.4 years and 9 females with mean age 44.5 ± 13.1 years). FIELD STRENGTH/SEQUENCE: A 3.0 T gradient-echo-based 3D-MRF sequence was used to simultaneously create proton density-weighted images and T1 , T2 , and T1p maps of knee cartilage. ASSESSMENT: Mean global cartilage and regional cartilage (lateral femur, lateral tibia, medial femur, medial tibia, and patella) T1 , T2 , and T1ρ relaxation times of the knee were measured. STATISTICAL TESTS: Kruskal-Wallis tests were used to compared cartilage T1 , T2 , and T1ρ relaxation times between different age groups, while Spearman correlation coefficients was used to determine the association between age and cartilage T1 , T2 , and T1ρ relaxation times. The value of P < 0.05 was considered statistically significant. RESULTS: Higher age groups showed higher global and regional cartilage T1 , T2 , and T1ρ . There was a significant difference between age groups in global cartilage T2 and T1ρ but no significant difference (P = 0.13) in global cartilage T1. Significant difference was also present between age groups in cartilage T2 and T1ρ for medial femur cartilage and medial tibia cartilage. There were significant moderate correlations between age and T2 and T1ρ for global cartilage (R2  = 0.63-0.64), medial femur cartilage (R2  = 0.50-0.56), and medial tibia cartilage (R2  = 0.54-0.66). CONCLUSION: Cartilage T2 and T1p relaxation times simultaneously measured using a 3D-MRF sequence in healthy volunteers showed age-dependent changes in knee cartilage, primarily within the medial compartment.

Optimization of spin-lock times for T1ρ mapping of human knee cartilage with bi- and stretched-exponential models.

Two optimization criteria based on Cramér-Rao Bounds are compared between each other and with non-optimized schedules for T1ρ mapping using synthetic data, model phantoms, and in-vivo knee cartilage. The curve fitting is done on complex-valued data using an iterative Nonlinear Least Squares (NLS) approach. The optimization criteria are compared based on the Mean Normalized Absolute Error (MNAE) and variance of the estimated parameters. The optimized spin-lock time (TSL) schedules provided improved results over the non-optimized schedules for all cases that were tested. The simulations showed that optimized schedules can reach the same precision and reduce acquisition times by 16.5 min (42%) for the bi-exponential model, and 6.6 min (22%) for the stretched-exponential model. In the model phantoms experiments, the bi-exponential MNAE was reduced from 0.47 to 0.36, while stretched-exponential from 0.28 to 0.20 with a Modified Cramér-Rao Lower Bound (MCRLB). In-vivo knee cartilage experiments show a reduction in bi-exponential MNAE from 0.47 to 0.31, and stretched-exponential from 0.047 to 0.039. The optimized spin-lock times criteria reduced the error in all cases, being more significant in the synthetic data and model phantoms. The optimized TSL schedules can be either used to improve the quality of parameter maps or reduce scan time.

3D magnetic resonance fingerprinting for rapid simultaneous T1, T2, and T1ρ volumetric mapping of human articular cartilage at 3 T.

Quantitative MRI can detect early biochemical changes in cartilage; however, the conventional techniques only measure one parameter (e.g., T1 , T2 , and T1ρ ) at a time while also being comparatively slow. We implemented a 3D magnetic resonance fingerprinting (3D-MRF) technique for simultaneous, volumetric mapping of T1 , T2 , and T1ρ in 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 T1 , T2, T1ρ relaxation times, and B1 + were 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 T1 , T2 , and T1ρ , respectively. The in vivo knee study showed excellent repeatability with rmsCV less than 1%, 2%, and 1% for T1 , T2 , and T1ρ , respectively. T1ρ relaxation time in the mild knee OA patients was significantly higher (p < 0.05) than in healthy subjects. The proposed 3D-MRF sequence is fast, reproducible, robust to B1 + inhomogeneity, and can simultaneously measure the T1 , T2 , T1ρ , and B1 + volumetric maps of the knee joint in a single scan within a clinically feasible scan time.

Alternating Learning Approach for Variational Networks and Undersampling Pattern in Parallel MRI Applications.

This work proposes an alternating learning approach to learn the sampling pattern (SP) and the parameters of variational networks (VN) in accelerated parallel magnetic resonance imaging (MRI). We investigate four variations of the learning approach, that alternates between improving the SP, using bias-accelerated subset selection, and improving parameters of the VN, using ADAM. The variations include the use of monotone or non-monotone alternating steps and systematic reduction of learning rates. The algorithms learn an effective pair to be used in future scans, including an SP that captures fewer k-space samples in which the generated undersampling artifacts are removed by the VN reconstruction. The quality of the VNs and SPs obtained by the proposed approaches is compared against different methods, including other kinds of joint learning methods and state-of-art reconstructions, on two different datasets at various acceleration factors (AF). We observed improvements visually and in three different figures of merit commonly used in deep learning (RMSE, SSIM, and HFEN) on AFs from 2 to 20 with brain and knee joint datasets when compared to the other approaches. The improvements ranged from 1% to 62% over the next best approach tested with VNs. The proposed approach has shown stable performance, obtaining similar learned SPs under different initial training conditions. We observe that the improvement is not only due to the learned sampling density, it is also due to the learned position of samples in k-space. The proposed approach was able to learn effective pairs of SPs and reconstruction VNs, improving 3D Cartesian accelerated parallel MRI applications.

Optimization of spin-lock times in T1ρ mapping of knee cartilage: Cramér-Rao bounds versus matched sampling-fitting.

PURPOSE: To compare different optimization approaches for choosing the spin-lock times (TSLs), in spin-lattice relaxation time in the rotating frame (T1ρ ) mapping. METHODS: Optimization criteria for TSLs based on Cramér-Rao lower bounds (CRLB) are compared with matched sampling-fitting (MSF) approaches for T1ρ 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 T1ρ 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 T1ρ mapping. All optimized criteria allowed good results even using rapid scans with two TSLs when a complex-valued fitting is done with iterative NLS or ANN.

Simultaneous bilateral T1 , T2 , and T1ρ relaxation mapping of the hip joint with magnetic resonance fingerprinting.

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 T1 , T2 , and T1ρ mapping of the hip joint. The study population consisted of six healthy volunteers with no known trauma or pain in the hip. Monoexponential T1 , T2 , and T1ρ 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 T1 (676.0 ± 45.4 and 687.6 ± 44.5 ms), T2 (22.5 ± 2.6 and 22.1 ± 2.5 ms), and T1ρ (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 T1 , p = 0.012 for T2 , and p < 0.001 for T1ρ ). The Wilcoxon test showed that T1 of femoral layers was significantly (p < 0.003) higher than that for acetabular cartilage. The experiments showed excellent repeatability with CVrms of 1%, 2%, and 4% for T1 , T2 , and T1ρ, respectively. It was concluded that bilateral T1 , T2 , and T1ρ relaxation times, as well as B1+ maps, can be acquired simultaneously from hip joints using the proposed MRF sequence.

Lower extremity MRI following 10-week supervised exercise intervention in patients with diabetic peripheral neuropathy.

INTRODUCTION: The 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 METHODS: Twenty 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. RESULTS: There were no changes between baseline1 and baseline2 MRI metrics (p>0.2). Significant changes (p<0.05) between baseline2 and postintervention MRI metrics were: gastrocnemius medialis (GM) T1 -2.3%±3.0% and soleus T2 -3.2%±3.1%. Trends toward significant changes (0.050.3) and tibial nerve fractional anisotropy (p>0.6) and apparent diffusion coefficient (p>0.4). CONCLUSIONS: The 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.

Fast data-driven learning of parallel MRI sampling patterns for large scale problems.

In this study, a fast data-driven optimization approach, named bias-accelerated subset selection (BASS), is proposed for learning efficacious sampling patterns (SPs) with the purpose of reducing scan time in large-dimensional parallel MRI. BASS is applicable when Cartesian fully-sampled k-space measurements of specific anatomy are available for training and the reconstruction method for undersampled measurements is specified; such information is used to define the efficacy of any SP for recovering the values at the non-sampled k-space points. BASS produces a sequence of SPs with the aim of finding one of a specified size with (near) optimal efficacy. BASS was tested with five reconstruction methods for parallel MRI based on low-rankness and sparsity that allow a free choice of the SP. Three datasets were used for testing, two of high-resolution brain images ([Formula: see text]-weighted images and, respectively, [Formula: see text]-weighted images) and another of knee images for quantitative mapping of the cartilage. The proposed approach has low computational cost and fast convergence; in the tested cases it obtained SPs up to 50 times faster than the currently best greedy approach. Reconstruction quality increased by up to 45% over that provided by variable density and Poisson disk SPs, for the same scan time. Optionally, the scan time can be nearly halved without loss of reconstruction quality. Quantitative MRI and prospective accelerated MRI results show improvements. Compared with greedy approaches, BASS rapidly learns effective SPs for various reconstruction methods, using larger SPs and larger datasets; enabling better selection of sampling-reconstruction pairs for specific MRI problems.

Pilot study quantifying muscle glycosaminoglycan using bi-exponential T1ρ mapping in patients with muscle stiffness after stroke.

Post stroke muscle stiffness is a common problem, which left untreated can lead to disabling muscle contractures. The purpose of this pilot study was to evaluate the feasibility of bi-exponential T1ρ mapping in patients with arm muscle stiffness after stroke and its ability to measure treatment related changes in muscle glycosaminoglycans (GAGs). Five patients with muscle stiffness after stroke and 5 healthy controls were recruited for imaging of the upper arm with 3D-T1ρ mapping. Patients were scanned before and after treatment with hyaluronidase injections, whereas the controls were scanned once. Wilcoxon Mann-Whitney tests compared patients vs. controls and patients pre-treatment vs. post-treatment. With bi-exponential modeling, the long component, T1ρl was significantly longer in the patients (biceps P = 0.01; triceps P = 0.004) compared to controls. There was also a significant difference in the signal fractions of the long and short components (biceps P = 0.03, triceps P = 0.04). The results suggest that muscle stiffness is characterized by increased muscle free water and GAG content. Post-treatment, the T1ρ parameters shifted toward control values. This pilot study demonstrates the application of bi-exponential T1ρ mapping as a marker for GAG content in muscle and as a potential treatment monitoring tool for patients with muscle stiffness after stroke.

Dynamic 31P-MRI and 31P-MRS of lower leg muscles in heart failure patients.

Impaired oxidative metabolism is one of multi-variate factors leading to exercise intolerance in heart failure patients. The purpose of the study was to demonstrate the use of dynamic 31P magnetic resonance spectroscopy (MRS) and 31P magnetic resonance imaging (MRI) techniques to measure PCr resynthesis rate post-exercise as a biomarker for oxidative metabolism in skeletal muscle in HF patients and controls. In this prospective imaging study, we recruited six HF patients and five healthy controls. The imaging protocol included 31P-MRS, spectrally selective 3D turbo spin echo for 31P-MRI, and Dixon multi-echo GRE for fat-water imaging on a 3 T clinical MRI scanner. All the subjects were scanned pre-exercise, during plantar flexion exercise, and post-exercise recovery, with two rounds of exercise for 31P -MRS and 31P-MRI, respectively. Unpaired t-tests were used to compare 31P-MRS and 31P-MRI results between the HF and control cohorts. The results show that PCr resynthesis rate was significantly slower in the HF cohort compared to the controls using 31P-MRS (P = 0.0003) and 31P-MRI (P = 0.0014). 31P-MRI showed significant differences between the cohorts in muscle groups (soleus (P = 0.0018), gastrocnemius lateral (P = 0.0007) and gastrocnemius medial (P = 0.0054)). The results from this study suggest that 31P-MRS/31P-MRI may be used to quantify lower leg muscle oxidative metabolism in HF patients, with 31P-MRI giving an additional advantage of allowing further localization of oxidative metabolism deficits. Upon further validation, these techniques may serve as a potentially useful clinical imaging biomarker for staging and monitoring therapies in HF-patients.

Measurement of Three-Dimensional Internal Dynamic Strains in the Intervertebral Disc of the Lumbar Spine With Mechanical Loading and Golden-Angle Radial Sparse Parallel-Magnetic Resonance Imaging.

BACKGROUND: Noninvasive measurement of internal dynamic strain can be potentially useful to characterize spine intervertebral disc (IVD) in the setting of injury or degenerative disease. PURPOSE: To develop and demonstrate a noninvasive technique to quantify three-dimensional (3D) internal dynamic strains in the IVD using a combination of static mechanical loading of the IVD using a magnetic resonance imaging (MRI)-compatible ergometer. STUDY TYPE: Prospective. SUBJECTS: Silicone gel phantom studies were conducted to assess strain variation with load and repeatability. Mechanical testing was done on the phantoms to confirm MR results. Eight healthy human volunteers (four men and four woman, age = 29 ± 5 years) underwent MRI using a rest, static loading, and recovery paradigm. Repeatability tests were conducted in three subjects. FIELD STRENGTH/SEQUENCE: MRI (3 T) with 3D continuous golden-angle radial sparse parallel (GRASP) and compressed sensing (CS) reconstruction. ASSESSMENT: CS reconstruction of the images, motion deformation, and Lagrangian strain maps were calculated for five IVD segments from L1/L2 to L5/S1. STATISTICAL TESTS: Ranges of displacement and strain in each subject and the resulting mean and standard deviation were calculated. Student t-tests were used to calculate changes in strain from loading to recovery. The correlation coefficient (CC) in the repeatability study was calculated. RESULTS: The most compressive strain experienced by the IVD segments under loaded conditions was in the L4/L5 segment (-7.5 ± 2.9%). The change in minimum strain from load to recovery was the most for the L4/L5 segment (-7.5% to -5.0%, P = 0.026) and the least for the L1/L2 segment (-4.4% to -3.9%, P = 0.51). In vivo repeatability in three subjects shows strong correlation between scans in subjects done 6 months apart, with CCs equal to 0.86, 0.94, and 0.94 along principal directions. DATA CONCLUSION: This study shows the feasibility of using static mechanical loading with continuous GRASP-MRI acquisition with CS reconstruction to measure 3D internal dynamic strains in the spine IVD. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 1.