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

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.

Age and Gender-Dependence of Single-and Bi-Exponential T1ρ MR Parameters in Knee Ligaments

BACKGROUND

There is limited understanding of differences in the composition and structure of ligaments between healthy males and females, and individuals of different ages. Females present higher risk for ligament injuries than males and there are conflicting reports on its cause. This study looks into T1ρ parameters for an explanation as it relates to proteoglycan, collagen, and water content in these tissues.

PURPOSE

To investigate gender-related and age-related differences in T1ρ parameters in knee joint ligaments in healthy volunteers using a T1ρ-prepared zero echo-time (ZTE)-based pointwise-encoding time-reduction with radial acquisition (T1ρ-PETRA) sequence.

STUDY TYPE

Prospective.

POPULATION

The study group consisted of 22 healthy subjects (11 females, ages: 41 ± 18 years, and 11 males, ages: 41 ± 14 years) with no known inflammation, trauma, or pain in the knee joint.

FIELD STRENGTH/SEQUENCE

A T1ρ-prepared 3D-PETRA sequence was used to acquire fat-suppressed images with varying spin-lock lengths (TSLs) of the knee joint at 3T.

ASSESSMENT

Monoexponential, biexponential, and stretched-exponential 3D-PETRA-T1ρ parameters were measured in the anterior cruciate ligament (ACL), posterior cruciate ligament (PCL), and patellar tendon (PT) by manually drawing ROIs over the entirety of the tissues.

STATISTICAL TESTS

Mann-Whitney U-tests were used to compare 3D-PETRA-T1ρ parameters in the ACL, PCL, and PT between males and females. Spearman correlation coefficients were used to determine the association between age and T1ρ parameters. Statistical significance was defined as P < 0.05.

RESULTS

Significant correlations with age were found the three ligaments with most of the measured T1ρ parameters (rs = 0.28-0.74) with the exception of the short fraction in the PCL (P = 0.18), and the short relaxation time in the ACL (P = 0.58) and in the PCL (P = 0.14).

DATA CONCLUSION

3D-PETRA-T1ρ can detect age-related differences in monoexponential, biexponential, and stretched-exponential T1ρ parameters in three ligaments of healthy volunteers, which are thought to be related to changes in tissue composition and structure during the aging process.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

Improved quantitative parameter estimation for prostate T2 relaxometry using convolutional neural networks

OBJECTIVE

Quantitative parameter mapping conventionally relies on curve fitting techniques to estimate parameters from magnetic resonance image series. This study compares conventional curve fitting techniques to methods using neural networks (NN) for measuring T2 in the prostate.

MATERIALS AND METHODS

Large physics-based synthetic datasets simulating T2 mapping acquisitions were generated for training NNs and for quantitative performance comparisons. Four combinations of different NN architectures and training corpora were implemented and compared with four different curve fitting strategies. All methods were compared quantitatively using synthetic data with known ground truth, and further compared on in vivo test data, with and without noise augmentation, to evaluate feasibility and noise robustness.

RESULTS

In the evaluation on synthetic data, a convolutional neural network (CNN), trained in a supervised fashion using synthetic data generated from naturalistic images, showed the highest overall accuracy and precision amongst the methods. On in vivo data, this best performing method produced low-noise T2 maps and showed the least deterioration with increasing input noise levels.

DISCUSSION

This study showed that a CNN, trained with synthetic data in a supervised manner, may provide superior T2 estimation performance compared to conventional curve fitting, especially in low signal-to-noise regions.

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.

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.

Improved Quantitative Parameter Estimation for Prostate T2 Relaxometry using Convolutional Neural Networks

This work seeks to evaluate multiple methods for quantitative parameter estimation from standard T2 mapping acquisitions in the prostate. The T2 estimation performance of methods based on neural networks (NN) was quantitatively compared to that of conventional curve fitting techniques. Large physics-based synthetic datasets simulating T2 mapping acquisitions were generated for training NNs and for quantitative performance comparisons. Ten combinations of different NN architectures, training strategies, and training corpora were implemented and compared with four different curve fitting strategies. All methods were compared quantitatively using synthetic data with known ground truth, and further compared on in vivo test data, with and without noise augmentation, to evaluate feasibility and noise robustness. In the evaluation on synthetic data, a convolutional neural network (CNN), trained in a supervised fashion using synthetic data generated from naturalistic images, showed the highest overall accuracy and precision amongst all the methods. On in vivo data, this best-performing method produced low-noise T2 maps and showed the least deterioration with increasing input noise levels. This study showed that a CNN, trained with synthetic data in a supervised manner, may provide superior T2 estimation performance compared to conventional curve fitting, especially in low signal-to-noise regions.

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.