Faculty Bibliography

Magnetic resonance fingerprinting (MRF), as an emerging versatile and noninvasive imaging technique, provides simultaneous quantification of multiple quantitative MRI parameters, which have been used to detect changes in cartilage composition and structure in osteoarthritis. Deep learning (DL)-based methods for quantification mapping in MRF overcome the memory constraints and offer faster processing compared to the conventional dictionary matching (DM) method. However, limited attention has been given to the fine-tuning of neural networks (NNs) in DL and fair comparison with DM. In this study, we investigate the impact of training parameter choices on NN performance and compare the fine-tuned NN with DM for multiparametric mapping in MRF. Our approach includes optimizing NN hyperparameters, analyzing the singular value decomposition (SVD) components of MRF data, and optimization of the DM method. We conducted experiments on synthetic data, the NIST/ISMRM MRI system phantom with ground truth, and in vivo knee data from 14 healthy volunteers. The results demonstrate the critical importance of selecting appropriate training parameters, as these significantly affect NN performance. The findings also show that NNs improve the accuracy and robustness of T₁, T₂, and T_1ρ mappings compared to DM in synthetic datasets. For in vivo knee data, the NN achieved comparable results for T₁, with slightly lower T₂ and slightly higher T_1ρ measurements compared to DM. In conclusion, the fine-tuned NN can be used to increase accuracy and robustness for multiparametric quantitative mapping from MRF of the knee joint.

PURPOSE/OBJECTIVE:To implement and evaluate the feasibility of brain spin-lattice relaxation in the rotating frame (T1ρ) mapping using a novel optimized pulse sequence that incorporates weighted spin-lock acquisitions, enabling high-resolution three-dimensional (3D) mapping. METHODS:The optimized variable flip-angle framework, previously proposed for knee T1ρ mapping, was enhanced by integrating weighted spin-lock acquisitions. This strategic combination significantly boosts signal-to-noise ratio (SNR) while reducing data acquisition time, facilitating high-resolution 3D-T1ρ mapping of the brain. The proposed sequence was compared with magnetization-prepared angle-modulated partitioned k-space spoiled gradient-echo sequence snapshots (MAPSS). RESULTS:) MAPSS in SNR. The weighted spin-lock acquisition combined with optimized variable flip angle improved the SNR over optimized variable flip angle alone by about 28%. CONCLUSION/CONCLUSIONS:Compared with the 20-min MAPSS sequence for brain T1ρ mapping, the proposed learned high-resolution 3D pulse sequence simultaneously achieved a 2.3-fold improvement in effective (3.2-fold nominal) spatial resolution, a 1.1-fold improvement in SNR, and a 2.5-fold reduction in scan time.

This study aimed to develop and evaluate a novel magnetization-prepared, ultra-short echo time (UTE)-capable, stack-of-spirals sequence (STFL) to quantify monoexponential and biexponential T_1ρ maps of the whole knee joint, addressing limitations of existing MRI techniques in assessing bone-patellar tendon-bone (BPTB) donor site healing and graft remodeling after anterior cruciate ligament (ACL) reconstruction (ACLR). Experiments were performed with agar-gel model phantoms, seven healthy volunteers (four males, average age 31.4 years old), and five ACLR patients (three males, average age 28.2 years old). Compared with a conventional Cartesian turbo fast low angle shot (CTFL) sequence, the STFL sequence demonstrated an improved signal-to-noise ratio (SNR), increasing from 16.5 for CTFL to 21.7 for STFL. In ACLR patients, the STFL sequence accurately detected increased fractions of short T_1ρ components within the ACL graft, rising from 0.15 to 0.38, compared with 0.11 to 0.18 with CTFL. Furthermore, the STFL sequence revealed significant decreases in the fraction of short T_1ρ components in the patellar tendon of ACLR patients (from 0.6 to 0.47) compared with healthy controls, whereas no significant changes were observed with the CTFL sequence. These findings suggest that the STFL sequence holds promise for noninvasive assessment of BPTB donor site healing and graft maturation following ACLR.

PURPOSE/OBJECTIVE:mapping of the knee joint, the magnetization-prepared angle-modulated partitioned k-space spoiled gradient echo snapshots (MAPSS), on bi-exponential (BE), stretched-exponential (SE), and mono-exponential (ME) relaxation models. METHODS:mapping of the knee joint using ME, SE, and BE models. The learned pulse sequence framework was used to improve quantitative accuracy and SNR and to reduce filtering effects. We compare the measured multi-compartment values between the two sequences (n = 8), and their repeatability (n = 4) in healthy volunteers (n = 12 total). RESULTS:repeatability tests showed a MAPD of 18.5% and 19.1% for MAPSS, and 16.8% and 15.5% for L-MPGRE. Bland-Altman region of interest (ROI)-wise analysis shows that bias is small, close to -1.5%, and the coefficient of variation is around 5.5% when comparing ROIs from both sequences. CONCLUSION/CONCLUSIONS:mapping in the knee cartilage with advantages, achieving similar accuracy and 15% better repeatability in only half of its scan time.

Non-linear least squares (NLS) methods are commonly used for quantitative magnetic resonance imaging (MRI), especially for multi-exponential T1ρ mapping, which provides precise parameter estimation for different relaxation models in tissues, such as mono-exponential (ME), bi-exponential (BE), and stretched-exponential (SE) models. However, NLS may suffer from problems like sensitivity to initial guesses, slow convergence speed, and high computational cost. While deep learning (DL)-based T1ρ fitting methods offer faster alternatives, they often face challenges such as noise sensitivity and reliance on NLS-generated reference data for training. To address these limitations of both approaches, we propose the HDNLS, a hybrid model for fast multi-component parameter mapping, particularly targeted for T1ρ mapping in the knee joint. HDNLS combines voxel-wise DL, trained with synthetic data, with a few iterations of NLS to accelerate the fitting process, thus eliminating the need for reference MRI data for training. Due to the inverse-problem nature of the parameter mapping, certain parameters in a specific model may be more sensitive to noise, such as the short component in the BE model. To address this, the number of NLS iterations in HDNLS can act as a regularization, stabilizing the estimation to obtain meaningful solutions. Thus, in this work, we conducted a comprehensive analysis of the impact of NLS iterations on HDNLS performance and proposed four variants that balance estimation accuracy and computational speed. These variants are Ultrafast-NLS, Superfast-HDNLS, HDNLS, and Relaxed-HDNLS. These methods allow users to select a suitable configuration based on their specific speed and performance requirements. Among these, HDNLS emerges as the optimal trade-off between performance and fitting time. Extensive experiments on synthetic data demonstrate that HDNLS achieves comparable performance to NLS and regularized-NLS (RNLS) with a minimum of a 13-fold improvement in speed. HDNLS is just a little slower than DL-based methods; however, it significantly improves estimation quality, offering a solution for T1ρ fitting that is fast and reliable.

BACKGROUND:Three-dimensional MR fingerprinting (3D-MRF) has been increasingly used to assess cartilage degeneration, particularly in the knee joint, by looking into multiple relaxation parameters. A comparable 3D-MRF approach can be adapted to assess cartilage degeneration for the hip joint, with changes to accommodate specific challenges of hip joint imaging. PURPOSE/OBJECTIVE:in clinically feasible scan times. STUDY TYPE/METHODS:Prospective. SUBJECTS/METHODS:Eight healthy subjects, three patients with mild osteoarthritis (OA), and one of the OA patients had femoral acetabular impingement (FAI). A National Institute of Standards and Technology/International Society for Magnetic Resonance in Medicine (NIST/ISMRM) system phantom was also used. FIELD STRENGTH/SEQUENCE/UNASSIGNED:mapping. ASSESSMENT/RESULTS:maps of 3D-MRF sequence were evaluated on a NIST/ISMRM phantom and human subjects. Differences in the parametric maps between OA and healthy subjects were assessed. STATISTICAL TESTS/METHODS:Regression, Bland-Altman, Kruskal-Wallis, and Wilcoxon tests were used to assess for accuracy, repeatability, and subregional variation. The P-value <0.05 indicated statistically significant. RESULTS:) in femoral lateral compartment of the hip joint compared to healthy controls. DATA CONCLUSION/CONCLUSIONS:3D-MRF may be a feasible approach for simultaneous, quantitative mapping of bilateral hip joint cartilage in healthy and mild OA patients. EVIDENCE LEVEL/METHODS:1 TECHNICAL EFFICACY: Stage 1.

Low-field strength scanners present an opportunity for more inclusive imaging exams and bring several challenges including lower signal-to-noise ratio (SNR) and longer scan times. Magnetic resonance fingerprinting (MRF) is a rapid quantitative multiparametric method that can enable multiple quantitative maps simultaneously. To demonstrate the feasibility of an MRF sequence for knee cartilage evaluation in a 0.55T system we performed repeatability and accuracy experiments with agar-gel phantoms. Additionally, five healthy volunteers (age 32 ± 4 years old, 2 females) were scanned at 3T and 0.55T. The MRI acquisition protocols include a stack-of-stars T_1ρ-enabled MRF sequence, a VIBE sequence with variable flip angles (VFA) for T₁ mapping, and fat-suppressed turbo flash (TFL) sequences for T₂ and T_1ρ mappings. Double-Echo steady-state (DESS) sequence was also used for cartilage segmentation. Acquisitions were performed at two different field strengths, 0.55T and 3T, with the same sequences but protocols were slightly different to accommodate differences in signal-to-noise ratio and relaxation times. Cartilage segmentation was done using five compartments. T₁, T₂, and T_1ρ values were measured in the knee cartilage using both MRF and conventional relaxometry sequences. The MRF sequence demonstrated excellent repeatability in a test-retest experiment with model agar-gel phantoms, as demonstrated with correlation and Bland-Altman plots. Underestimation of T₁ values was observed on both field strengths, with the average global difference between reference values and the MRF being 151 ms at 0.55T and 337 ms at 3T. At 0.55T, MRF measurements presented significant biases but strong correlations with the reference measurements. Although a larger error was present in T₁ measurements, MRF measurements trended similarly to the conventional measurements for human subjects and model agar-gel phantoms.

BACKGROUND:parameters for an explanation as it relates to proteoglycan, collagen, and water content in these tissues. PURPOSE/OBJECTIVE:-PETRA) sequence. STUDY TYPE/METHODS:Prospective. POPULATION/METHODS: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/UNASSIGNED:-prepared 3D-PETRA sequence was used to acquire fat-suppressed images with varying spin-lock lengths (TSLs) of the knee joint at 3T. ASSESSMENT/RESULTS: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/METHODS:parameters. Statistical significance was defined as P < 0.05. RESULTS: = 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/CONCLUSIONS: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/METHODS:2 TECHNICAL EFFICACY: Stage 1.

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/OBJECTIVE:maps using a 3D-MRF sequence for simultaneous measurement. STUDY TYPE/METHODS:Prospective. SUBJECTS/METHODS:Fourteen healthy subjects (35.4 ± 9.3 years, eight males), scanned on Day 1 and Day 7. FIELD STRENGTH/SEQUENCE/UNASSIGNED:maps. ASSESSMENT/RESULTS: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/METHODS: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: > 0.59. CONCLUSION/CONCLUSIONS:with good agreement with conventional sequences. EVIDENCE LEVEL/METHODS:1 TECHNICAL EFFICACY: Stage 1.

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.