Faculty Bibliography

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.

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.

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.

PURPOSE:mapping. METHODS: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:mapping of the knee joint.

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.

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.