Faculty Bibliography

BACKGROUND:) mapping is sensitive to early cartilage changes, but the mono-exponential (ME) model may be limited. Multi-component models can capture more tissue complexity, but their diagnostic advantage has not been validated. PURPOSE/OBJECTIVE:models can improve early knee OA detection over the ME model. STUDY TYPE/METHODS:Case-control study. POPULATION/METHODS:Twenty-six healthy subjects (mean age 51.5) and 26 early knee OA patients (mean age 61.8). FIELD STRENGTH/SEQUENCE/UNASSIGNED:-prepared Turbo FLASH sequence at 3 T field strength. ASSESSMENT/RESULTS:parameters from three exponential models were adjusted for age. To maximize group separability, the parameters were combined into single discriminators for both global knee cartilage and six anatomical sub-regions. Diagnostic performance was assessed based on the ability of these combined models to distinguish early OA. STATISTICAL TESTS/METHODS:Parameters were adjusted for age. Mann-Whitney U-test (group comparisons), linear discriminant analysis (LDA), and area under the receiver operating characteristic (ROC) curve (AUC) with bootstrapped 95% confidence intervals (CI). Significance level set at p < 0.05, using the false discovery rate (FDR) to correct for multiple comparisons. RESULTS:In the global analysis, no model demonstrated significant diagnostic performance (p-values of 0.63, 0.96, 0.63 for ME, SE, and BE). Multi-regional SE model (AUC = 0.83, CI: 0.72, 0.93) significantly distinguished OA and healthy groups. Calibration analysis showed the SE model had the lowest Brier score (0.17), significantly better than the ME model (0.26). DATA CONCLUSION/CONCLUSIONS:parameter maps suggests an improvement in diagnostic performance for early knee OA compared to globally averaged measurements. The stretched-exponential model showed the most promise. However, small sample size and wide confidence intervals highlight the need for further validation with a larger cohort before clinical utility claims can be made. EVIDENCE LEVEL/METHODS:4. TECHNICAL EFFICACY/UNASSIGNED:Stage 2.

PURPOSE/OBJECTIVE:mapping of the knee in vivo. METHODS:imaging was performed at 3 T. A bovine tendon was imaged at 0°, 55°, and 90° using CW, HS4 (3/6 ms), and TAN (3/6 ms) preparations at spin-lock amplitudes of 500-1000 Hz. Five healthy volunteers and five patients with knee osteoarthritis were scanned. Data were fit with mono-exponential (ME) and stretched-exponential (SE) models. Intrasubject repeatability was assessed in healthy volunteers. RESULTS:values most consistent with CW, while HS4-6 ms produced substantially higher values in the knee joint. TAN-6 ms (CV = 3.47%) and HS4-6 ms (CV = 4.24%) demonstrated better repeatability than CW (CV = 7.24%). CONCLUSION/CONCLUSIONS:contamination and high repeatability. The near orientation-independence of the SE parameter α suggests that it may serve as a more robust biomarker for highly ordered tissues.

This study investigates whether multi-component T₂ mapping, using bi-exponential (BE) and stretched-exponential (SE) models, enhances the early detection of knee osteoarthritis (OA) compared with the conventional mono-exponential (ME) approach. T₂ relaxation maps were derived from 26 patients with early-stage OA and 26 healthy controls. To minimize the influence of age-related cartilage changes, all model-derived parameters were adjusted for age prior to analysis. Quantitative T₂ parameters were extracted from six anatomically defined cartilage sub-regions to capture spatially heterogeneous tissue alterations characteristic of early OA. These parameters were then integrated using linear discriminant analysis to assess combined diagnostic performance. Global whole-cartilage analyses demonstrated limited discriminatory power across all models, with area under the receiver operating characteristic curve (AUC) values not exceeding 0.65, indicating that diffuse averaging obscures subtle, localized degeneration. In contrast, sub-regional analysis improved classification accuracy, highlighting the importance of regional assessment in early disease. Among the evaluated models, the BE-T₂ model showed the highest performance, achieving an AUC of 0.68, and marginally outperforming both the SE model (AUC = 0.60) and the ME model (AUC = 0.51). These findings suggest that multi-component T₂ mapping, particularly when applied at a sub-regional level, may offer improved sensitivity to early cartilage compositional changes. Overall, this approach shows strong potential as a noninvasive imaging biomarker for the early detection of knee OA.

We analyzed the feasibility of using a UTE stack-of-spirals turbo FLASH (STFL) sequence to measure T_1ρ relaxation in the Achilles tendon. Six HS (25-31 years) and five AT patients (32-47 years) participated. The study evaluates the clinical utility of the STFL sequence to generate T_1ρ maps using mono-exponential (ME) and bi-exponential (BE) fitting models. In a phantom experiment, ME-T_1ρ values and SNR estimated from the STFL sequence are compared with those of the Cartesian turbo FLASH (CTFL) sequence. In human subjects, we evaluate differences in estimated ME (ME-T_1ρ) and BE parameters (short T_1ρ, long T_1ρ, and short fraction) between AT and HS groups along with repeatability of STFL. The agarose phantom demonstrates biases of 2.89% (3% agarose), -1.88% (5%), and -0.92% (7%) between ME-T_1ρ values from STFL and CTFL. In the bovine Achilles tendon, STFL shows a large bias of -58.6%, with a lower median ME-T_1ρ (2.9 ms) than CTFL (4.6 ms). SNR is higher in STFL (77.05-80.72 for 3%-7% agarose; 24.43 for bovine tendon) than CTFL (66.73-58.97 for agarose; 3.21 for bovine tendon). ME and BE parameters were averaged over the entire Achilles tendon, and none showed significant group differences (p > 0.05; effect size = 0.05-0.22). Subregional analysis showed that in the mid-Achilles tendon, short and long BE-T_1ρ components were 26% and 37% lower in AT than HS, though not statistically significant. The LDA-combined BE parameter showed significant group separation in the midtendon region (p = 0.016; effect size = 1.53). In HS, the long BE-T_1ρ component showed subregional variation (p = 0.006), increasing 58% from calcaneal to midtendon, and then decreasing 23% toward the intramuscular region. ME and BE fitting showed high repeatability with scan-rescan variations of 2.64% (T_1ρ), 3.38% (short T_1ρ), 3.0% (long T_1ρ), and 0.21% (short fraction). We demonstrated the feasibility of using STFL for T_1ρ quantification in the Achilles tendon.

Multi-component T1ρ mapping of the knee joint using nonlinear least squares (NLS)-based methods is usually a computationally intensive task, limiting its application to only a few voxels in the knee joint. Deep learning (DL) is a computationally fast alternative, but requires a large amount of training data. We propose the Synthetic data-Guided supervised DL Network (SGDNet) that utilizes synthetically generated data for training, eliminating the need for large datasets of T1ρ maps. Initially, residual connections are added to improve gradient flow and stabilize training. A self-attention module is also integrated into the SGDNet to obtain more accurate estimated relaxation maps. Additionally, to ensure both parameter fidelity and data consistency, we employ a customized loss function that penalizes discrepancies between actual and predicted T1ρ values as well as between measured and simulated MR signals. To combine speed and precision, we further introduce HSGDNet, a hybrid approach that uses SGDNet's outputs as initialization for a few NLS iterations. Extensive experimental analysis reveals that HSGDNet outperforms the competing methods by achieving average error reductions of 91.4%, 31.5%, and 36.0% for mono-exponential (ME), stretched-exponential (SE), and bi-exponential (BE) components, respectively. HSGDNet accelerates whole-knee T1ρ fitting over NLS by approximately 67.4 × for ME, 53.9 × for SE, and 42.3 × for BE. Finally, to evaluate robustness under pathological and protocol variations, we validate HSGDNet on an early osteoarthritis (EOA) dataset acquired with distinct spin-lock times (TSLs) values. Overall, HSGDNet establishes itself as an efficient method for rapid, precise, and robust multi-component T1ρ mapping in the knee joint.

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.

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.