Objective: Only few studies have investigated quantitative magnetic resonance imaging (MRI) T₂ mapping of knee cartilage in population-based cohorts. Our objective was to evaluate the association between T₂ relaxation times of different cartilage segments and the presence of knee MRI-based osteoarthritis (OA) and patient characteristics in a large population-based cohort. Design: In this cross-sectional study, we included 673 females (mean age: 59.8 years; standard deviation: 3.7) scanned with 1.5T-MRI from a sub-cohort of the Rotterdam Study. T₂ relaxation times were calculated in six femoral and tibial cartilage regions of interest. Associations between T₂ relaxation times, MRI Osteoarthritis Knee Score (MOAKS)-based tibiofemoral OA, and Knee injury and Osteoarthritis Outcome Score (KOOS)-based symptom status were evaluated using multivariate fixed effects regression analyses. Results: A total of 1332 knees were included, of which 237 (17.7%) had MRI-based OA. Patients with OA had higher T₂ relaxation times across all cartilage segments, and T₂ values positively correlated with BMI (r = 0.17–0.46), the strongest correlations being in the lateral compartment. Weak associations were found between T₂ relaxation times and age. After adjustments, T₂ values in the lateral weight-bearing femur (OR: 0.67; 95%CI: 0.56–0.79), lateral tibia (OR: 1.11; 95%CI: 1.00–1.24), lateral posterior femur (OR: 1.48; 95%CI: 1.28–1.72), and medial posterior femur (OR: 1.14; 95%CI: 1.01–1.30), were associated with the presence of OA. T₂ relaxation times were not associated with the KOOS-based symptom status. Conclusion: In this population-based cohort, T₂ values were associated with BMI. Additionally, T₂ values in the lateral cartilage subregions were associated with MRI-based OA.

Purpose To evaluate the predictive utility of radiomic features extracted from ultrashort echo time (UTE) MRI in comparison to conventional proton density (PD) sequences for short-term (24-week) and long-term (5-year) clinical outcomes in patients with patellar tendinopathy (PT) receiving exercise therapy. Materials and methods This prospective study of 76 PT patients undergoing 24-week exercise therapy underwent baseline 3D UTE and PD MRI at 3.0 T. The patellar tendon segmentation used nnU-Net, evaluated with Dice coefficient. Six predictive models consisting of clinical covariates and radiomic features from UTE and PD were developed using Elastic Net with 10-fold cross-validation. Model performance in predicting responsiveness of the patient-reported Victorian Institute of Sports Assessment (VISA-P) score was evaluated using the area under the receiver operating characteristic curve (ROC AUC) and the precision-recall curve (PR AUC), with 95% confidence intervals. Results The mean Dice similarity coefficient for the automatic segmentation of the patellar tendon from 3D-PD was 0.92 (SD: 0.02) and from 3D-UTE-Cones 0.89 (SD: 0.03). The UTE-based radiomics model demonstrated the highest predictive performance at 24 weeks (ROC AUC: 0.714 [95% CI: 0.701–0.727]; PR AUC: 0.848 [0.837–0.858]), while the PD-based model showed the lowest (ROC AUC: 0.569 [0.553–0.584]; PR AUC: 0.710 [0.692–0.727]). At the 5-year follow-up, UTE radiomics maintained robust performance (ROC AUC: 0.692 [0.677–0.706]; PR AUC: 0.822 [0.810–0.834]), whereas PD radiomics remained limited (ROC AUC: 0.578 [0.561–0.594]; PR AUC: 0.694 [0.676–0.713]). Conclusions Radiomics features extracted from UTE MRI demonstrate the highest predictive performance for clinical outcomes.

High levels of physical activity or high BMI during puberty could negatively influence bone and cartilage development. Little is known about the effects of loading on patellar and femoral bone shape in a young population. Therefore, we aim to identify the association between 3D patella and femur shape and biomechanical loading in a young adolescent population. Participants were selected from an ongoing cohort study (Generation-R study). Participants that underwent knee-MRI at 13 years-old follow-up were included. Patellae and femora were segmented from these MRIs and using these 3D models, statistical shape modeling was performed. Generalized estimating equations were used to analyze the association between loading (BMI, physical activity and sports participation) and shape variation. Bonferroni correction was used to correct for multiple testing. 1912 participants underwent MRI of which 3638 patellae and 3355 femora were included in the statistical shape models. Nine patellar (modes 1–7, 10 and 11) and nine femoral (modes 1–3, 6–10 and 14) shape modes were associated with BMI. Sports participation at thirteen years old was associated with one patellar (mode 1) and two femoral (modes 1 and 6) shape modes. One shape mode (mode 12) was associated with sports participation at 9 and 13 years old. Sports participation and BMI were significantly associated with bone shape variations. BMI was associated with most shape variations found in our statistical shape models, emphasizing the significant impact of BMI on bone morphology during adolescence with implications for musculoskeletal health and injury prevention.

Objective: Quantitative Magnetic Resonance Imaging (MRI) holds great promise for the early detection of cartilage deterioration. Here, a Magnetic Resonance Fingerprinting (MRF) framework is proposed for comprehensive and rapid quantification of T ₁, T ₂ ^∗, and T _RAFF2 with whole-knee coverage. Methods: A MRF framework was developed to achieve quantification of Relaxation Along a Fictitious Field in the 2nd rotating frame of reference (T _RAFF2) along with T ₁ and T ₂ ^∗. The proposed sequence acquires 65 measurements of 25 high-resolution slices, interleaved with 7 inversion pulses and 40 RAFF2 trains, for whole-knee quantification in a total acquisition time of 3:25 min. Comparison with reference T ₁, T ₂ ^∗, and T _RAFF2 methods was performed in phantom and in seven healthy subjects at 3 T. Repeatability (test-retest) with and without repositioning was also assessed. Results: Phantom measurements resulted in good agreement between MRF and the reference with mean biases of -54, 2, and 5 ms for T ₁, T ₂ ^∗, and T _RAFF2, respectively. Complete characterization of the whole-knee cartilage was achieved for all subjects, and, for the femoral and tibial compartments, a good agreement between MRF and reference measurements was obtained. Across all subjects, the proposed MRF method yielded acceptable repeatability without repositioning (R ²≥ 0.94) and with repositioning (R ²≥ 0.57) for T ₁, T ₂ ^∗, and T _RAFF2. Significance: The short scan time combined with the whole-knee coverage makes the proposed MRF framework a promising candidate for the early assessment of cartilage degeneration with quantitative MRI, but further research may be warranted to improve repeatability after repositioning and assess clinical value in patients.

Objective: To examine the association between 3D patellar shape and 1) isolated magnetic resonance imaging (MRI)-based patellofemoral osteoarthritis (PFOA), 2) the morphological features of PFOA, and 3) the clinical symptoms of PFOA. Design: MRI data from 66 women with isolated MRI-based PFOA and 66 age- and BMI-matched healthy women were selected from a cohort study. The patellae were manually segmented from MRI scans and used to create a 3D statistical shape model (SSM) of the patella. Structural abnormalities were semi-standardized scored on MRI using MRI osteoarthritis knee score (MOAKS). Regression analyses were applied to determine the associations between the shape parameters retrieved from the SSM, group status, clinical symptoms, and structural abnormalities. Results: Four shape variants showed a statistically significant (<0.05) association with the group status. The mode responsible for most of the shape variations showed participants with PFOA possess a relatively thicker dorsal bump on the articular part of the patella, compared to patellae of control participants. Three of these variants showed an association with the presence of osteophytes and cartilage loss on the patella. Multiple associations were found between patellar shape and the clinical symptoms of PFOA. Conclusions: Patellar shape is associated with the prevalence of MRI-based PFOA in women. Some shape variants were also associated with clinical symptoms. Interestingly, one particular shape variant associated with the presence of MRI-based PFOA was earlier shown to be associated with structural abnormalities associated with OA in a population aged under 40. This may suggest that patellar shape may be an early detectable risk factor for PFOA.

Background: Altered perfusion might play an important role in the pathophysiology of patellofemoral pain (PFP), a common knee complaint with unclear pathophysiology. Purpose: To investigate differences in dynamic contrast-enhanced (DCE)-MRI perfusion parameters between patients with PFP and healthy control subjects. Population/Subjects/Phantom/Specimen/Animal Model: Thirty-five adult patients with PFP and 44 healthy adult control subjects. Field Strength/Sequence: 3T DCE-MRI consisting of a sagittal, anterior-posterior, frequency-encoded, fat-suppressed 3D spoiled gradient-echo sequence with intravenous contrast administration. Assessment: Patellar bone volumes of interest (VOIs) were delineated by a blinded observer. Quantitative perfusion parameters (k_ep and k_trans) were calculated from motion-compensated DCE-MRI data by fitting Tofts' model. Weighted mean and unweighted median values of k_ep and k_trans were computed within the patellar bone VOIs. Statistical Tests: Differences in patellar bone perfusion parameters were compared between groups by linear regression analyses, adjusted for confounders. Results: Mean differences of weighted mean and unweighted median were 0.0039 (95% confidence interval [CI] –0.0013; 0.0091) and 0.0052 (95% CI –0.0078; 0.018) for k_trans, and 0.046 (95% CI –0.021; 0.11) and 0.069 (95% CI –0.017; 0.15) for k_ep, respectively. All perfusion parameters were not significantly different between groups (P-values: 0.32; 0.47 for k_trans, and 0.24; 0.15) for k_ep. However, a significant difference in variance between populations was observed for k_trans (P-value 0.007). Data Conclusion: Higher patellar bone perfusion parameters were found in patients with PFP when compared to healthy control subjects, but these differences were not statistically significant. This result, and the observed significant difference in k_trans variance, warrant further research. Level of Evidence: 1. Technical Efficacy: Stage 3. J. Magn. Reson. Imaging 2018;47:1344–1350.

Purpose: To identify the optimal combination of pharmacokinetic model and arterial input function (AIF) for quantitative analysis of blood perfusion in the patellar bone using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Materials and Methods: This method design study used a random subset of five control subjects from an Institutional Review Board (IRB)-approved case–control study into patellofemoral pain, scanned on a 3T MR system with a contrast-enhanced time-resolved imaging of contrast kinetics (TRICKS) sequence. We systematically investigated the reproducibility of pharmacokinetic parameters for all combinations of Orton and Parker AIF models with Tofts, Extended Tofts (ETofts), and Brix pharmacokinetic models. Furthermore, we evaluated if the AIF should use literature parameters, be subject-specific, or group-specific. Model selection was based on the goodness-of-fit and the coefficient of variation of the pharmacokinetic parameters inside the patella. This extends previous studies that were not focused on the patella and did not evaluate as many combinations of arterial and pharmacokinetic models. Results: The vascular component in the ETofts model could not reliably be recovered (coefficient of variation [CV] of v_p >50%) and the Brix model parameters showed high variability of up to 20% for k_el across good AIF models. Compared to group-specific AIF, the subject-specific AIF's mostly had higher residual. The best reproducibility and goodness-of-fit were obtained by combining Tofts' pharmacokinetic model with the group-specific Parker AIF. Conclusion: We identified several good combinations of pharmacokinetic models and AIF for quantitative analysis of perfusion in the patellar bone. The recommended combination is Tofts pharmacokinetic model combined with a group-specific Parker AIF model. Level of Evidence: 2. Technical Efficacy: Stage 1. J. Magn. Reson. Imaging 2018;47:848–858.