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.

Quantitative imaging is increasingly used for both clinical and research applications in musculoskeletal (MSK) disorders. Its widespread use, coupled with an assortment of modalities and vendors, has led to a diverse range of analysis methods and challenges in reproducing results both within and across centers. Clearly, consensus is needed to establish consistent data organization principles and thus permit the use of standardized image processing pipelines. Here, we—members of the Open and Reproducible Musculoskeletal Imaging Research (ORMIR) community—introduce the ORMIR-MIDS data format, which is derived from the existing Brain and Medical Imaging Data Structures (BIDS and MIDS) and extends them to MSK applications. ORMIR-MIDS comprises both a standard specification and a set of software tools for data conversion and organization. The latter permits the conversion of image data to a standardized format, in an organized folder structure, and produces up to 3 metadata files: important data-processing information, patient data, and complete image header tags to allow conversion back to the original format. The tool currently supports a range of imaging modalities and sub-modalities relevant to the MSK system, with more to come in the future. A suite of test data is also provided to demonstrate the functionality of the software, and the file system structure and associated image files and metadata after conversion of these test data are demonstrated here. With ORMIR-MIDS, we provide an open specification for multimodal MSK imaging, alongside tools for creating compliant datasets. Adherence to our standard will improve harmonization of imaging data across vendors and institutions and permit the development of reproducible processing pipelines and data repositories for MSK research.

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.