Cartilage transmits and redistributes biomechanical loads in the knee joint during exercise. Exercise-induced loading alters cartilage hydration and is detectable using quantitative magnetic resonance imaging (MRI), where T 2 relaxation time (T 2 ) is influenced by cartilage collagen composition, fiber orientation, and changes in the extracellular matrix. This study characterized short-term transient responses of healthy knee cartilage to running-induced loading using bilateral scans and image registration. Eleven healthy female recreational runners (33.73 ± 4.22 years) and four healthy female controls (27.25 ± 1.38 years) were scanned on a 3T GE MRI scanner with quantitative 3D double-echo in steady-state before running overground (runner group) or resting (control group) for 40 min. Subjects were scanned immediately post-activity at 5-min intervals for 60 min. T 2 times were calculated for femoral, tibial, and patellar cartilage at each time point and analyzed using a mixedeffects model and Bonferroni post hoc. There were immediate decreases in T 2 (mean ± SEM) post-run in superficial femoral cartilage of at least 3.3% ± 0.3% (p = .002) between baseline and Time 0 that remained for 25 min, a decrease in superficial tibial cartilage T 2 of 2.9% ± 0.4% (p = .041) between baseline and Time 0, and a decrease in superficial patellar cartilage T 2 of 3.6% ± 0.3% (p = .020) 15 min post-run. There were decreases in the medial posterior region of superficial femoral cartilage T 2 of at least 5.3 ± 0.2% (p = .022) within 5 min post-run that remained at 60 min post-run. These results increase understanding of transient responses of healthy cartilage to repetitive, exercise-induced loading and establish preliminary recommendations for future definitive studies of cartilage response to running.
Background: Deep learning (DL)-based automatic segmentation models can expedite manual segmentation yet require resource-intensive fine-tuning before deployment on new datasets. The generalizability of DL methods to new datasets without fine-tuning is not well characterized. Purpose: Evaluate the generalizability of DL-based models by deploying pretrained models on independent datasets varying by MR scanner, acquisition parameters, and subject population. Study Type: Retrospective based on prospectively acquired data. Population: Overall test dataset: 59 subjects (26 females); Study 1: 5 healthy subjects (zero females), Study 2: 8 healthy subjects (eight females), Study 3: 10 subjects with osteoarthritis (eight females), Study 4: 36 subjects with various knee pathology (10 females). Field Strength/Sequence: A 3-T, quantitative double-echo steady state (qDESS). Assessment: Four annotators manually segmented knee cartilage. Each reader segmented one of four qDESS datasets in the test dataset. Two DL models, one trained on qDESS data and another on Osteoarthritis Initiative (OAI)-DESS data, were assessed. Manual and automatic segmentations were compared by quantifying variations in segmentation accuracy, volume, and T2 relaxation times for superficial and deep cartilage. Statistical Tests: Dice similarity coefficient (DSC) for segmentation accuracy. Lin's concordance correlation coefficient (CCC), Wilcoxon rank-sum tests, root-mean-squared error-coefficient-of-variation to quantify manual vs. automatic T2 and volume variations. Bland-Altman plots for manual vs. automatic T2 agreement. A P value < 0.05 was considered statistically significant. Results: DSCs for the qDESS-trained model, 0.79-0.93, were higher than those for the OAI-DESS-trained model, 0.59-0.79. T2 and volume CCCs for the qDESS-trained model, 0.75-0.98 and 0.47-0.95, were higher than respective CCCs for the OAI-DESS-trained model, 0.35-0.90 and 0.13-0.84. Bland-Altman 95% limits of agreement for superficial and deep cartilage T2 were lower for the qDESS-trained model, AE2.4 msec and AE4.0 msec, than the OAI-DESS-trained model, AE4.4 msec and AE5.2 msec. Data Conclusion:The qDESS-trained model may generalize well to independent qDESS datasets regardless of MR scanner, acquisition parameters, and subject population.
Objective: To evaluate effects of a common CT contrast agent (iohexol) on the mechanical behaviors of cartilage and meniscus.Methods: Indentation responses of juvenile bovine cartilage and meniscus were monitored following exposure to undiluted contrast agent (100% CA), 50% CA/water, 50% CA/Phosphate Buffered Saline (PBS) or PBS alone, and during re-equilibration in PBS. The normalized peak increased with CA exposure time for cartilage but decreased for meniscus, suggesting an increased effective stiffness for cartilage and decreased stiffness for meniscus. Long-term changes to % &'( in both tissues were consistent with changes in ) * $$$ .Conclusion: Exposure to iohexol solutions affected joint tissues differentially, with increased cartilage stiffness, likely relating to competing hyperosmotic and hypotonic interactions with tissue fixed charges, and decreased meniscus stiffness, likely dominated by hyperosmolarity.These altered tissue mechanics could allow non-physiological deformation during ambulatory weight-bearing, resulting in an increased risk of tissue or cell damage.
Objective: To evaluate effects of a common CT contrast agent (iohexol) on the mechanical behaviors of cartilage and meniscus.Methods: Indentation responses of juvenile bovine cartilage and meniscus were monitored following exposure to undiluted contrast agent (100% CA), 50% CA/water, 50% CA/Phosphate
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