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PurposeTo organize a multi-institute knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression.Materials and methodsA dataset partition consisting of three-dimensional knee MRI from 88 retrospective patients at two time points (baseline and 1-year follow-up) with ground truth articular (femoral, tibial, and patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated against ground truth segmentations using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a holdout test set. Similarities in automated segmentations were measured using pairwise Dice coefficient correlations. Articular cartilage thickness was computed longitudinally and with scans. Correlation between thickness error and segmentation metrics was measured using the Pearson correlation coefficient. Two empirical upper bounds for ensemble performance were computed using combinations of model outputs that consolidated true positives and true negatives.ResultsSix teams (T 1-T 6) submitted entries for the challenge. No differences were observed across any segmentation metrics for any tissues (P = .99) among the four top-performing networks (T 2, T 3, T 4, T 6). Dice coefficient correlations between network pairs were high (> 0.85). Per-scan thickness errors were negligible among networks T 1-T 4 (P = .99), and longitudinal changes showed minimal bias (< 0.03 mm). Low correlations (ρ < 0.41) were observed between segmentation metrics and thickness error. The majority-vote ensemble was comparable to top-performing networks (P = .99). Empirical upper-bound performances were similar for both combinations (P = .99).ConclusionDiverse networks learned to segment the knee similarly, where high segmentation accuracy did not correlate with cartilage thickness accuracy and voting ensembles did not exceed individual network performance.See also the commentary by Elhalawani and Mak in this issue.Keywords: Cartilage, Knee, MR-Imaging, Segmentation © RSNA, 2020Supplemental material is available for this article.

Objectives: The aim of this study was to assess the texture of repair tissue and tissue adjacent to the repair site after matrix-associated chondrocyte transplantation (MACT) of the knee using gray-level co-occurrence matrix (GLCM) texture analysis of T2 quantitative maps. Methods: Twenty patients derived from the MRI sub-study of multicenter, single-arm phase III study underwent examination on a 3 T MR scanner, including a T2 mapping sequence 12 and 24 months after MACT. Changes between the time points in mean T2 values and 20 GLCM features were assessed for repair tissue, adjacent tissue, and reference cartilage. Differences in T2 values and selected GLCM features between the three cartilage sites at two time points were analyzed using linear mixed-effect models. Results: A significant decrease in T2 values after MACT, between time points, was observed only in repair cartilage (p < 0.001). Models showed significant differences in GLCM features between repair tissue and reference cartilage, namely, autocorrelation (p < 0.001), correlation (p = 0.015), homogeneity (p = 0.002), contrast (p < 0.001), and difference entropy (p = 0.047). The effect of time was significant in a majority of models with regard to GLCM features (except autocorrelation) (p ≤ 0.001). Values in repair and adjacent tissue became similar to reference tissue over time. Conclusions: GLCM is a useful add-on to T2 mapping in the evaluation of knee cartilage after MACT by increasing the sensitivity to changes in cartilage structure. The results suggest that cartilage tissue adjacent to the repair site heals along with the cartilage implant. Key Points: • GLCM is a useful add-on to T2mapping in the evaluation of knee cartilage after MACT by increasing the sensitivity to changes in cartilage structure. • Repair and adjacent tissue became similar to reference tissue over time. • The results suggest that cartilage tissue adjacent to the repair site heals along with the cartilage implant. [ABSTRACT FROM AUTHOR]

Objectives: To determine the relaxation times of the sodium nucleus, and to investigate the repeatability of quantitative, in vivo TSC measurements using sodium magnetic resonance imaging (23Na-MRI) in human skeletal muscle and explore the discriminatory value of the method by comparing TSCs between healthy subjects and patients with Addison's disease.Materials and Methods: In this prospective study, ten healthy subjects and five patients with Addison's disease were involved. 23Na-MRI data sets were acquired using a density-adapted, three-dimensional radial projection reconstruction pulse sequence (DA-3DPR) with a modification for the relaxation times measurements. Differences in TSC between muscle groups and between healthy participants were analysed using a nonparametric Friedman ANOVA test. An interclass correlation coefficient (ICC) was used as the repeatability index. Wilcoxon rank sum test was used for evaluation of differences in TSC between study participants.Results: The mean T1 in the gastrocnemius medialis (GM), the tibialis anterior (TA), and the soleus (S) was 25.9 ± 2.0 ms, 27.6 ± 2.0 ms, and 28.2 ± 2.0 ms, respectively. The mean short component of T2*, T2*short were GM: 3.6 ± 2.0 ms; TA: 3.2 ± 0.5 ms; and S: 3.0 ± 1.0 ms, and the mean long component of T2*, T2*long, were GM: 12.9 ± 0.9 ms; TA: 12.8 ± 0.7 ms; and S: 12.9 ± 2.0 ms, respectively. In healthy volunteers, TSC values in the GM were 19.9 ±0.1  mmol/L, 13.8 ±0.2 mmol/L in TA, and 12.6 ± 0.2 mmol/L in S, and were significantly different (p = 0.0005). The ICCs for GM, TA and S were 0.784, 0.818, 0.807, respectively. In patients with Addison's disease, TSC in GC, TA, and S were 10.2 ± 1.0 mmol/L, 8.4 ± 0.6 mmol/L, and 7.2 ± 0.1 mmol/L, respectively.Conclusions: TSC quantification in a healthy subject's calf at 7.0 T is reliable; the technique is able to distinguish sodium level differences between muscles and between healthy subjects and Addison's disease patients. [ABSTRACT FROM AUTHOR]

We measured and imaged magnetic field distributions of thin layers (2-D objects with negligible thickness) of biological and physical samples, by using nuclear magnetic resonance (NMR). The image represents the magnetic susceptibility distribution in the sample. We used a standard gradient echo imaging method, susceptible to magnetic field homogeneity, for detection. Since the physical and biological samples we investigated do not generate any NMR signal, we used a homogeneous phantom reference--a container filled with water--as a medium. The image acquired by this method is actually a projection of the sample properties onto the homogeneous phantom. The method can be applied in nanotechnology, microelectronics, and especially in the biological and medical sciences. Index Terms--Magnetic liquids, magnetic resonance imaging, magnetic susceptibility, thin layers.