Centrality evolution of the charged-particle pseudorapidity density over a broad pseudorapidity range in Pb-Pb collisions at root s(NN)=2.76TeV

Peer reviewe

Deep learning-based segmentation of knee MRI for fully automatic subregional morphological assessment of cartilage tissues:data from the Osteoarthritis Initiative

Abstract Morphological changes in knee cartilage subregions are valuable imaging-based biomarkers for understanding progression of osteoarthritis, and they are typically detected from magnetic resonance imaging (MRI). So far, accurate segmentation of cartilage has been done manually. Deep learning approaches show high promise in automating the task; however, they lack clinically relevant evaluation. We introduce a fully automatic method for segmentation and subregional assessment of articular cartilage, and evaluate its predictive power in context of radiographic osteoarthritis progression. Two data sets of 3D double-echo steady-state (DESS) MRI derived from the Osteoarthritis Initiative were used: first, n = 88; second, n = 600, 0-/12-/24-month visits. Our method performed deep learning-based segmentation of knee cartilage tissues, their subregional division via multi-atlas registration, and extraction of subregional volume and thickness. The segmentation model was developed and assessed on the first data set. Subsequently, on the second data set, the morphological measurements from our and the prior methods were analyzed in correlation and agreement, and, eventually, by their discriminative power of radiographic osteoarthritis progression over 12 and 24 months, retrospectively. The segmentation model showed very high correlation (r > 0.934) and agreement (mean difference <  116 mm³) in volumetric measurements with the reference segmentations. Comparison of our and manual segmentation methods yielded r = 0.845–0.973 and mean differences = 262–501 mm³ for weight-bearing cartilage volume, and r = 0.770–0.962 and mean differences = 0.513–1.138 mm for subregional cartilage thickness. With regard to osteoarthritis progression, our method found most of the significant associations identified using the manual segmentation method, for both 12- and 24-month subregional cartilage changes. The method may be effectively applied in osteoarthritis progression studies to extract cartilage-related imaging biomarkers

Predicting osteoarthritis onset and progression with 3D texture analysis of cartilage MRI DESS:6-year data from osteoarthritis initiative

Abstract In this study, we developed a gray level co-occurrence matrix-based 3D texture analysis method for dual-echo steady-state (DESS) magnetic resonance (MR) images to be used for knee cartilage analysis in osteoarthritis (OA) studies and use it to study changes in articular cartilage between different subpopulations based on their rate of progression into radiographically confirmed OA. In total, 642 series of right knee DESS MR images at 3T were obtained from baseline, 36- and 72-month follow-ups from the OA Initiative database. At baseline, all 214 subjects included in the study had Kellgren-Lawrence (KL) grade <2. Three groups were defined, based on time of progression into radiographic OA (ROA) (KL grades ≥2): control (no progression), fast progressor (ROA at 36 months), and slow progressor (ROA at 72 months) groups. 3D texture analysis was used to extract textural features for femoral and tibial cartilages. All textural features, in both femur and tibia, showed significant longitudinal changes across all groups and tissue layers. Most of the longitudinal changes were observed in progressors, but significant changes were observed also in controls. Differences between groups were mostly seen at baseline and 72 months. The method is sensitive to cartilage changes before and after ROA. It was able to detect longitudinal changes in controls and progressors and to distinguish cartilage alterations due to OA and aging. Moreover, it was able to distinguish controls and different progressor groups before any radiographic signs of OA and during OA. Thus, texture analysis could be used as a marker for the onset and progression of OA

Association between quantitative MRI and ICRS arthroscopic grading of articular cartilage

Abstract Purpose: To investigate the association of quantitative magnetic resonance imaging (qMRI) parameters with arthroscopic grading of cartilage degeneration. Arthroscopy of the knee is considered to be the gold standard of osteoarthritis diagnostics; however, it is operator-dependent and limited to the evaluation of the articular surface. qMRI provides information on the quality of articular cartilage and its changes even at early stages of a disease. Methods: qMRI techniques included T₁ relaxation time, T₂ relaxation time, and delayed gadolinium-enhanced MRI of cartilage mapping at 3 T in ten patients. Due to a lack of generally accepted semiquantitative scoring systems for evaluating severity of cartilage degeneration during arthroscopy, the International Cartilage Repair Society (ICRS) classification system was used to grade the severity of cartilage lesions. qMRI parameters were statistically compared to arthroscopic grading conducted with the ICRS classification system. Results: qMRI parameters were not linearly related to arthroscopic grading. Spearman’s correlation coefficients between qMRI and arthroscopic grading were not significant. The relative differences in qMRI parameters of superficial and deep cartilage varied with degeneration, suggesting different macromolecular alterations in different cartilage zones. Conclusions: Results suggest that loss of cartilage and the quality of remaining tissue in the lesion site may not be directly associated with each other. The severity of cartilage degeneration may not be revealed solely by diagnostic arthroscopy, and thus, qMRI can have a role in the investigation of cartilage degeneration

Assessing post-traumatic changes in cartilage using T1ρ dispersion parameters

Abstract Degeneration of cartilage can be studied non-invasively with quantitative MRI. A promising parameter for detecting early osteoarthritis in articular cartilage is T1ρ, which can be tuned via the amplitude of the spin-lock pulse. By measuring T1ρ at several spin-lock amplitudes, the dispersion of T1ρis obtained. The aim of this study is to find out if the dispersion contains diagnostically relevant information complementary to a T1ρ measurement at a single spin-lock amplitude. To this end, five differently acquired dispersion parameters are utilized; A, B, τc, T1ρ/T2, and R2 − R1ρ. An open dataset of an equine model of post-traumatic cartilage was utilized to assess the T1ρ dispersion parameters for the evaluation of cartilage degeneration. Firstly, the parameters were compared for their sensitivity in detecting degenerative changes. Secondly, the relationship of the dispersion parameters to histological and biomechanical reference parameters was studied. Parameters A, T1ρ/T2, and R2 − R1ρ were found to be sensitive to lesion-induced changes in the cartilage within sample. Strong correlations of several dispersion parameters with optical density, as well as with collagen fibril angle were found. Most of the dispersion parameters correlated strongly with individual T1ρ values. The results suggest that dispersion parameters can in some cases provide a more accurate description of the biochemical composition of cartilage as compared to conventional MRI parameters. However, in most cases the information given by the dispersion parameters is more of a refinement than complementary to conventional quantitative MRI

Elevated adiabatic T₁ρ and T₁ρ in articular cartilage are associated with cartilage and bone lesions in early osteoarthritis:a preliminary study

Abstract Purpose: To evaluate adiabatic T₁ρ and T₂ρ of articular cartilage in symptomatic osteoarthritis (OA) patients and asymptomatic volunteers, and to determine their association with magnetic resonance imaging (MRI)‐based structural abnormalities in cartilage and bone. Materials and Methods: A total of 24 subjects (age range: 50–68 years; 12 female) were enrolled, including 12 early OA patients and 12 volunteers with normal joint function. Patients and volunteers underwent 3T MRI. T₂ρ, adiabatic T₁ρ, and T₂ρ relaxation times of knee articular cartilage were measured. Proton density (PD)‐ and T₁‐weighted MR image series were also obtained and separately evaluated for morphological changes using the MRI OA Knee Scoring (MOAKS) system. Comparisons using the Mann–Whitney nonparametric test were performed after dividing the study participants according to physical symptoms as determined by Western Ontario and McMaster Universities (WOMAC) score or presence of cartilage lesions, bone marrow lesions, or osteophytes. Results: Elevated adiabatic T₁ρ and T₂ρ relaxation times of articular cartilage were associated with cartilage loss (P = 0.024–0.047), physical symptoms (0.0068–0.035), and osteophytes (0.0039–0.027). Elevated adiabatic T₁ρ was also associated with bone marrow lesions (0.033). Conclusion: Preliminary data suggest that elevated adiabatic T₁ρ and T₂ρ of cartilage are associated with morphological abnormalities of cartilage and bone, and thus may be applicable for in vivo OA research and diagnostics

Machine learning prediction of collagen fiber orientation and proteoglycan content from multiparametric quantitative MRI in articular cartilage

Abstract Background: Machine learning models trained with multiparametric quantitative MRIs (qMRIs) have the potential to provide valuable information about the structural composition of articular cartilage. Purpose: To study the performance and feasibility of machine learning models combined with qMRIs for noninvasive assessment of collagen fiber orientation and proteoglycan content. Study type: Retrospective, animal model. Animal model: An open-source single slice MRI dataset obtained from 20 samples of 10 Shetland ponies (seven with surgically induced cartilage lesions followed by treatment and three healthy controls) yielded to 1600 data points, including 10% for test and 90% for train validation. Field strength/sequence: A 9.4 T MRI scanner/qMRI sequences: T₁, T₂, adiabatic T1ρ and T2ρ, continuous-wave T1ρ and relaxation along a fictitious field (TRAFF) maps. Assessment: Five machine learning regression models were developed: random forest (RF), support vector regression (SVR), gradient boosting (GB), multilayer perceptron (MLP), and Gaussian process regression (GPR). A nested cross-validation was used for performance evaluation. For reference, proteoglycan content and collagen fiber orientation were determined by quantitative histology from digital densitometry (DD) and polarized light microscopy (PLM), respectively. Statistical tests: Normality was tested using Shapiro–Wilk test, and association between predicted and measured values was evaluated using Spearman’s Rho test. A P-value of 0.05 was considered as the limit of statistical significance. Results: Four out of the five models (RF, GB, MLP, and GPR) yielded high accuracy (R² = 0.68–0.75 for PLM and 0.62–0.66 for DD), and strong significant correlations between the reference measurements and predicted cartilage matrix properties (Spearman’s Rho = 0.72–0.88 for PLM and 0.61–0.83 for DD). GPR algorithm had the highest accuracy (R² = 0.75 and 0.66) and lowest prediction-error (root mean squared [RMSE] = 1.34 and 2.55) for PLM and DD, respectively. Data conclusion: Multiparametric qMRIs in combination with regression models can determine cartilage compositional and structural features, with higher accuracy for collagen fiber orientation than proteoglycan content. Evidence level: 2 Technical efficacy: Stage