Case Report: Dislocation of lateral menisci secondary to congenital lateral tibiofemoral cartilage thickening in both knees.

A 24-year-old male patient complained of mild knee pain after jogging. The subsequent knee MRI demonstrated bilateral lateral thickened tibiofemoral cartilages, evidenced by deformities of the bilateral subchondral bone beneath the lateral femoral condyle cartilage. The corresponding dislocations of almost all the left lateral meniscus and part of the right lateral meniscus to the center of the joint were detected. After excluding diagnoses of congenital ring-shaped meniscus, bucket handle tear of the C-shaped lateral meniscus, and central tear of the discoid meniscus, the displacement of all or part of the lateral meniscus into the intercondylar notch was considered a consequence of congenital thickening of the lateral superior and inferior cartilage. This case may report a new variant of knee joint pathology.

Cartilage thickness in the medial compartment of the knee joint evaluated by MRI three-dimensional analysis correlates with weight-bearing line ratio and joint line convergence angle.

PURPOSE: The correlation between cartilage thickness evaluated by 3D MRI and knee alignment has not been elucidated. The study's purpose was to retrospectively verify whether average cartilage thickness calculated by the automatic MRI 3D analysis system for each subregion was correlated with knee alignment. METHODS: A total of 53 patients underwent medial meniscus repair or high tibial osteotomy for medial knee osteoarthritis. Cartilage thickness was automatically calculated using 3D MRI software. Knee alignment, weight-bearing line ratio (WBLR), joint line convergence angle (JLCA), medial proximal tibial angle (MPTA), and lateral distal femoral angle (LDFA) were calculated on a weight-bearing long-leg radiograph using digital planning software. The association between knee alignment and the average cartilage thickness at 18 subregions in the medial femoral condyle (MFC) and medial tibial plateau (MTP) was evaluated using Spearman's rank correlation coefficient. RESULTS: Cartilage thickness of the MFC had moderately positive correlations with WBLR at four subregions and weak correlations at two subregions. Cartilage thickness of the MTP had moderately positive correlations with WBLR at four subregions. Cartilage thickness of the MFC had moderately negative correlations with JLCA at six subregions. Cartilage thickness of the MTP had moderately negative correlations with JLCA at four subregions and a weak correlation at one subregion. Conversely, cartilage thickness had weak correlations with MPTA or LDFA. CONCLUSIONS: In knees with pathological conditions in the medial compartment, cartilage thicknesses by 3D MRI had positive correlations with WBLR and JLCA at almost all subregions of the MFC and at the anterior-middle and central-external of the MTP. Treatment strategies should be considered in light of these regions. LEVEL OF EVIDENCE: Cross-sectional study (diagnosis); Level of evidence, 2.

Variations in knee cartilage thickness: Fully automatic three-dimensional analysis of MRIs from five manufacturers.

BACKGROUND: Measurements of knee cartilage thickness derived from MR images are attractive biomarkers for osteoarthritis research. Although some cross-sectional multivendor studies exist, none have employed fully automatic three-dimensional MRI analysis. Our objective was to evaluate the variations in knee cartilage thickness measurements obtained using automated methods and MRI instruments from five different vendors. METHODS: The subjects were 10 healthy volunteers aged 22-60 years. MRI models with 3 Tesla strength from five different companies were used. Cartilage thickness was quantified fully automatically for seven regions. We hypothesized that "the MRI model influences cartilage thickness measurements." Inter-measurement error, defined as the absolute difference between the targeted and median thicknesses determined by the five MRI models, was analyzed using histograms. The factors generating the largest inter-measurement error were also examined. RESULTS: No exceptional trends attributable to a specific instrument model were observed, and the p-value from the Kruskal-Wallis test exceeded 0.05 in all seven regions. Therefore, the study hypothesis was rejected. Of the 350 measurements, the inter-measurement error was ≤0.05 mm in 53 %, ≤0.10 mm in 75 %, and ≤0.20 mm in 95 %. Analysis of the medial tibial cartilage, which had the largest inter-measurement error, revealed mis-extraction of synovial fluid as cartilage. CONCLUSIONS: The choice of MRI model did not influence cartilage thickness measurements. Overall, 95 % of the inter-measurement errors were within 0.20 mm. The greatest error resulted from mis-extracting synovial fluid as cartilage.

Automated measurement and grading of knee cartilage thickness: a deep learning-based approach.

Background: Knee cartilage is the most crucial structure in the knee, and the reduction of cartilage thickness is a significant factor in the occurrence and development of osteoarthritis. Measuring cartilage thickness allows for a more accurate assessment of cartilage wear, but this process is relatively time-consuming. Our objectives encompass using various DL methods to segment knee cartilage from MRIs taken with different equipment and parameters, building a DL-based model for measuring and grading knee cartilage, and establishing a standardized database of knee cartilage thickness. Methods: In this retrospective study, we selected a mixed knee MRI dataset consisting of 700 cases from four datasets with varying cartilage thickness. We employed four convolutional neural networks-UNet, UNet++, ResUNet, and TransUNet-to train and segment the mixed dataset, leveraging an extensive array of labeled data for effective supervised learning. Subsequently, we measured and graded the thickness of knee cartilage in 12 regions. Finally, a standard knee cartilage thickness dataset was established using 291 cases with ages ranging from 20 to 45 years and a Kellgren-Lawrence grading of 0. Results: The validation results of network segmentation showed that TransUNet performed the best in the mixed dataset, with an overall dice similarity coefficient of 0.813 and an Intersection over Union of 0.692. The model's mean absolute percentage error for automatic measurement and grading after segmentation was 0.831. The experiment also yielded standard knee cartilage thickness, with an average thickness of 1.98 mm for the femoral cartilage and 2.14 mm for the tibial cartilage. Conclusion: By selecting the best knee cartilage segmentation network, we built a model with a stronger generalization ability to automatically segment, measure, and grade cartilage thickness. This model can assist surgeons in more accurately and efficiently diagnosing changes in patients' cartilage thickness.

Biomechanical properties of articular cartilage in different regions and sites of the knee joint: acquisition of osteochondral allografts.

Osteochondral allograft (OCA) transplantation involves grafting of natural hyaline cartilage and supporting subchondral bone into the cartilage defect area to restore its biomechanical and tissue structure. However, differences in biomechanical properties and donor-host matching may impair the integration of articular cartilage (AC). This study analyzed the biomechanical properties of the AC in different regions of different sites of the knee joint and provided a novel approach to OCA transplantation. Intact stifle joints from skeletally mature pigs were collected from a local abattoir less than 8 h after slaughter. OCAs were collected from different regions of the joints. The patella and the tibial plateau were divided into medial and lateral regions, while the trochlea and femoral condyle were divided into six regions. The OCAs were analyzed and compared for Young's modulus, the compressive modulus, and cartilage thickness. Young's modulus, cartilage thickness, and compressive modulus of OCA were significantly different in different regions of the joints. A negative correlation was observed between Young's modulus and the proportion of the subchondral bone (r = - 0.4241, P < 0.0001). Cartilage thickness was positively correlated with Young's modulus (r = 0.4473, P < 0.0001) and the compressive modulus (r = 0.3678, P < 0.0001). During OCA transplantation, OCAs should be transplanted in the same regions, or at the closest possible regions to maintain consistency of the biomechanical properties and cartilage thickness of the donor and recipient, to ensure smooth integration with the surrounding tissue. A 7 mm depth achieved a higher Young's modulus, and may represent the ideal length.

The knee connectome: A novel tool for studying spatiotemporal change in cartilage thickness.

Cartilage thickness change is a well-documented biomarker of osteoarthritis pathogenesis. However, there is still much to learn about the spatial and temporal patterns of cartilage thickness change in health and disease. In this study, we develop a novel analysis method for elucidating such patterns using a functional connectivity approach. Descriptive statistics are reported for 1186 knees that did not develop osteoarthritis during the 8 years of observation, which we present as a model of cartilage thickness change related to healthy aging. Within the control population, patterns vary greatly between male and female subjects, while body mass index (BMI) has a more moderate impact. Finally, several differences are shown between knees that did and did not develop osteoarthritis. Some but not all significance appears to be accounted for by differences in sex, BMI, and knee alignment. With this work, we present the connectome as a novel tool for studying spatiotemporal dynamics of tissue change.

[Quantitative study of 3.0T MRI on the thickness of knee joint cartilage in healthy young people].

OBJECTIVE: To explore 3.0T MRI accurate measurement of knee cartilage thickness in healthy youth provides reliable anatomical parameters for quantitative diagnosis of osteoarthritis and accurate osteotomy of joint replacement. METHODS: From January 2013 to December 2013, 30 healthy young volunteers including 14 males and 16 females with an average age of (25.8±2.4) years old ranging from 22 to 33 years were recruited in Changchun, Jilin Province, and a 3.0T MRI scan was performed on the bilateral knee joints of each volunteer. The cartilage thickness was measured on the lateral femoral condyle (LFC), medial femoral condyle (MFC), lateral tibial plateau (LTP) and medial tibial plateau (MTP). RESULTS: In four regions of the knee joint:LFC, MFC, LTP and MTP, whether young men or women, there was no significant difference in cartilage thickness between the left and right knee joints (P>0.05). There were significant differences in knee cartilage thickness between healthy young men and women (P<0.05). In the same sex group, LFC cartilage thickness was thinner in the middle, thicker in front and rear;MFC cartilage thickness was the thinnest in front and gradually thickening from the front to the rear; LTP cartilage thickness was thickest in the middle, second in the rear and thinnest in the front;MTP cartilage thickness was the thinnest in the front, was relatively uniform in the middle and rear and thicker than that in the front. CONCLUSION: In Northeast China, among healthy adults aged 22 to 33, gender difference may be an important factor in the difference of cartilage thickness in various regions of the knee joint. Regardless of whether male or female healthy young people, the cartilage thickness of the entire knee joint is unevenly distributed, but there is no significant difference in cartilage thickness in the same area between the left and right knee joints.

Ultrasonographic Evaluation of the Distal Femoral Cartilage Thickness in Parkinson's Patients.

OBJECTIVES: Both Parkinson's disease (PD) and osteoarthritis (OA) are characterized by chronic inflammation and tissue degeneration. The aim of this study is to investigate the relationship between PD and distal femoral cartilage thickness (DFCT). Our study is the first in the literature to measure DFCT in PD. METHODS: 68 patients with PD and 30 healthy individuals participated. The patient group was divided into three subgroups, according to the Hoehn Yahr stages (HYS): mild, moderate and severe. Patient subgroups and the control group were compared with each other in terms of neutrophil-lymphocyte ratio (NLR), C-reactive protein (CRP), and DFCT. RESULTS: The NLR and CRP levels of the PD patients were higher than the values of the healthy people. The DFCT values of the mild PD subgroup were significantly higher than those of the control group, except for one value. The DFCT values of the moderate PD subgroup and the healthy group were similar. The DFCT values of the severe PD subgroup were lower than the values of the healthy group. CONCLUSIONS: Our study showed the presence of ultrasonographic evidence consistent with early signs of cartilage destruction in early-stage PD disease. As the PD stage progressed, the cartilage thickness decreased accordingly.

Decreased Elastic Modulus of Knee Articular Cartilage Based on New Macroscopic Methods Accurately Represents Early Histological Findings of Degeneration.

OBJECTIVE: Ex vivo nanoindentation measurement has reported that elastic modulus decreases as cartilage degenerates, but no method has been established to macroscopically evaluate mechanical properties in vivo. The objective of this study was to evaluate the elastic modulus of knee joint cartilage based on macroscopic methods and to compare it with gross and histological findings of degeneration. DESIGN: Osteochondral sections were taken from 50 knees with osteoarthritis (average age, 75 years) undergoing total knee arthroplasty. The elastic modulus of the cartilage was measured with a specialized elasticity tester. Gross findings were recorded as International Cartilage Repair Society (ICRS) grade. Histological findings were graded as Mankin score and microscopic cartilage thickness measurement. RESULTS: In ICRS grades 0 to 2 knees with normal to moderate cartilage abnormalities, the elastic modulus of cartilage decreased significantly as cartilage degeneration progressed. The elastic modulus of cartilage was 12.2 ± 3.8 N/mm for ICRS grade 0, 6.3 ± 2.6 N/mm for ICRS grade 1, and 3.8 ± 2.4 N/mm for ICRS grade 2. Similarly, elastic modulus was correlated with Mankin score (r = -0.51, P < 0.001). Multiple regression analyses showed that increased Mankin score is the most relevant factor associated with decreased elastic modulus of the cartilage (t-value, -4.53; P < 0.001), followed by increased histological thickness of the cartilage (t-value, -3.15; P = 0.002). CONCLUSIONS: Mechanical properties of damaged knee cartilage assessed with new macroscopic methods are strongly correlated with histological findings. The method has potential to become a nondestructive diagnostic modality for early cartilage damage in the clinical setting.

Evaluation of the effect of high-intensity laser therapy (HILT) on function, muscle strength, range of motion, pain level, and femoral cartilage thickness in knee osteoarthritis: randomized controlled study.

This study was designed as a double-blind randomized placebo-controlled study. The aim of this study was to evaluate the effects of high-intensity laser therapy (HILT) on pain, range of motion, function, muscle strength, and femoral cartilage thickness in patients with knee osteoarthritis. Sixty patients who were admitted between November 2021 and April 2022 and diagnosed with knee osteoarthritis based on anamnesis, physical examination, and imaging methods were included in the study. The patients observed during the research were randomly divided into two groups with 30 patients in each group. Hotpack, transcutaneous electrical nerve stimulation (TENS), exercise (5 days a week for a total of 15 sessions), and HILT (analgesic mode with a power of 10.0 w, energy density of 12 j/cm2, and 2 min for every 25 cm2, biostimulant mode with a power of 5.0 W, energy density of 120 j/cm2, and 10 min for each 25 cm2; total 9 sessions 3 days a week) were applied for 3 weeks for the first group, and hot pack, TENS, exercise (5 days a week for a total of 15 sessions), and sham laser treatment (0 W total 9 sessions 3 days a week) was applied for 3 weeks for the second group. The patients were evaluated with the determined scales before the treatment, at the end of the treatment, and at the third month. A goniometer was used to measure joint range of motion measurement, a visual analog scale (VAS) for pain, WOMAC Osteoarthritis Index to assess pain and function, Biodex System 3 isokinetic device for knee flexion-extension muscle strength measurement, and ultrasonography to measure femoral cartilage thickness. There was no statistically significant difference in VAS, range of motion, WOMAC, muscle strength, and femoral cartilage thickness measurement between the groups, whether before treatment, after treatment or at the third-month follow-up (p > 0.05). There was a statistically significant decrease in pain intensity, an increase in flexion range of motion, WOMAC, and femoral cartilage thickness in both groups (p < 0.005). A statistically significant increase was found in the average peak torque flexion muscle strength measurements at isokinetic 60°/s angular velocities in the post-treatment and third-month checkup compared to the pre-treatment analysis in both groups (p < 0.05). In conclusion, there was no statistically significant difference between HILT + exercise and placebo laser + exercise observed. However, the exercise program performed under the supervision of a physiotherapist has been shown to be effective in improving all parameters.

Charting Aging Trajectories of Knee Cartilage Thickness for Early Osteoarthritis Risk Prediction: An MRI Study from the Osteoarthritis Initiative Cohort.

Knee osteoarthritis (OA), a prevalent joint disease in the U.S., poses challenges in terms of predicting of its early progression. Although high-resolution knee magnetic resonance imaging (MRI) facilitates more precise OA diagnosis, the heterogeneous and multifactorial aspects of OA pathology remain significant obstacles for prognosis. MRI-based scoring systems, while standardizing OA assessment, are both time-consuming and labor-intensive. Current AI technologies facilitate knee OA risk scoring and progression prediction, but these often focus on the symptomatic phase of OA, bypassing initial-stage OA prediction. Moreover, their reliance on complex algorithms can hinder clinical interpretation. To this end, we make this effort to construct a computationally efficient, easily-interpretable, and state-of-the-art approach aiding in the radiographic OA (rOA) auto-classification and prediction of the incidence and progression, by contrasting an individual's cartilage thickness with a similar demographic in the rOA-free cohort. To better visualize, we have developed the toolset for both prediction and local visualization. A movie demonstrating different subtypes of dynamic changes in local centile scores during rOA progression is available at https://tli3.github.io/KneeOA/. Specifically, we constructed age-BMI-dependent reference charts for knee OA cartilage thickness, based on MRI scans from 957 radiographic OA (rOA)-free individuals from the Osteoarthritis Initiative cohort. Then we extracted local and global centiles by contrasting an individual's cartilage thickness to the rOA-free cohort with a similar age and BMI. Using traditional boosting approaches with our centile-based features, we obtain rOA classification of KLG ≤ 1 versus KLG = 2 (AUC = 0.95, F1 = 0.89), KLG ≤ 1 versus KLG ≥ 2 (AUC = 0.90, F1 = 0.82) and prediction of KLG2 progression (AUC = 0.98, F1 = 0.94), rOA incidence (KLG increasing from < 2 to ≥ 2; AUC = 0.81, F1 = 0.69) and rOA initial transition (KLG from 0 to 1; AUC = 0.64, F1 = 0.65) within a future 48-month period. Such performance in classifying KLG ≥ 2 matches that of deep learning methods in recent literature. Furthermore, its clinical interpretation suggests that cartilage changes, such as thickening in lateral femoral and anterior femoral regions and thinning in lateral tibial regions, may serve as indicators for prediction of rOA incidence and early progression. Meanwhile, cartilage thickening in the posterior medial and posterior lateral femoral regions, coupled with a reduction in the central medial femoral region, may signify initial phases of rOA transition.

Associations between weight change, knee subcutaneous fat and cartilage thickness in overweight and obese individuals: 4-Year data from the osteoarthritis initiative.

OBJECTIVE: To assess (i) the impact of changes in body weight on changes in joint-adjacent subcutaneous fat (SCF) and cartilage thickness over 4 years and (ii) the relation between changes in joint-adjacent SCF and knee cartilage thickness. DESIGN: Individuals from the Osteoarthritis Initiative (total=399) with > 10% weight gain (n=100) and > 10% weight loss (n=100) over 4 years were compared to a matched control cohort with less than 3% change in weight (n=199). 3.0T Magnetic Resonance Imaging (MRI) of the right knee was performed at baseline and after 4 years to quantify joint-adjacent SCF and cartilage thickness. Linear regression models were used to evaluate the associations between the (i) weight change group and 4-year changes in both knee SCF and cartilage thickness, and (ii) 4-year changes in knee SCF and in cartilage thickness. Analyses were adjusted for age, sex, baseline body mass index (BMI), tibial diameter (and weight change group in analysis (ii)). RESULTS: Individuals who lost weight over 4-years had significantly less joint-adjacent SCF (beta range, medial/lateral joint sides: 2.2-4.2 mm, p < 0.001) than controls; individuals who gained weight had significantly greater joint-adjacent SCF than controls (beta range: -1.4 to -3.9 mm, p < 0.001). No statistically significant associations were found between weight change and cartilage thickness change. However, increases in joint-adjacent SCF over 4 years were significantly associated with decreases in cartilage thickness (p = 0.04). CONCLUSIONS: Weight change was associated with joint-adjacent SCF, but not with change in cartilage thickness. However, 4-year increases in joint-adjacent SCF were associated with decreases in cartilage thickness independent of baseline BMI and weight change group.

The relationship between cartilage thickness and muscle thickness or leg length discrepancy in poliomyelitis sequelae.

OBJECTIVES: To evaluate the relationship between cartilage loss and differences in muscle thickness and/or leg length in poliomyelitis sequelae (PMS). Our study is the first to evaluate the relationship between cartilage loss and both muscle atrophy and leg length discrepancy in the same population. METHODS: 37 patients with PMS and 38 healthy controls were included. Talar and distal femoral cartilage thicknesses and gastrocnemius medialis and quadriceps femoris muscle thicknesses were measured via ultrasound. Leg length differences and manual muscle strength were also evaluated. RESULTS: The mean muscle thicknesses and cartilage thicknesses were thinner in the more affected legs than in the less affected legs in the patient group. All of the ultrasonographic measurements were thinner in the less affected legs of the patient group than in the right legs of the control group, except for the knee cartilage thicknesses. While there was a correlation between the cartilage thickness difference and the muscle thickness difference between the less and more affected legs in the patient group, there was no correlation between the cartilage thickness difference and leg length differences. CONCLUSIONS: Patients with PMS are predisposed to osteoarthritis. Talar and knee cartilage thicknesses may be more associated with the muscle thickness than the leg length discrepancy in PMS.

Application of diffuse optical back reflection spectroscopy for determining articular cartilage thickness in a clinical setting.

PURPOSE: Develop a spectroscopic method to assess cartilage thickness during the arthroscopic examination. METHODS: Currently, arthroscopy assesses cartilage damage visually; outcomes are based on the surgeon's subjective experience. Light reflection spectroscopy is a promising method for measuring cartilage thickness based on the absorption of light by the subchondral bone. In the presented study, in vivo diffuse optical back reflection spectroscopic measurements were acquired by gently placing an optical fibre probe on different locations of the articular cartilage of 50 patients during complete knee replacement surgery. The optical fibre probe consists of two optical fibers with a diameter of 1 mm to deliver the light and detect back-reflected light from the cartilage. Centre to centre distance between the source and the detector fibers was 2.4 mm. Actual thicknesses of the articular cartilage samples were measured under microscopy using histopathological staining. RESULTS: Using half of the samples in the patient data, a linear regression model was formed to estimate cartilage thicknesses from the spectroscopic measurements. The regression model was then used to predict the cartilage thickness in the second half of the data. The cartilage thickness was predicted with a mean error of 8.7% if the actual thickness was less than 2.5 mm (R2 = 0.97). CONCLUSION: The outer diameter of the optical fibre probe was 3 mm, which can fit into the arthroscopy channel and can be used to measure the cartilage thickness in real-time during the arthroscopic examination of the articular cartilage.

Which risk factors determine cartilage thickness and composition change in radiographically normal knees? - Data from the Osteoarthritis Initiative.

Objective: Therapy for osteoarthritis ideally aims at preserving structure before radiographic change occurs. This study tests: a) whether longitudinal deterioration in cartilage thickness and composition (transverse relaxation-time T2) are greater in radiographically normal knees "at risk" of incident osteoarthritis than in those without risk factors; and b) which risk factors may be associated with these deteriorations. Design: 755 knees from the Osteoarthritis Initiative were studied; all were bilaterally Kellgren Lawrence grade [KLG] 0 initially, and had magnetic resonance images available at 12- and 48-month follow-up. 678 knees were "at risk", whereas 77 were not (i.e., non-exposed reference). Cartilage thickness and composition change was determined in 16 femorotibial subregions, with deep and superficial T2 being analyzed in a subset (n â€‹= â€‹59/52). Subregion values were used to compute location-independent change scores. Results: In KLG0 knees "at risk", the femorotibial cartilage thinning score (-634 â€‹± â€‹516 â€‹µm) over 3 years exceeded the thickening score by approximately 20%, and was 27% greater (p â€‹< â€‹0.01; Cohen D -0.27) than the thinning score in "non-exposed" knees (-501 â€‹± â€‹319 â€‹µm). Superficial and deep cartilage T2 change, however, did not differ significantly between both groups (p â€‹≥ â€‹0.38). Age, sex, body mass index, knee trauma/surgery history, family history of joint replacement, presence of Heberden's nodes, repetitive knee bending were not significantly associated with cartilage thinning (r2<1%), with only knee pain reaching statistical significance. Conclusions: Knees "at risk" of incident knee OA displayed greater cartilage thinning scores than those "non-exposed". Except for knee pain, the greater cartilage loss was not significantly associated with demographic or clinical risk factors.

Association between knee cartilage thickness determined by magnetic resonance imaging three-dimensional analysis and the International Cartilage Repair Society (ICRS) arthroscopic grade.

BACKGROUND: The purpose of this study was to retrospectively investigate whether the average cartilage thickness calculated by magnetic resonance imaging (MRI) three-dimensional (3D) analysis system was correlated with the International Cartilage Repair Society (ICRS) grade at each subregion, as a representative scoring for arthroscopic evaluation. METHODS: The subjects were 102 patients who underwent arthroscopy for meniscus repair or high tibial osteotomy for medial osteoarthritis of the knee. Cartilage lesions were arthroscopically quantified according to the ICRS grade at each subregion. Fluoroscopy was used to compare the subregions on arthroscopic evaluation with subregions on MRI. The average cartilage thickness at each subregion was also automatically calculated from MRI data using our 3D analysis system. The association between ICRS grade and the average cartilage thickness at 18 subregions in the medial femoral and medial tibial regions was evaluated using Spearman's rank correlation coefficient. RESULTS: Examination of the fluoroscopic images revealed that the posterior subregions in the medial femoral region did not match the position between arthroscopy and MRI; therefore, those three subregions were excluded. In the medial femoral region, the ICRS grade correlated moderately with cartilage thickness at five subregions and weakly at one subregion. In the medial tibial region, the ICRS grade correlated moderately with cartilage thickness at four subregions and weakly at one subregion, but it did not correlate at the other four subregions. CONCLUSION: The average cartilage thickness determined by MRI 3D analysis correlated with arthroscopic grade at 11 of 15 subregions in the medial femoral and tibial regions.

Glenohumeral Cartilage Thickness: Implications in Prosthetic Design and Osteochondral Allograft Transplantation.

OBJECTIVE: A complete understanding of the glenohumeral joint anatomy is crucial for osteochondral allograft (OCA) transplantation and prosthetic design. However, existing data on the cartilage thickness distribution are not consistent. This study aims to describe the cartilage thickness distribution at both the glenoid cavity and humeral head in males and females. DESIGN: Sixteen fresh cadaveric shoulder specimens were dissected and separated to expose the glenoid and humeral head articular surfaces. The glenoid and humeral head were cut into 5-mm coronal sections. Sections were imaged and cartilage thickness was measured at 5 standardized points on each section. Measurements were analyzed based on age, sex, and regional location. RESULTS: For the humeral head, cartilage was thickest centrally (M = 1.77 ± 0.35 mm) and thinnest superiorly and inferiorly (M = 1.42 ± 0.37 mm, 1.42 ± 0.29 mm). At the glenoid cavity, cartilage was thickest in the superior and inferior areas (M = 2.61 ± 0.47 mm, 2.53 ± 0.58 mm) and thinnest centrally (M = 1.69 ± 0.22 mm). Males were found to have thicker cartilage at both the humeral head and glenoid (P = 0.0014, P = 0.0133). CONCLUSIONS: Articular cartilage thickness distribution of the glenoid and humeral head is nonuniform and reciprocal in nature. These results can be used to further inform prosthetic design and OCA transplantation. We noted a significant difference in cartilage thickness between males and females. This suggests that the sex of the patient should be taken into consideration when matching donors for OCA transplantation.

Patterns of variation among baseline femoral and tibial cartilage thickness and clinical features: Data from the osteoarthritis initiative.

Objective: To employ novel methodologies to identify phenotypes in knee OA based on variation among three baseline data blocks: 1) femoral cartilage thickness, 2) tibial cartilage thickness, and 3) participant characteristics and clinical features. Methods: Baseline data were from 3321 Osteoarthritis Initiative (OAI) participants with available cartilage thickness maps (6265 knees) and 77 clinical features. Cartilage maps were obtained from 3D DESS MR images using a deep-learning based segmentation approach and an atlas-based analysis developed by our group. Angle-based Joint and Individual Variation Explained (AJIVE) was used to capture and quantify variation, both shared among multiple data blocks and individual to each block, and to determine statistical significance. Results: Three major modes of variation were shared across the three data blocks. Mode 1 reflected overall thicker cartilage among men, those with higher education, and greater knee forces; Mode 2 showed associations between worsening Kellgren-Lawrence Grade, medial cartilage thinning, and worsening symptoms; and Mode 3 contrasted lateral and medial-predominant cartilage loss associated with BMI and malalignment. Each data block also demonstrated individual, independent modes of variation consistent with the known discordance between symptoms and structure in knee OA and reflecting the importance of features such as physical function, symptoms, and comorbid conditions independent of structural damage. Conclusions: This exploratory analysis, combining the rich OAI dataset with novel methods for determining and visualizing cartilage thickness, reinforces known associations in knee OA while providing insights into the potential for data integration in knee OA phenotyping.

Exploring the differences between radiographic joint space width and MRI cartilage thickness changes using data from the IMI-APPROACH cohort.

OBJECTIVE: Longitudinal weight-bearing radiographic joint space width (JSW) and non-weight-bearing MRI-based cartilage thickness changes often show weak correlations. The current objective was to investigate these correlations, and to explore the influence of different factors that could contribute to longitudinal differences between the two methods. METHODS: The current study included 178 participants with medial osteoarthritis (OA) out of the 297 knee OA participants enrolled in the IMI-APPROACH cohort. Changes over 2 years in medial JSW (ΔJSWmed), minimum JSW (ΔJSWmin), and medial femorotibial cartilage thickness (ΔMFTC) were assessed using linear regression, using measurements from radiographs and MRI acquired at baseline, 6 months, and 1 and 2 years. Pearson R correlations were calculated. The influence of cartilage quality (T2 mapping), meniscal extrusion (MOAKS scoring), potential pain-induced unloading (difference in knee-specific pain scores), and increased loading (BMI) on the correlations was analyzed by dividing participants in groups based on each factor separately, and comparing correlations (slope and strength) between groups using linear regression models. RESULT: Correlations between ΔMFTC and ΔJSWmed and ΔJSWmin were statistically significant (p < 0.004) but weak (R < 0.35). Correlations were significantly different between groups based on cartilage quality and on meniscal extrusion: only patients with the lowest T2 values and with meniscal extrusion showed significant moderate correlations. Pain-induced unloading or BMI-induced loading did not influence correlations. CONCLUSIONS: While the amount of loading does not seem to make a difference, weight-bearing radiographic JSW changes are a better reflection of non-weight-bearing MRI cartilage thickness changes in knees with higher quality cartilage and with meniscal extrusion.

Getting Cartilage Thickness Measurements Right: A Systematic Inter-Method Comparison Using MRI Data from the Osteoarthritis Initiative.

OBJECTIVE: Magnetic resonance imaging is the standard imaging modality to assess articular cartilage. As the imaging surrogate of degenerative joint disease, cartilage thickness is commonly quantified after tissue segmentation. In lack of a standard method, this study systematically compared five methods for automatic cartilage thickness measurements across the knee joint and as a function of region and sub-region: 3D mesh normals (3D-MN), 3D nearest neighbors (3D-NN), 3D ray tracing (3D-RT), 2D centerline normals (2D-CN), and 2D surface normals (2D-SN). DESIGN: Based on the manually segmented femoral and tibial cartilage of 507 human knee joints, mean cartilage thickness was computed for the entire femorotibial joint, 4 joint regions, and 20 subregions using these methods. Inter-method comparisons of mean cartilage thickness and computation times were performed by one-way analysis of variance (ANOVA), Bland-Altman analyses and Lin's concordance correlation coefficient (CCC). RESULTS: Mean inter-method differences in cartilage thickness were significant in nearly all subregions (P < 0.001). By trend, mean differences were smallest between 3D-MN and 2D-SN in most (sub)regions, which is also reflected by highest quantitative inter-method agreement and CCCs. 3D-RT was prone to severe overestimation of up to 2.5 mm. 3D-MN, 3D-NN, and 2D-SN required mean processing times of ≤5.3 s per joint and were thus similarly efficient, whereas the time demand of 2D-CN and 3D-RT was much larger at 133 ± 29 and 351 ± 10 s per joint (P < 0.001). CONCLUSIONS: In automatic cartilage thickness determination, quantification accuracy and computational burden are largely affected by the underlying method. Mesh and surface normals or nearest neighbor searches should be used because they accurately capture variable geometries while being time-efficient.