Osteoarthritis imaging最新文献

{"title":"ADVANCING HIP OSTEOARTHRITIS PREDICTION: INSIGHTS FROM MULTI-MODAL PREDICTIVE MODELING WITH INDIVIDUAL PARTICIPANT DATA OF THE WORLD COACH CONSORTIUM","authors":"M.A. van den Berg , F. Boel , M.M.A. van Buuren , N.S. Riedstra , J. Tang , H. Ahedi , N. Arden , S.M.A. Bierma-Zeinstra , C.G. Boer , F.M. Cicuttini , T.F. Cootes , K.M. Crossley , D.T. Felson , W.P. Gielis , J.J. Heerey , G. Jones , S. Kluzek , N.E. Lane , C. Lindner , J.A. Lynch , R. Agricola","doi":"10.1016/j.ostima.2025.100343","DOIUrl":"10.1016/j.ostima.2025.100343","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Radiographic hip osteoarthritis (RHOA) is a multifactorial disease, making early detection of individuals at risk challenging yet essential for timely intervention and evaluation of preventive strategies. Integrating information on multiple different data modalities using individual participant data from diverse cohorts may enhance predictive modeling in the early stages of RHOA. A focus on model interpretability may further enable the identification of clinically relevant patient subgroups and potential intervention targets.</div></div><div><h3>OBJECTIVE</h3><div>Creating a multi-modal prediction model for improving the performance of RHOA incidence prediction models compared to clinical features alone, and investigating the estimated predictor effects and the generalizability of the models to similar populations.</div></div><div><h3>METHODS</h3><div>We pooled individual participant data from nine prospective cohort studies within the Worldwide Collaboration on OsteoArthritis prediCtion for the Hip (World COACH consortium). All studies included standardized anteroposterior pelvic, long-limb, and/or hip radiographs, assessed for RHOA at baseline and after 4–8 years of follow-up. Incident RHOA was defined as the development of RHOA (grade ≥2) in hips without definite RHOA at baseline (grade <2). The original cohort values of clinical predictors including age, birth-assigned sex, body mass index (BMI), smoking status, diabetes, and hip pain were harmonized into one consistent dataset. X-ray-derived predictors describing the hip morphology, the alpha angle and the lateral center edge angle, were automatically and uniformly determined using automated landmark points placed with Bonefinder®. Additionally, the values of 13 shape modes explaining 85% of the variation from a landmark-based statistical shape model were included. This SSM was built on all baseline RHOA grade <2 hips within World COACH. Risk prediction models were built with generalized linear mixed effects models (GLMM) and Random Forest (RF) models while adjusting for correlations within cohorts and individuals. The discriminative performance (AUC) of different model configurations and the linear versus non-linear approaches were compared through stratified 5-fold cross-validation. For each model configuration, predictions were made with and without cohort labels to assess heterogeneity between cohorts.</div></div><div><h3>RESULTS</h3><div>In total, 29,110 hips without definite RHOA at baseline were included of which 5.0% developed RHOA within 4-8 years (mean age 63.7 (8.6) years, 75.5% female, mean BMI 27.5 (4.7) kg/m<sup>2</sup>). When comparing our uni-modal prediction model using only the clinical predictors (Model 1) to those with X-ray information added (Table 1), we observed a higher discriminative performance for the multi-modal models. Overall, including cohort information significantly improved model performance (p < 0.05), and the RF mode","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100343"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PREVALENCE OF ACETABULAR DYSPLASIA IN 6-YEAR-OLDS IN A GENERAL POPULATION","authors":"N. Hendriks ,&nbsp;F. Boel ,&nbsp;C. Lindner ,&nbsp;F. Rivadeneira ,&nbsp;C.J. Tiderius ,&nbsp;S.M.A. Bierma-Zeinstra ,&nbsp;R. Agricola ,&nbsp;J. Runhaar","doi":"10.1016/j.ostima.2025.100292","DOIUrl":"10.1016/j.ostima.2025.100292","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Acetabular dysplasia (AD) is an important risk factor for early hip OA in adults. In Europe, infants are screened for developmental hip dysplasia. However, AD can also develop during skeletal maturation and these cases often remain unrecognized. Potentially, AD could be influenced prior to the closure of the hip growth plates. Understanding AD development during growth is crucial to prevent future joint degeneration. Different definitions are used to measure AD, depending on the stage of skeletal maturation. More knowledge of the prevalence of AD in the general population is required to understand its development during growth.</div></div><div><h3>OBJECTIVE</h3><div>1) To estimate the prevalence of AD in 6-year-olds from the general population, and 2) to compare different AD definitions in this age group.</div></div><div><h3>METHODS</h3><div>Data from The Generation R Study, a population-based study examining growth and health from fetal life to adulthood, was used. All participants aged 6 years, with high-resolution dual-energy x-ray absorptiometry (DXA) anteroposterior image of the right hip available were included. The hip shape was outlined with 70 landmarks using BoneFinder®. Using these landmarks, the acetabular index (AI), a measurement of acetabular roof inclination, was calculated to assess AD (AI&gt;20°). While AI is commonly used in children, the lateral center-edge angle (LCEA), as indicator for acetabular roof coverage of the femoral head, was also calculated. Mean LCEA and prevalence of AD (LCEA&lt;15°) were compared to measures using AI.</div></div><div><h3>RESULTS</h3><div>In total, 3,270 participants were included with a mean age of 6.2 (SD 0.6) years, and 51% was female. The mean AI was 11.3° (SD 5.0°) and the mean LCEA was 19.5° (SD 5.9°). The distribution for both AD definitions is shown in Figure 1. An AI&gt;20° was found in 124 participants, indicating a AD prevalence of 3.8% (95%CI, 3.1% - 4.5%). Based on the LCEA, the AD prevalence was 21.3% (95%CI, 19.9% - 22.7%).</div></div><div><h3>CONCLUSION</h3><div>The prevalence of AD in 6-year-olds is 3.8%, based on the AI. The LCEA classifies more hips as dysplastic in 6-year-olds. The validity of the LCEA in this age group and clinical relevance of these newly classified dysplastic hips need to be determined. A better understanding of the development of AD is important, as recovery during growth may be feasible and could contribute to the prevention of OA.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100292"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IN VIVO MICRO COMPUTED TOMOGRAPHY IMAGING ALLOWS LONGITUDINAL ASSESSMENT OF MULTI-SCALE CHANGES TO WHOLE JOINT WITH PROGRESSION OF OA","authors":"H. Liu,&nbsp;J.L. Gregory,&nbsp;M.O. Silva,&nbsp;C.E. Davey,&nbsp;K.S. Stok","doi":"10.1016/j.ostima.2025.100300","DOIUrl":"10.1016/j.ostima.2025.100300","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Longitudinal assessment of knee joint structure holds promise for providing invaluable spatial-temporal information on the progression of degenerative musculoskeletal (MSK) diseases involving the knee joint.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;This proof-of-concept study aims to establish a time-lapse &lt;em&gt;in vivo&lt;/em&gt; imaging protocol with high temporal resolution to longitudinally track multi-scale structural changes, including mechanical alteration to whole joint structure, sensitive microstructural changes to subchondral bone, and abnormal bone remodeling activity, in a mouse collagenase-induced osteoarthritis (OA) model.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;Eight male C57BL/10 mice aged nine weeks were recruited and assigned to two longitudinal groups, control (CT) and OA. Of these, four ten-week-old mice assigned to the OA group received intra-articular injection of collagenase on the right knee to destabilize the right tibiofemoral joint. Longitudinal &lt;em&gt;in vivo&lt;/em&gt; micro-computed tomography (microCT) scans were performed one day before collagenase injection and then weekly for eight weeks in total, resulting in nine scans for each animal. &lt;em&gt;In vivo&lt;/em&gt; microCT (Scanco Medical) was performed with a source voltage of 70 kVp, an integration time of 350 &lt;em&gt;ms&lt;/em&gt;, a current of 114 μ&lt;em&gt;A&lt;/em&gt;, and an isotropic nominal resolution of 10.4 μ&lt;em&gt;m&lt;/em&gt; with 1000 projections, with each scanning taking around 30 minutes. Quantitative morphometric analysis (QMA) was performed to measure longitudinal changes to structure of whole joint and subchondral bone, including joint space width (mm), and trabecular thickness (mm). Visualization of dynamic bone remodeling was performed by registering serial microCT scans. Bone resorption rate, BRR (%/day), and bone formation rate, BFR (%/day) were measured to quantify bone remodeling activity. To test the differences between CT and OA group at each time point from week 1 to week 8, a one-way analysis of covariance was used.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;Three weeks post OA-induction, a significantly smaller joint space width was observed in medial osteoarthritic joint (202 μm), when compared to CT joint (228 μm) (p &lt; 0.01). Regarding trabecular thickness, significant differences were observed at multiple time points between CT and OA groups, specifically in the first three weeks at the early stage of OA progression at lateral side (p &lt; 0.01). Representative 3D visualization of bone formation and bone resorption is shown in &lt;strong&gt;Figure 1 A-B&lt;/strong&gt;. Abnormal bone remodeling activities were observed in osteoarthritic femur. When compared to control femur, significantly larger bone resorption rate was observed in the first week post collagenase injection in both the lateral (p &lt; 0.01) and medial femur (p &lt; 0.01), as shown in &lt;strong&gt;Figure 1 C-D&lt;/strong&gt;.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;CONCLUSION&lt;/h3&gt;&lt;div&gt;This proof-of-concept study, for the first time, demonstr","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100300"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A FULLY-AUTOMATED TECHNIQUE FOR KNEE CARTILAGE AND DENUDED BONE AREA MORPHOMETRY IN SEVERE RADIOGRAPHIC KNEE OA – METHOD DEVELOPMENT AND VALIDATION","authors":"W. Wirth ,&nbsp;F. Eckstein","doi":"10.1016/j.ostima.2025.100349","DOIUrl":"10.1016/j.ostima.2025.100349","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Automated cartilage segmentation using convolutional neural networks (CNN) has been shown to provide moderate to high accuracy in comparison with gold-standard manual approaches. It also displays similar sensitivity to longitudinal change and to between-group differences in change as has been reported for manual analysis [1-3]. Denuded areas of subchondral bone (dAB) provide challenges and impair the accuracy of automated cartilage segmentation in knees with severe radiographic OA (KLG 4). The reason is that CNNs are trained to detect cartilage, but encounter “difficulties” to properly segment areas where cartilage is lost entirely. CNNs therefore often segment cartilage cover in some areas of actual full thickness loss or ignore dABs entirely. This was observed to result in an overestimation of cartilage thickness and an underestimation of dABs in knees with severe OA [4].&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;To improve CNN-based automated segmentation in severely osteoarthritic knee cartilage by using an automated post-processing algorithm that relies on a multi-atlas registration for reconstructing the total area of subchondral bone (tAB). We evaluate the agreement, accuracy and longitudinal sensitivity to cartilage change of this new methodology.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;Sagittal DESS and coronal FLASH MRIs were acquired by the Osteoarthritis Initiative (OAI). 2D U-Net models were trained for both MRI protocols using manual cartilage segmentations of knees with radiographic OA (KLG2-4, n training / validation set: 86/18 knees, baseline scans only) or severe radiographic OA (KLG4, n training/ validation set: 29/6 knees. These were trained either from baseline scans only [KLG4&lt;sub&gt;BL&lt;/sub&gt;] or from baseline and follow-up scans [KLG4&lt;sub&gt;BL+FU&lt;/sub&gt;]. The trained models were then applied to the test set comprising 10 KLG4 knees with manual cartilage segmentations from both DESS and FLASH MRI available and to n=125/14 knees with manual cartilage segmentations from either DESS or FLASH MRI available. Automated, registration-based post-processing was applied to reconstruct missing parts of the tAB and to refine the segmentations (Fig. 1), particularly in areas of denuded bone. The agreement and accuracy of automated cartilage analysis were evaluated in the test set for individual cartilages using Dice Similarity coefficients (DSC), correlation analysis, and by determining systematic offsets between manual and automated analysis. The sensitivity to one-year change was assessed using the standardized response mean (SRM) across the entire femorotibial joint in 104/24 (DESS/FLASH) knees with manual baseline and follow-up segmentations.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;The strongest agreement (DSC 0.80±0.07 to 0.89±0.05) and lowest systematic offsets for cartilage thickness (1.2% to 8.5%) were observed for CNNs trained on KLG2-4 rather than KLG4 knees. Similar observations were made for dABs (-40.6% to 3.","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100349"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BEYOND ACETABULAR DYSPLASIA AND PINCER MORPHOLOGY: REFINING HIP OSTEOARTHRITIS RISK ASSESSMENT THROUGH STATISTICAL SHAPE MODELING","authors":"F. Boel ,&nbsp;M.A. van den Berg ,&nbsp;N.S. Riedstra ,&nbsp;M.M.A. van Buuren ,&nbsp;J. Tang ,&nbsp;H. Ahedi ,&nbsp;N. Arden ,&nbsp;S.M.A. Bierma-Zeinstra ,&nbsp;C.G. Boer ,&nbsp;F.M. Cicuttini ,&nbsp;T.F. Cootes ,&nbsp;K.M. Crossley ,&nbsp;D.T. Felson ,&nbsp;W.P. Gielis ,&nbsp;J.J. Heerey ,&nbsp;G. Jones ,&nbsp;S. Kluzek ,&nbsp;N.E. Lane ,&nbsp;C. Lindner ,&nbsp;J.A. Lynch ,&nbsp;R. Agricola","doi":"10.1016/j.ostima.2025.100341","DOIUrl":"10.1016/j.ostima.2025.100341","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Hip morphology has been recognized as an important risk factor for the development of hip OA. In previous studies within the Worldwide Collaboration on OsteoArthritis prediCtion for the Hip consortium (World COACH), both acetabular dysplasia (AD) and pincer morphology–characterized by acetabular under- and overcoverage of the femoral head–were associated with the development of radiographic hip OA (RHOA) within 4-8 years, with an odds ratio (OR) of 1.80 (95% confidence interval (CI) 1.40-2.34) and 1.50 (95% CI 1.05-2.15), respectively. However, we know that not everyone with AD or pincer morphology will develop RHOA. Specific baseline characteristics or variations in hip shape among individuals with AD and pincer morphology may influence their risk of developing RHOA. Statistical shape models (SSM), describing the mean hip shape of a population and a range of independent shape variations, can be utilized to study these variations in hip shape.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;To evaluate whether specific hip shape variations or baseline characteristics within individuals with either AD or pincer morphology are associated with the development of RHOA within 4-8 years.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;We pooled individual participant data from seven prospective cohort studies within the World COACH consortium. Standardized anteroposterior (AP) pelvic radiographs were obtained at baseline and within 4-8 years follow-up. RHOA was scored by KLG or (modified) Croft grade. We harmonized the RHOA scores into “No OA” (KLG/Croft = 0), “doubtful OA” (KLG/Croft = 1), or “definite OA” (KLG/Croft ≥ 2 or total hip replacement). The Wiberg center edge angle (WCEA), measuring the weight-bearing femoral head coverage, and the lateral center edge angle (LCEA), measuring the bony femoral head coverage, were automatically determined using a validated method. Hips were included if they had baseline and follow-up RHOA scores, no RHOA at baseline, and either AD defined by a WCEA ≤ 25° or pincer morphology defined by a LCEA ≥45°. For both populations, an SSM was created of the acetabular roof, posterior wall, femoral head and neck, and teardrop (Fig 1). We analyzed the first 13 shape modes that explained around 90% of total shape variation in the population. The association between each shape mode, sex, baseline age, BMI, diabetes and smoking habits, and the development of RHOA was estimated using univariate generalized linear mixed-effects models. The mixed effects were added to account for the potential clustering within cohorts and participants. The results were expressed as ORs with 95% CIs.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;The AD population consisted of 4,737 hips, of which 2.6% developed incident RHOA (Table 1). Four of the 13 shape modes (Fig 1) were associated with the development of RHOA. Additionally, in hips with AD, females had higher odds of incident RHOA than males (OR 2.85, 95% CI 1.46 – 5.58), and each year inc","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100341"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FINITE ELEMENT MODELING OF IN VIVO HUMAN KNEE BONES USING HR-PQCT: EFFECTS OF BOUNDARY CONDITIONS AND MODEL CONFIGURATION ON PREDICTED STRAIN ENERGY DENSITY","authors":"C.E. Stirling ,&nbsp;S.K. Boyd","doi":"10.1016/j.ostima.2025.100322","DOIUrl":"10.1016/j.ostima.2025.100322","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Bone strength assessment is essential in musculoskeletal research for understanding bone mechanics under loading. High-resolution peripheral quantitative computed tomography (HR-pQCT) and micro-finite element (μFE) analysis provide insights into bone strength. While widely used for the distal radius and tibia, knee joint modeling is more complex due to interactions of bone, cartilage, and soft tissue, and the significantly larger size of the joint. This study aims to develop a knee bone μFE model using HR-pQCT data, focusing on boundary conditions and material properties affecting strain energy density (SED) in the femur and tibia.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;1) Investigate the influence of boundary conditions on stress distribution in knee joint finite element models. 2) Evaluate how the elastic modulus of load transfer material influences bone mechanics.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;HR-pQCT scans of a 35-year-old female with a recent ACL injury were performed on the knee joint in full extension. A boundary material was applied to simulate a transitional layer between the bone and surrounding tissues. The material was generated using a voxel-based approach that mapped to the bone shape by extruding filled slices along the Z-axis (Figure 1). Finite element models with uniaxial compression boundary conditions were generated with two configurations of boundary materials: bone-shaped boundary material, which adapts to the shape of the largest epiphysis of the bone, or rectangular boundary materials, which create a square-shaped material around the minimum/maximum bounds of the epiphysis bone regions. Both types of models were solved with a range of boundary material elastic moduli (2000, 2500, 3000, 3500 MPa) and lengths extending from the bone surface of 1, 3, 5, and 7 mm. The primary output was model SED in subchondral regions of interest (ROI) to test the boundary material’s impact on mechanical predictions.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;Tibial models contained 500 million degrees of freedom, and femur models included 900 million. As load transfer material length increased beyond 1 mm, the mean SED within ROIs initially decreased, then increased beyond 3 mm—suggesting an optimal load transfer material length between 3 mm and 7 mm. SED skewness and kurtosis increased with material length, indicating more heterogeneous stress distributions. Longer segments (e.g., 5-7 mm) substantially increased computational cost, highlighting a trade-off between the extent of material used for load transfer and simulation efficiency. The bone-shaped boundary material method was more computationally efficient and produced less variability as material length increased. As the elastic modulus of the load transfer material increased, average SED values also increased, particularly with longer PMMA segments.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;CONCLUSION&lt;/h3&gt;&lt;div&gt;We found that load transfer material length and elastic modulus si","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100322"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COMPARATIVE STUDY: QDESS VERSUS RAFO-4 PERFORMANCE IN 5-MINUTE, SIMULTANEOUS, RELIABLE 3D T2 MAPPING AND MORPHOLOGICAL MR IMAGING","authors":"K. Balaji ,&nbsp;P.M. Vicente ,&nbsp;S. Kukran ,&nbsp;M. Mendoza ,&nbsp;A.A. Bharath ,&nbsp;P.J. Lally ,&nbsp;N.K. Bangerter","doi":"10.1016/j.ostima.2025.100336","DOIUrl":"10.1016/j.ostima.2025.100336","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Cartilage T&lt;sub&gt;2&lt;/sub&gt; is a non-invasive MRI biomarker for KOA as it is sensitive to the underlying collagen hydration/organization. Cartilage microstructural changes seen in early KOA result in elevated T&lt;sub&gt;2&lt;/sub&gt;. Cartilage T&lt;sub&gt;2&lt;/sub&gt; maps could be used in DMOAD clinical trials.&lt;/div&gt;&lt;div&gt;Quantitative DESS (qDESS) simultaneously acquires 3D, morphological whole knee images and quantitative T&lt;sub&gt;2&lt;/sub&gt; maps in ∼5 minutes. Recently, we developed RaFo-4 balanced Steady State Free Precession (RaFo-4 bSSFP) that also has the potential to simultaneously acquire 3D, morphological whole knee images with high SNR efficiency and quantitative cartilage T&lt;sub&gt;2&lt;/sub&gt; maps in ∼5 minutes. RaFo-4 uses machine learning (Random Forest) to estimate voxel-level cartilage T&lt;sub&gt;2&lt;/sub&gt; from bSSFP images. In this preliminary study, we compared qDESS and RaFo-4 bSSFP in morphological imaging and cartilage T&lt;sub&gt;2&lt;/sub&gt; mapping.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;1) Which technique (qDESS or RaFo-4 bSSFP) has better test-retest repeatability of cartilage T&lt;sub&gt;2&lt;/sub&gt; maps? 2) Which technique gives higher quality morphological images, as quantified using SNR of femoral, patellar, and tibial cartilage and CNR of cartilage-muscle, cartilage-synovial fluid, and synovial fluid-muscle?&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;10 healthy volunteers (HVs: 7F, 3M, 20-40 age range) were scanned on a 3T Siemens Verio (Erlangen, Germany) using an 8-channel knee coil with ethics approval. Test-retest 3D (80 slices) sagittal knee images were acquired using qDESS (water excitation, 20° flip angle, 21.77 ms TR, 6 ms TE, 364 Hz/Px receiver bandwidth, 0 dummy scans per volume) and bSSFP (water excitation, 22° flip angle, 8.6 ms TR, 4.3 ms TE, 364 Hz/Px receiver bandwidth, 0 dummy scans per volume) for both knees of each HV with knee repositioning. qDESS and bSSFP were resolution- (0.4 × 0.4 × 1.5 mm&lt;sup&gt;3&lt;/sup&gt; voxel volume, 150 × 150 × 120 mm&lt;sup&gt;3&lt;/sup&gt; field of view) and scan time-matched (5:05 min. for qDESS and 5:04 min for bSSFP). 4 separate phase-cycled bSSFP images were acquired with phase cycling increments [0°, 90°, 180°, 270°]. Parallel imaging was used (GRAPPA R=2 for bSSFP and qDESS with 24 reference lines; 6/8&lt;sup&gt;th&lt;/sup&gt; phase/slice partial Fourier for bSSFP). Cartilage in qDESS images was segmented using DOSMA and those segmentation masks were used on the bSSFP images. Test-retest repeatability was calculated using the ICC and coefficient of variation (CoV) after removing outlier T&lt;sub&gt;2&lt;/sub&gt; estimates (T&lt;sub&gt;2&lt;/sub&gt; &lt; 20 ms, T&lt;sub&gt;2&lt;/sub&gt; &gt; 90 ms). The percentage of outlier estimates was also calculated. For quantitatively evaluating morphological image quality, SNR and CNR were calculated from the Root Sum of Squares (RSOS) of the two qDESS echos and four phase-cycled bSSFP images.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;1) In Fig1, RaFo-4 preserves cartilage T&lt;sub&gt;2&lt;/sub&gt; spatial variations seen in qDESS T&lt;sub&gt;2&lt;/sub&gt; ma","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100336"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"THE EFFECT OF RECONSTRUCTION KERNEL AND MONOCHROMATIC ENERGY PAIRS USED IN DUAL ENERGY CT IMAGING OF THE PROXIMAL HUMERUS","authors":"S. Quayyum ,&nbsp;C.R. Dickerson ,&nbsp;M.R. Maly ,&nbsp;G.S. Athwal ,&nbsp;N.K. Knowles","doi":"10.1016/j.ostima.2025.100309","DOIUrl":"10.1016/j.ostima.2025.100309","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Dual-energy computed tomography (DECT) allows for more accurate volumetric vBMD by accounting for marrow alterations with aging, disease and acute injuries. Tissue alterations, including vBMD, have been identified as potential biomarkers for early shoulder OA. Reconstruction kernel and energy pair images used in DECT alter vBMD and resulting estimated bone stiffness in image-based finite element models (FEMs). Prior to clinical investigation, the effect of imaging parameters must be understood.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;This study investigated how varying reconstruction kernel, and DECT monochromatic energy pair combinations influenced 1) vBMD, and 2) FEM estimated stiffness in the proximal humerus of cadaveric models.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;Cadaveric specimens (n = 7; 14 shoulders) were scanned bilaterally using DECT (GE Revolution HD GSI) with a K&lt;sub&gt;2&lt;/sub&gt;HPO&lt;sub&gt;4&lt;/sub&gt; calibration phantom. DECT images were reconstructed using bone sharpening (BONE) and standard (STD) kernels. Simulated monochromatic images were created at 40, 90, and 140 keV using the manufacturers GSI software and combined into energy pairs (40/90, 90/140, 40/140 keV). Images were processed with custom Python scripts and 3D Slicer software to segment and extract vBMD values in proximal humeral head and diaphysis locations. Image-based FEMs were used to compare estimated bone stiffness across models generated from each image. Results were compared using a two-way RM-ANOVA.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;The highest vBMD values occurred in the humeral shaft diaphysis across all kernel and energy pair combinations (Table 1). There were significant differences in vBMD across energy pairs and kernels within the diaphysis region, with the greatest vBMD occurring with the 90/140 keV energy pair. No significant differences in mean vBMD values across energy pair combinations occurred for the anatomic neck. Increased vBMD input to FEMs resulted in similar trends, with the highest FEM stiffness in the diaphysis region, and those generated from 90/140 keV DECT images (Table 2). Significant differences remained in the diaphysis with no difference in the anatomic neck FEMs.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;CONCLUSION&lt;/h3&gt;&lt;div&gt;Higher vBMD values in the diaphysis reflect its cortical bone density, with significant differences by kernel and energy pair. Lower vBMD values in the anatomic neck, a trabecular-rich region, occur partially due to the heterogeneous composition, with minimal cortical bone. The BONE kernel at higher energy pairs (e.g., 90/140 keV) improved contrast but resulted in the greatest vBMD, a trend that was not observed with the other two energy pairs. Trends in vBMD persisted in FEMs indicating choice of energy pair combination has a large effect on vBMD and FEM stiffness in regions of high cortical bone, with the 90/140 keV energy pair, but little effect on trabecular regions within the proximal humerus of the cadavers ","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100309"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"REGIONAL DEPTH-SPECIFIC SUBCHONDRAL BONE DENSITY IN OA AND NORMAL DISTAL FEMORA: PRECISION AND PRELIMINARY COMPARISONS","authors":"J.D. Johnston ,&nbsp;A.E. Sacher ,&nbsp;C.E. McLennan ,&nbsp;J.A. Lynch ,&nbsp;T. Neogi ,&nbsp;D.J. Hunter ,&nbsp;D.R. Wilson ,&nbsp;S.A. Kontulainen","doi":"10.1016/j.ostima.2025.100294","DOIUrl":"10.1016/j.ostima.2025.100294","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>The exact role of altered subchondral bone in OA pathogenesis and pain is unclear. Clinical quantitative CT (QCT) combined with depth-specific image processing has been previously used to study subchondral bone mineral density (BMD) at the proximal tibia and patella. Limited depth-specific QCT research has been completed at the OA distal femur.</div></div><div><h3>OBJECTIVES</h3><div>To 1) assess the short-term precision of automated, regional, depth-specific subchondral BMD measures at the distal femur in individuals with and without OA; and 2) determine whether regional and focal BMD metrics were able to discriminate differences in subchondral bone density patterns between normal and OA distal femora.</div></div><div><h3>METHODS</h3><div>Fourteen participants (3M:11F; mean age: 49.9 (SD: 11.9) years) were recruited and classified as normal (n=7) or OA (n=7). Each participant was scanned three times over two days using clinical QCT. Two BMD assessments were evaluated at the distal femur: mean regional density and peak focal density. BMD measures were assessed across three depths (0-2.5, 2.5-5, 5-7.5 mm) and six sub-regions of the distal femur (medial/lateral, anterior/central/posterior), as per the MOAKS approach (Fig.1). We assessed precision using root mean square coefficients of variation (CV%_RMS). To explore potential differences between OA and normal distal femora, we performed parametric t-tests and non-parametric Mann-Whitney statistical analyses and also determined Cohen’s d effect sizes, with an absolute d &gt; 0.8 considered clinically significant.</div></div><div><h3>RESULTS</h3><div>CV%_RMS ranged from 1.6% to 3.6% (average: 2.2%) for measures of regional BMD while CV%_RMS ranged from 1.6% to 6.9% (average: 2.7%) for measures of focal BMD. Statistical comparisons indicated lower BMD in OA distal femoral in the medial-anterior region at depths of 2.5-5 mm (regional: -17%; focal: -19%) and 5-7.5 mm (regional: -21%; focal: -25%) (Fig. 2). All other BMD measures were similar between normal and OA distal femora (p &gt; 0.05). Cohen's d effect sizes ranged from -1.7 to 0.76.</div></div><div><h3>CONCLUSION</h3><div>This automated technique offers precise measures of subchondral BMD at the distal femur. This approach has potential to quantify and distinguish OA-related alterations in subchondral BMD at the distal femur.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100294"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AUTOMATIC EXTRACTION OF KNEE ALIGNMENT AND MORPHOLOGY MEASURES FROM 3D MODELS IN A YOUNG-ADOLESCENT OPEN-POPULATIONS COHORT STUDY","authors":"R. van Paassen ,&nbsp;N. Tumer ,&nbsp;J. Hirvasniemi ,&nbsp;E.M. Macri ,&nbsp;I. Bosch ,&nbsp;E. Langius ,&nbsp;A. Roos ,&nbsp;T.M. Piscaer ,&nbsp;A.A. Zadpoor ,&nbsp;S.M.A. Bierma-Zeinstra ,&nbsp;E.H.G. Oei ,&nbsp;M. van Middelkoop","doi":"10.1016/j.ostima.2025.100327","DOIUrl":"10.1016/j.ostima.2025.100327","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Proper knee alignment is crucial for knee joint function. Little is known about knee alignment and morphology during growth; most research and current normal values were determined in adults. Imaging-based landmarks have to be identified to determine knee alignment parameters such as bisect offset or patellar translation. Currently, these landmarks are often determined manually on 2D image slices, which is time-consuming and can lead to interrater variability. Automatic extraction of these landmarks in 3D could help overcome these inconsistencies.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;To determine the concurrent validity of automatically extracted alignment parameters and morphology measures from two previously developed 3D statistical shape models (SSMs) - one for the patella and one for the distal femur- and to establish normative values and evaluate sex-based differences in these parameters among a young adolescent population.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;We included data from 1912 participants (aged 14.1 ± 0.67) who underwent knee MRI in the Generation R study, a large prospective population cohort study that follows children from fetal life until adulthood. MRI was performed using a 3.0T MRI (Discovery MR750w, GE Healthcare, Milwaukee, WI, USA), with both knees fully extended, using a water excitation Gradient Recalled Acquisition in Steady State sequence. Using a combined multi-atlas and appearance-based segmentation technique, 3638 patellae and 3355 femora were segmented from MRI scans. The 3D reconstructed bone samples derived from these segmentations were used to create two separate SSMs: one for the patella and one for the distal femur. Six patella and ten femur landmarks were annotated on the mean patella and femur shapes. Using the automatically established correspondences across bone samples during the SSMs generation, the landmarks identified on the mean bone shapes were transferred to the individual bone samples used to build the SSMs. One researcher manually annotated 30 randomly selected MRIs twice (15 boys and 15 girls) to determine the reliability of landmarks automatically extracted from the SSMs. Using these landmarks, we calculated 17 alignment parameters and morphology measurements: bisect offset; epicondylar width; femoral notch depth; femoral notch width; medial and lateral inclination angles; lateral patellar tilt; medial and lateral anterior-posterior (AP) length to epicondylar width ratio; patellar lateral translation; patellar length, thickness, and width; patellar tilt angle; sulcus angle; sulcus depth; and trochlear angle. Inter-method concurrent validity between the manually annotated parameters (mean of the two annotations) and automatically calculated parameters was determined using the intraclass correlation coefficient (ICC) for absolute agreement, calculated with a two-way mixed-effects model for single rater measurements. For alignment and morphology parameters with an ","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100327"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}