Osteoarthritis imaging最新文献

{"title":"CAN REGISTRATION-BASED LOCATION-INDEPENDENT MEASUREMENT INCREASE THE SENSITIVITY TO BETWEEN-GROUP DIFFERENCES IN LONGITUDINAL CHANGE OF LAMINAR CARTILAGE T2?","authors":"W. Wirth ,&nbsp;F. Eckstein","doi":"10.1016/j.ostima.2025.100331","DOIUrl":"10.1016/j.ostima.2025.100331","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Location-independent measurements of cartilage thinning and thickening were shown to be more sensitive to differences in longitudinal change between groups than location-based measures [1,2]. They remove the link between the magnitude and direction of the change and its location, and hence are sensitive to local changes in the joint, independent of where they occur. Location-independent measures of T2 lengthening and shortening computed from 16 femorotibial subregions have been previously applied to a model of early OA. The model compared 3y T2 change in KLG 0 knees with contralateral (CL) joint space narrowing (JSN) vs that in KLG 0 knees with CL KLG 0 (controls) [3]. In this model, location-independent measures were found to provide similar discrimination between these two groups as location-based measures. However, location-independent measures obtained across all individual voxels in the joint (instead of subregions) have been previously suggested to provide more detailed insights into OA-related cartilage thickness changes [4], but no study previously evaluated the sensitivity of such voxel-based shortening and lengthening scores to differences in change of laminar T2.</div></div><div><h3>OBJECTIVE</h3><div>To compare the sensitivity of voxel-based location-independent lengthening and shortening T2 scores to between-group differences in longitudinal change vs. the previously established technique of subregion-based location-independent and location-based measures in the above early OA model.</div></div><div><h3>METHODS</h3><div>Multi-echo spin-echo (MESE) MRIs were acquired at year 1 and 4 in the OAI (3T Trio, Siemens). We studied 39 KLG 0 knees with CL JSN, and 39 matched controls (criteria: same sex pain frequency, similar age (±5y) and BMI (±5kg/m²)) with bilateral KLG 0 [2]. Segmentation of the 4 femorotibial cartilages (medial/lateral tibia: MT/LT and central medial/lateral femoral condyle: cMF/cLF) was performed manually by experienced readers. Laminar T2 was computed for each segmented cartilage voxel and classified as deep or superficial, based on the distance to the cartilage surfaces. Location-based and subregion-based location-independent measures were obtained as described previously [2]. Voxel-based location-independent changes in laminar T2 were derived, summarizing the negative/positive changes across all voxels, for each of the femorotibial cartilages using the voxel-based approach (Fig. 1) These were then summarized across the entire femorotibial joint (FTJ). Location-based, subregion-based location independent, and voxel-based location-independent laminar T2 change was compared between the CL JSN vs. control knees using Cohen's D as a measure of effect size with 95% confidence intervals obtained using boot-strapping.</div></div><div><h3>RESULTS</h3><div>In the deep layer, location-based longitudinal change in femorotibial T2 revealed a Cohen’s D between both groups of 0.37 [0.04, 0.","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100331"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523433","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 INFLUENCE OF WEIGHT-BEARING AND FLEXION ON 3D JOINT SPACE WIDTH IN KNEE OSTEOARTHRITIS","authors":"F.F.J. Simonis ,&nbsp;W.M. Brink ,&nbsp;F.F. Schröder ,&nbsp;W.C. Verra ,&nbsp;T.D. Turmezei ,&nbsp;S.C. Mastbergen ,&nbsp;M.P. Jansen","doi":"10.1016/j.ostima.2025.100320","DOIUrl":"10.1016/j.ostima.2025.100320","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>In knee OA, radiographic JSW is used as a surrogate for MRI-measured cartilage thickness, though they often do not correlate well. Variations in positioning between radiography (weight-bearing semi-flexion) and MRI (non-weight-bearing extension) may contribute to discrepancies.</div></div><div><h3>OBJECTIVE</h3><div>This study aimed to evaluate differences in 3D JSW and cartilage thickness distribution between these positions in knee OA patients.</div></div><div><h3>METHODS</h3><div>21 symptomatic knee OA patients (KLG 2/3) were included. Exclusion criteria included prior knee surgery, MRI ineligibility, inability to stand unassisted for 15 minutes, or knee width &gt; 15 cm (knee coil limit). A knee MRI protocol was performed using a 0.25T weight-bearing MRI system (G-scan Brio, Esaote). A coronal 3D dual-echo SSFP sequence (SHARC) was acquired to obtain images with an isotropic resolution of 0.66mm in both extended and flexed knee positions under weight-bearing conditions by rotating the system to 81°. Both scans were repeated under non-weight-bearing conditions by rotating the system to a horizontal position (0°). Knee flexion angles were measured, and the femur and tibia bones were segmented in 3D Slicer. 3D models were exported to Stradview to measure the tibia-femur distance at each vertex as a measure of JSW. The models and data were registered to canonical surfaces in wxRegSurf and further analyzed in MATLAB using the Surfstat package for statistical parametric mapping to derive p-values corrected for multiple vertex-wise comparisons.</div></div><div><h3>RESULTS</h3><div>The average knee angles of the 21 patients were 7.4±3.7° (extended) and 19.1±5.5° (flexed). The average JSW ranged from 3.1 mm to 14.7 mm across patients (Figure 1). A significantly smaller JSW for weight-bearing vs non-weight-bearing conditions, particularly in the outer medial and posterior lateral tibia for extended positions, and in the posterior medial tibia for flexed positions, was seen (Figure 2). Flexion increased the JSW in the anterior tibia and decreased it in the posterior tibia, particularly laterally in weight-bearing positions.</div></div><div><h3>CONCLUSION</h3><div>JSW distribution in knee OA patients varies significantly depending on both weight-bearing and knee flexion angle, and radiographic JSW measurements may not accurately reflect the joint space in non-weight-bearing positions, such as those used in MRI, especially in the lateral compartment. Currently ongoing cartilage analyses will indicate to which extent these JSW variations are attributable to changes in cartilage thickness or meniscal positioning.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100320"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523455","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":"BASELINE C-SCORE ON WEIGHT-BEARING CT PREDICTS 2-YEAR WORSENING OF KNEE PAIN IN WOMEN","authors":"S. Li ,&nbsp;N.A. Segal ,&nbsp;I. Tolstykh ,&nbsp;M.C. Nevitt ,&nbsp;T.D. Turmezei","doi":"10.1016/j.ostima.2025.100345","DOIUrl":"10.1016/j.ostima.2025.100345","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>The B-score is a statistical score derived from non-weight-bearing MRI to assess femoral bone shape and its relationship with knee OA. However, CT scans may offer a more reliable and robust evaluations of bone shape, as they not only provide clearer differentiation between bone and soft tissue but also eliminate distortion artefact that can occur with MRI.</div></div><div><h3>OBJECTIVE</h3><div>To investigate a new “C-score” for femoral bone shape derived from CT as a predictive imaging biomarker for worsening knee pain in men and women with or at risk for knee osteoarthritis.</div></div><div><h3>METHODS</h3><div>This study included 649 knees from 389 participants (219 women) with a mean±SD age of 63.8±9.6 years and BMI of 28.5±5.0 kg/m². C-scores were calculated from baseline weight-bearing CT (WBCT) imaging of the knee joint: 0.37 mm voxels, FOV 30 × 20 cm, 120 kVp, 5.0 mA on a LineUp scanner, Curvebeam LLC, Warrington, PA. All distal femurs were segmented using Stradview to produce a surface mesh. A canonical distal femur mesh was registered using wxRegSurf to each individual femur to build the study population shape model. Each knee's C-score was derived from the distance along the vector for femur shape between the average KL0/1 and KL2/3/4 shapes from the study population using a custom script in MATLAB. A single unit of the C-score was standardized as 1SD along this vector for the KL0/1 population (Figure 1). Generalized estimating equations adjusted for age, sex, BMI and presence of up to 2 knees per participant were used to assess associations between baseline C-score and 2-year minimally clinically important worsening (MCIW) of the Western Ontario McMaster’s University Osteoarthritis Scale (WOMAC) pain subscore (2 points). MCIW is defined as the smallest difference on a pain scale that either patients perceive as worsening or requires change in treatment.</div></div><div><h3>RESULTS</h3><div>186 knees demonstrated pain worsening (32.71% women and 23.2% men). 98 knees had MCIW of pain (19.0% women and 9.8% men). C-scores ranged from -2.64 to +3.34 in women and -3.96 to +2.83 in men, with mean±SD values of 0.16±1.06 and -0.52±1.01 respectively (p-value for difference between sexes p=0.0003). Women without MCIW pain had a mean C-score of +0.31, while those with worsening pain had a mean C-score of +0.72. Men had mean C-scores of -0.03 and -0.01, respectively. In fully adjusted models, baseline C-score predicted 2-year MCIW pain (OR: 1.27, 95% CI: 1.00–1.62, p=0.047). In sex-stratified models, the odds ratios for 2-year MCIW pain in women and men were 1.49 (95% CI: 1.10–2.01, p=0.0159) and 1.01 (95% CI: 0.70–1.47, p=0.95), respectively.</div></div><div><h3>CONCLUSION</h3><div>Higher C-scores in women were significantly associated with worsening knee pain over 2 years, suggesting the C-score as a potential predictive biomarker for knee pain progression.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100345"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523606","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":"EVALUATION OF DIFFERENT METHODS OF AUTOMATED 3-D JOINT SPACE MAPPING FROM WEIGHT BEARING CT SUGGESTS A TIBIAL MESH-TO-MESH APPROACH IS MOST SENSITIVE","authors":"N.A. Segal ,&nbsp;T. Whitmarsh ,&nbsp;N.H. Degala ,&nbsp;J.A. Lynch ,&nbsp;T.D. Turmezei","doi":"10.1016/j.ostima.2025.100338","DOIUrl":"10.1016/j.ostima.2025.100338","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Weight bearing CT (WBCT) has the distinct advantage over radiography of being able to provide 3-D imaging of the knee joint while standing. It is also more practicable and better at depicting mineralized joint structures than MRI. Several different approaches to 3-D JSW measurement have been developed, but their repeatability has not been directly compared.</div></div><div><h3>OBJECTIVE</h3><div>To compare the test-retest repeatability of three different methods of 3-D joint space mapping (JSM) of the tibiofemoral compartment from WBCT imaging data.</div></div><div><h3>METHODS</h3><div>14 individuals recruited and consented at the University of Kansas Medical Center had baseline and follow-up WBCT imaging suitable for analysis. Participant demographics were: mean ± SD age 61.3 ± 8.4 years, BMI 30.7 ± 4.3 kg/m² and male:female ratio 8:6. All scanning was performed on the same XFI WBCT scanner (Planmed Oy, Helsinki, Finland) with the mean ± SD interval between baseline and follow-up attendances 14.9 ± 8.1 days. A Synaflexer^TM device was used to standardize knee positioning during scanning. Imaging acquisition parameters were 96 kV tube voltage, 51.4 mA tube current, 3.5 s exposure time. A standard bone algorithm was applied for reconstruction with 0.3 mm isotropic voxels and a 21 cm vertical scan range. Both knees were included in all analyses with SD adjustments made for multiple observations from the same individual. Participant ID and scan sequence were anonymized prior to analyses. An algorithm based on U-net was implemented in C++ using LibTorch and integrated into ScanXM software for automatic segmentation of the femur and tibia from all knees. Three different JSM techniques were applied: (1) femur-to-tibia deconvolution in which the femur was the base (performed in Stradview); (2) tibia-to-femur deconvolution in which the same was done but from the tibia; and (3) tibia-to-femur mesh-to-mesh distance using a custom MATLAB script. Results from each technique were registered using wxRegSurf and displayed on their average halfway joint space mesh (i.e. the middle plane of the joint space) using custom MATLAB scripts. Bland Altman descriptive statistics were calculated as 3-D bias (follow-up minus baseline) and limit of agreement (LOA) maps for all knees. Summary statistics also included root mean square coefficient of variation (RMSCV) and LOA as a % of the mean.</div></div><div><h3>RESULTS</h3><div>3-D bias and LOA maps for all knees are displayed on the halfway joint space patches as if viewing the right knee from the inferior aspect (Figure 1). Both deconvolution techniques showed similar noise patterns of bias around a zero value, while the mesh-to-mesh technique suggested systematically wider anterior and narrower posterior JSW at follow-up, but this was of sub-millimeter magnitude. Both deconvolution techniques also showed a pattern of worsening LOA towards the joint space patch margins,","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100338"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523922","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":"AUTOMATING IMAGING BIOMARKER ANALYSIS FOR KNEE OSTEOARTHRITIS USING AN OPEN-SOURCE MRI-BASED DEEP LEARNING PIPELINE","authors":"A. Goyal ,&nbsp;F. Belibi ,&nbsp;V. Sahani ,&nbsp;R. Pedersen ,&nbsp;Y. Vainberg ,&nbsp;A. Williams ,&nbsp;C. Chu ,&nbsp;B. Haddock ,&nbsp;G. Gold ,&nbsp;A.S. Chaudhari ,&nbsp;F. Kogan ,&nbsp;A.A. Gatti","doi":"10.1016/j.ostima.2025.100288","DOIUrl":"10.1016/j.ostima.2025.100288","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Quantitative MRI and [¹⁸F]NaF PET enable assessment of cartilage composition, bone shape, and subchondral bone metabolism in knee OA. Current workflows rely on manual segmentation that is time-consuming and subject to inter- and intra-reader variability. Furthermore, computing quantitative metrics requires considerable time and expertise. An open-source, automated, deep learning (DL) pipeline with standardized biomarker extraction has the potential to enhance reproducibility and make large-scale analysis accessible to clinical research communities, including non-technical users.</div></div><div><h3>OBJECTIVE</h3><div>Develop and validate an automated DL-based pipeline for comprehensive MRI-based segmentation and quantitative analysis of multiple knee tissues from multi-modal MR and PET images.</div></div><div><h3>METHODS</h3><div>We developed and open-sourced a comprehensive segmentation and analysis pipeline. A 2D U-Net was trained to segment 9 tissues using a dataset of 347 DESS and qDESS images: 3 bones (femur, tibia, patella), 4 cartilage regions (femoral, medial and lateral tibial, patellar), and 2 menisci (medial and lateral). Subchondral bone masks and femoral cartilage subregions were fitted automatically. Quantitative imaging biomarkers were computed as follows: cartilage T2 was computed analytically from qDESS scans; cartilage thickness was computed as the 3D Euclidean thickness of cartilage overlying the bone surface; meniscal volume was calculated as the product of voxel count and voxel volume; OA bone shape (BScore) was derived using a neural shape model; PET-derived subchondral bone metabolism was computed as regional SUVmean/max, and kinetic modeling via Hawkin’s method was used to extract KiNLR (bone mineralization rate) and K1 (perfusion to subchondral bone). To evaluate the pipeline, 20 unilateral qDESS and [¹⁸F]NaF PET knee scans (10 symptomatic OA, 10 controls) were analyzed by the automated pipeline, and two manual annotators. Manual and automated segmentations were compared using the Dice Similarity Coefficient (DSC) and average symmetric surface distance (ASSD). Biomarkers were compared using ICC and normalized mean RMSE (NRMSE).</div></div><div><h3>RESULTS</h3><div>All automated segmentations had good to excellent overlap measured using DSC (bone: 0.95-0.98; cartilage: 0.84-0.91; menisci: 0.85-0.89) and small surface errors (bone: 0.13-0.32 mm; cartilage: 0.11-0.21 mm; menisci: 0.17-0.30 mm). Notably, automated segmentations had better DSC and ASSD than the inter-rater comparison (Fig. 2). With the exception of cartilage thickness and patellar cartilage whole T2 values, all quantitative metrics showed excellent agreement with ICC &gt;0.96 and NRMSE &lt;0.1, comparable to inter-rater comparison. Bone metrics (BScore, SUV, PET kinetics) had ICC &gt;0.96. Cartilage metrics had more variability, with the best reproducibility for whole cartilage T2 (ICC 0.89-0.98, NRMSE 0.01-0.04), then superficia","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100288"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524003","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":"DO RATES OF FEMOROTIBIAL CARTILAGE LOSS IN KELLGREN-LAWRENCE 2 AND 3 KNEES DIFFER BETWEEN THOSE WITH MILD-MODERATE VS. SEVERE PATELLOFEMORAL STRUCTURAL DAMAGE?","authors":"F.W. Roemer ,&nbsp;M.P. Jansen ,&nbsp;S. Maschek ,&nbsp;S. Mastbergen ,&nbsp;A. Wisser ,&nbsp;H.H. Weinans ,&nbsp;F.J. Blanco ,&nbsp;F. Berenbaum ,&nbsp;M. Kloppenburg ,&nbsp;I.K. Haugen ,&nbsp;D.J. Hunter ,&nbsp;A. Guermazi ,&nbsp;W. Wirth","doi":"10.1016/j.ostima.2025.100311","DOIUrl":"10.1016/j.ostima.2025.100311","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Knees with radiographic disease severity of Kellgren-Lawrence (KL) 2 and 3 are commonly included in disease-modifying (DMOAD) clinical trials of knee osteoarthritis (OA). In an eligibility context, semi-quantitative (sq) MRI assessment has been used to define structural disease severity, rule out diagnoses of exclusion, and possibly define a structural phenotype. The KL system focuses on the femorotibial joint (FTJ) only, with MRI stratification being commonly limited to the FTJ. It is unclear whether sq MRI of the patellofemoral joint (PFJ) should be included for eligibility assessment.</div></div><div><h3>OBJECTIVE</h3><div>The aim was to assess whether rates of quantitative femorotibial (FT) cartilage loss are increased for knees with semiquantitatively (sq)-defined severe patellofemoral (PF) cartilage damage and/or large bone marrow lesions (BMLs) vs. those without over a period of 24 months.</div></div><div><h3>METHODS</h3><div>626 knees with Kellgren-Lawrence 2 and 3 from the FNIH and IMI-APPROACH studies were included. MRI assessment was performed using the MRI Osteoarthritis Knee Score (MOAKS) instrument. Medial FT quantitative cartilage thickness loss was derived from baseline and 24-month manual segmentations and was compared between knees with severe vs. mild-moderate PF cartilage damage and between knees with vs. without large PF BMLs. Between-group comparisons were performed using analysis of variance (ANOVA) and were stratified by baseline medial FT cartilage damage severity (defined as mild, moderate, or severe).</div></div><div><h3>RESULTS</h3><div>410 (65%) knees were categorized as mild, 92 (15%) as moderate, and 124 (20%) as severe medial FT cartilage damage. For almost all categories of FT cartilage damage, the difference in quantitative medial FT cartilage loss was not statistically significant (<strong>Table 1</strong>). Only for the category of knees with moderate medial FT cartilage damage, statistically higher rates of quantitative medial FT cartilage loss were observed for those with large PF BMLs compared to those without (-0.245 ± 0.304 mm vs. -0.134 ± 0.218 mm) (<strong>Table 2</strong>).</div></div><div><h3>CONCLUSION</h3><div>For the large majority of sq-defined FT cartilage damage categories, no statistically significant differences in FT rates of quantitative cartilage loss were detected. Screening for PF cartilage damage and BMLs does not appear to be required in a disease-modifying OA drug trial.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100311"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524025","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":"AGREEMENT BETWEEN IN VIVO AND EX VIVO PHOTON-COUNTING CT MEASURES OF SUBCHONDRAL BONE FEATURES IN PATIENTS WITH KNEE OSTEOARTHRITIS","authors":"C.T. Nielsen ,&nbsp;M. Boesen ,&nbsp;M. Henriksen ,&nbsp;J.U. Nybing ,&nbsp;S.W. Bardenfleth ,&nbsp;C.K. Rasmussen ,&nbsp;M.W. Brejnebøl ,&nbsp;A.S. Poulsen ,&nbsp;S.M. Aljuboori ,&nbsp;K.I. Bunyoz ,&nbsp;S. Overgaard ,&nbsp;A. Troelsen ,&nbsp;H. Bliddal ,&nbsp;H. Gudbergsen ,&nbsp;F. Müller","doi":"10.1016/j.ostima.2025.100304","DOIUrl":"10.1016/j.ostima.2025.100304","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Bone changes are integral to the onset and progression of OA. Many aspects remain poorly understood due to the inability to assess bone architecture in vivo. Research has relied on ex vivo imaging, hindering evaluation of early-stage disease and longitudinal analysis. Conventional CT lacks the resolution to visualise subchondral bone microstructure. While ex vivo Photon Counting CT (PCCT) has demonstrated imaging comparable to μCT, its ability to capture bone microstructure in vivo in knee OA patients under clinical conditions remains unproven.</div></div><div><h3>OBJECTIVE</h3><div>The aim of this study was to compare in vivo and ex vivo PCCT of subchondral bone features in patients with knee OA.</div></div><div><h3>METHODS</h3><div>Pre-surgery in vivo and post-surgery ex vivo PCCT (Siemens Naeotom Alpha, Siemens Healthineers, Germany) of the tibial plateau from participants with severe knee OA referred to arthroplasty surgery from January 2022 through September 2023 were compared. Acquisition/reconstruction details: a tube current of 120 kV, a matrix size of 1024 × 1024, a slice thickness of 0.2 mm, and a FOV of 150 × 150 mm. 18 in vivo/ex vivo PCCT pairs were included. The ex vivo scans was registered to the in vivo scans. Linear regression and Bland-Altman plots were used to assess correlation and agreement between in vivo and ex vivo measures of bone volume fraction (BV/TV), trabecular thickness (Tb.Th.), and attenuation in healthy and sclerotic trabecular bone. Delineated areas of bone sclerosis were compared using the Dice coefficient and Hausdorff distance, Fig. 1.</div></div><div><h3>RESULTS</h3><div>Comparing in vivo and ex vivo scans strong correlations were found for BV/TV, R²=0.82 and attenuation in both healthy, R²=0.89, and sclerotic, R²=0.79, bone, while a moderate correlation was found for Tb.Th., R²=0.55. Bias for BV/TV and Tb.Th. was -4.1% and -0.598mm, respectively, and -41.4 HU and -81.1 HU for healthy and sclerotic bone, respectively. A proportional bias was observed for BV/TV and Tb.Th., Fig. 2. There was excellent agreement between the segmentations of sclerotic areas, Dice coefficient = 0.91 and Hausdorff distance = 0.11mm.</div></div><div><h3>CONCLUSION</h3><div>In patients with severe knee OA, BV/TV and attenuation can be obtained with high correlation and small bias between in vivo and ex vivo scans. Tb.Th. showed moderate correlation and larger bias. Subchondral bone sclerosis, a key OA feature, is well translated from ex vivo to in vivo PCCT. Longitudinal studies using in vivo PCCT are feasible, but caution may be advised when measuring Tb.Th.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100304"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524124","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":"EFFECT OF LATERAL MENISCUS POSTERIOR ROOT TEARS ON CARTILAGE AND MENISCAL MECHANICS","authors":"J.S. Broberg,&nbsp;E. Hoptioncann,&nbsp;A. Kimbowa,&nbsp;A. Yung,&nbsp;K. Bale,&nbsp;I. Hacihaliloglu,&nbsp;P. Lodhia,&nbsp;D.R. Wilson","doi":"10.1016/j.ostima.2025.100353","DOIUrl":"10.1016/j.ostima.2025.100353","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Measuring cartilage and meniscal mechanics in loaded knees is essential to understanding the effects of lateral meniscus posterior root tears (LMPRTs) and the effectiveness of meniscal repair procedures that seek to protect the joint from degeneration. Studies have assessed mechanics with thin-film pressure sensors or finite element models, but their conclusions are limited by the invasiveness or inherent assumptions of the techniques employed. Ultra-high field MRI provides sufficient resolution to measure cartilage and meniscal mechanics during loading in a compatible loading device, without requiring disruption or simulation of the articulating joint surfaces. However, no studies have evaluated the impact of LMPRTs on the cartilage and meniscal mechanics in a human cadaveric knee using such a method.</div></div><div><h3>OBJECTIVE</h3><div>Test the hypothesis that LMPRTs increase femoral and tibial cartilage strain and meniscal extrusion.</div></div><div><h3>METHODS</h3><div>Six human knee lateral compartments (mean age 70 yrs) were tested. Anatomical alignment in full extension was maintained during preparation. The lateral meniscus and its roots, meniscotibial ligament, and attachment to the popliteus, as well as the ACL, were preserved. Specimens were placed in a novel pneumatic compression apparatus customized for use a 9.4T MRI scanner. Morphologic scans with a resolution of 0.06 × 0.12 × 0.4 mm were acquired before loading and after 2 hours of loading (Figure 1). The load applied was constant and equivalent to 48% body weight to simulate two-legged standing. An artificial LMPRT was then created, and specimens were left unloaded until testing the next day with the same protocol. Joint tissues were manually segmented for both intact and LMPRT conditions, in both loaded and unloaded states. Flattened cartilage profiles were generated to calculate cartilage strain in the axial direction, with negative strain indicating compression. The mean and maximum strains in the tibiofemoral contact area were determined in both the femoral and tibial cartilage. Meniscal extrusion was measured as the perpendicular distance between the external edge of the meniscus and the line bisecting the external edge of the tibial plateau and femoral condyle in the most anterior slice of the popliteus’ insertion. All measures were compared between conditions with paired Student’s t-tests with significance set to 0.05.</div></div><div><h3>RESULTS</h3><div>Maximum compressive strain in the tibiofemoral contact region of the femoral (p = 0.013) and tibial (p = 0.010) cartilage increased significantly after the LMPRT (Figure 2). The increase in mean compressive strain in the tibiofemoral contact region after the LMPRT was not significantly different for the femoral (p = 0.103) or tibial (p = 0.065) cartilage. Likewise, the increase in meniscal extrusion after the LMPRT was not significantly different (p = 0.143). Specimens with a greater incr","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100353"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524180","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 VARIATION IN TRAPEZIOMETACARPAL BONE MICROARCHITECTURE IN FEMALES WITH OSTEOARTHRITIS USING HR-PQCT","authors":"M.T. Kuczynski ,&nbsp;C. Hasselaar ,&nbsp;G. Dhaliwal ,&nbsp;C. Hiscox ,&nbsp;N.J. White ,&nbsp;S.L. Manske","doi":"10.1016/j.ostima.2025.100299","DOIUrl":"10.1016/j.ostima.2025.100299","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>The trapeziometacarpal (TMC) joint, comprised of the trapezium (TRP) and first metacarpal (MC1) bones, is a mechanically complex, saddle-shaped joint. Studies have estimated that the peak forces acting on the TMC joint are up to five times higher than the corresponding external forces [1]. Moreover, cadaveric studies have shown non-uniform cartilage loss in TMC joint with OA [2]. While several cadaveric studies have investigated TMC joint cartilage and bone changes, evaluation of subchondral bone changes in the TMC joint <em>in vivo</em> is lacking.</div></div><div><h3>OBJECTIVE</h3><div>The objective of this study was to investigate differences in bone microarchitecture in anatomical quadrants of the TMC joint in women with TMC OA compared to age- and sex-matched controls. We hypothesized that women with TMC OA will exhibit quadrant-specific differences in bone microarchitecture compared to controls. Specifically, we hypothesized that the volar region of the TMC joint will demonstrate an increase in trabecular thickness, bone volume, and volumetric bone mineral density due to localized bone adaptations as a response to increased loading in the volar region.</div></div><div><h3>METHODS</h3><div>14 females diagnosed with symptomatic TMC OA (mean age: 60 ± 6.5 years) and 12 similarly aged female controls (mean age: 59 ± 5.7 years) were scanned using HR-pQCT (XtremeCT2, Scanco Medical). A standard HR-pQCT scanning protocol was used (61 µm³ voxels). Images were preprocessed using a Laplace-Hamming filter and segmented with a fixed threshold (15% of the maximum intensity). A bone coordinate system was automatically defined for the MC1 and TRP [3], and used to separate each bone into four anatomical quadrants: 1) radial-dorsal (RD), 2) radial-volar (RV), 3) ulnar-dorsal (UD), and 4) ulnar-volar (UV). For each whole bone and quadrant, we computed volumetric bone mineral density (vBMD, mg HA/cm³), bone volume fraction (BV/TV, %), and bone thickness (B.Th, mm). A mixed ANOVA was used to compare bone measures in each bone and quadrant between groups.</div></div><div><h3>RESULTS</h3><div>We did not observe a significant difference in total bone parameters between groups for the MC1 or TRP. However, we found a statistically significant interaction effect between the volar and dorsal quadrants of the TRP and group for B.Th (p = 0.02, Figure 1, Table 1). Compared to controls, the mean B.Th in the TRP of the OA group was 1.9% lower in the RD quadrant, 7.5% lower in the UD quadrant, 4.8% greater in the RV quadrant, and 6.2% greater in the UV quadrant.</div></div><div><h3>CONCLUSION</h3><div>Our results suggest that whole bone TMC microarchitecture may not differ between OA and controls; however, we found significant differences in quadrant bone microarchitecture. This suggests that the MC1 and TRP undergo localized bone microarchitectural changes to adapt to the loading of the TMC joint. Further, our results s","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100299"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523382","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":"OPTIMIZED DEEP LEARNING METHOD FOR AUTOMATED SEGMENTATION OF BONE MARROW LESIONS","authors":"Q. Shihua ,&nbsp;W. Qiong ,&nbsp;S. Juan ,&nbsp;B.D. Jeffrey ,&nbsp;M. Timothy ,&nbsp;Z. Ming","doi":"10.1016/j.ostima.2025.100319","DOIUrl":"10.1016/j.ostima.2025.100319","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Bone Marrow Lesions (BMLs), characterized by high-signal intensity on fat-suppressed MRIs, are associated with the progression of knee osteoarthritis (OA). In early OA or when joint damage is not visible on radiographs, BMLs are predictive markers for progression. However, their irregular distribution, potentially large size, and low-contrast boundaries challenge BML segmentation.</div></div><div><h3>OBJECTIVE</h3><div>This study introduces a novel training strategy for enhancing automated BML segmentation accuracy</div></div><div><h3>METHODS</h3><div>We aimed to optimize a deep learning method for automatic BML detection and segmentation in MRI, using the Osteoarthritis Initiative (OAI) dataset split into 70% training (210 participants), 15% validation (45 participants), and 15% testing (45 participants), totaling 1025, 190, and 201 MRIs, respectively. Images were employed using data augmentation like brightness, contrast, and geometric transformations. We applied a closing operation, a morphological technique combining dilation and erosion, to smooth edges, addressing the coarse manual labels that impair training. Several models (U-net, SwinUnetR, AttentionUnet, and U-net++) were trained with single-label (BML) and dual-label (BML + femur bone) outputs. Model performance was measured with the Dice Similarity Coefficient (DSC) for overlap and HD95 for boundary error. Cross-entropy and Dice loss functions improved sensitivity during training, particularly in dual-label channels where the femur bone location helped constrain BML positions. We also applied Pixel-Wise Voting (PWV) to improve segmentation stability and accuracy by averaging results from image variations, reducing false positives, and enhancing final segmentation outcomes.</div></div><div><h3>RESULTS</h3><div>UNet++ model with dual-label (BML + femur bone) yielded the best accuracy, outperforming U-net, SwinUnetR, and AttentionUnet. Figure 1 shows its predicted region (yellow) overlapping well with the manually labeled BML and aligning with boundaries. Specifically, the dual-label Unet model with PWV improved DSC from 62.21% to 64.88% for BML and to 96.52% for bone, while HD95 dropped to 26.82% for BML and 15.52% for bone. SwinUnetR with dual-label and PWV also showed improved DSC (65.06% to 66.70% for BML; 96.34% for bone) and reduced HD95 to 28.31% for BML and 11.54% for bone. AttentionUnet exhibited notable PWV improvements in bone segmentation. Overall, Unet++ achieved the highest performance with dual-label and PWV, increasing DSC from 66.16% to 68.48% for BML and 96.66% for bone, with the lowest HD95 values.</div></div><div><h3>CONCLUSION</h3><div>This study employed augmentation strategies, a closing operation, and both single- and dual-label analyses to train four models—Unet, SwinUnetR, AttentionUnet, and Unet++. Cross-entropy loss and Pixel-Wise Voting (PWV) enhanced model performance, with dual-label consistently outperforming single-label, es","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100319"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523454","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}