Osteoarthritis imaging最新文献

{"title":"A POST-PROCESSING METHOD FOR REFINEMENT OF CT-BASED DEEP LEARNING ACETABULAR SEGMENTATIONS","authors":"S. Zhang, R. Mohankumar, A. Naraghi, C. Whyne, A.K.O. Wong","doi":"10.1016/j.ostima.2026.100449","DOIUrl":"10.1016/j.ostima.2026.100449","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Accurate acetabular segmentation is important for clinical evaluation of hip morphology and surgical planning. TotalSegmentator (a neural network-based method) enables scalable CT-based hip bone segmentation, but focal errors persist in the acetabular region despite strong overall performance. These limitations highlight the need for targeted regional refinement of the acetabulum.</div></div><div><h3>OBJECTIVE</h3><div>To develop and validate a post-processing refinement method to improve CT-based acetabular segmentations generated by TotalSegmentator.</div></div><div><h3>METHODS</h3><div>A post-processing workflow was developed in Python to refine TotalSegmentator-generated acetabular segmentations. The acetabulum was localized using connected-components analysis of the obturator foramen and a solidity-based rule to identify the acetabular roof. Binary thresholding at 150 HU suppressed over-segmented non-osseous voxels, while HU-guided mask expansion restored under-segmented osseous voxels. Morphological closing, flood-filling, and selection of the largest 3D connected-component were used to close acetabular contour gaps and to remove residual irregularities. Rule-based slice-wise voxel count checks were used to selectively apply larger-kernel fallback morphological closing when closing using the default kernel size was insufficient. The original segmentation was restored when refinement introduced more error. An established subset of the TotalSegmentator dataset comprising 203 hips from 102 CT scans was used for validation. After exclusion of hips with incomplete view, metal implants, focal acetabular lesions, or pelvic fractures, 104 hips from 58 CT scans were included in the final analysis. The original TotalSegmentator hip bone annotations were manually revised to generate corrected ground truth segmentations. Performance of the post-processing workflow was evaluated by comparing post-processing refined and original acetabular segmentations against corrected ground truths using Dice similarity coefficient (DSC) to quantify overlap, and surface distance metrics, including: surface Dice, average surface distance (ASD), and Hausdorff distance (HD; 95th and 100th percentiles) to quantify boundary agreement. One-sided paired t-tests were used to assess improvement.</div></div><div><h3>RESULTS</h3><div>Within the validation dataset, patients had a mean age of 67.3±11.9 years; 63.8% were male, 96.6% of scans were acquired on Siemens scanners, and the median tube voltage was 100 (interquartile range: 100, 120) kVp. Quantitative segmentation performance within the acetabular region is summarized in Table 1. Compared with the original TotalSegmentator output, the refined segmentations showed significantly higher overall DSC and surface Dice scores, as well as significantly lower ASD and HD95, for both left and right hips. HD100 did not differ significantly between the original and refined segmentations for either side. R","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100449"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DEEP-LEARNING DETECTION OF INTRA-ARTICULAR CALCIFICATION ON KNEE CT","authors":"X. Qiu, J.G. Domingues, J.A. Lynch, R. Kijowski, H. Park, M. Jarraya, F. Liu, A. Guermazi, T. Neogi","doi":"10.1016/j.ostima.2026.100442","DOIUrl":"10.1016/j.ostima.2026.100442","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Intra-articular calcification may represent a distinct radiological osteoarthritis phenotype associated with structural joint and compositional tissue changes. Advanced image analysis and deep learning approaches can extract high-dimensional imaging features that are difficult to quantify visually and may improve the detection and characterization of calcification-related osteoarthritis phenotypes.</div></div><div><h3>OBJECTIVE</h3><div>As a first step in this broader planned work, we aimed to develop an initial deep learning model to classify knees with and without intra-articular calcifications on knee CT scans from the MOST Study.</div></div><div><h3>METHODS</h3><div>We analyzed knee CT data from the MOST Study after image quality control. 4,014 knee CTs were read by a musculoskeletal radiologist using BUCKS in a semi-quantitative manner. Among these, 3,584 knees had no mineralization, and 430 had either cartilage or meniscal mineralization (BUCKS>0 in different meniscus and/or cartilage subregions). CT images were preprocessed by standardizing orientation, cropping to the knee region, resizing to 224 × 224 × 128 voxels, and applying min–max normalization to scale intensities to 0–1. Data were partitioned into training, validation, and hold-out test sets using stratified sampling to preserve the proportion of mineralization cases across all splits: 2,508 training samples, including 301 with mineralization; 602 validation samples, including 64 with mineralization; and 603 testing samples, including 65 with mineralization. We implemented a modified 3D ResNet-18 architecture, which has been previously used for CT images. Specifically, this consisted of 3D convolutional layers, residual blocks, global average pooling, dropout regularization with p = 0.2, and a final fully connected output layer for binary classification of intra-articular calcification. To address the severe class imbalance during training, we applied balanced class weights to the binary cross-entropy loss function, heavily penalizing misclassifications of the minority calcification class.</div></div><div><h3>RESULTS</h3><div>The modified 3D ResNet-18 demonstrated strong discrimination for IA calcification detection. Across training epochs, validation AUC increased to a peak of 0.91, with validation accuracy reaching 0.919. On the independent hold-out test set, the model achieved an PR-AUC of 0.809, AUC of 0.921 and an accuracy of 0.949, with balanced performance, as reflected in robust sensitivity and specificity for detecting calcification<strong>.</strong> These results indicate that 3D CT-based deep learning can identify imaging patterns associated with IA calcification despite class imbalance.</div></div><div><h3>CONCLUSION</h3><div>A modified 3D ResNet-18 model achieved promising performance for automated IA calcification detection on knee CT scans, although external validation and more granular characterization of calcification subtypes rem","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100442"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148557877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"QUANTITATIVE MEDIAL SUBCHONDRAL BONE ATTRITION FROM MRI IS ASSOCIATED WITH KNEE OSTEOARTHRITIS SEVERITY AND SYMPTOMS","authors":"T. Yu, C.A. Hincapié, O. Espin-Garcia, A.KO. Wong","doi":"10.1016/j.ostima.2026.100403","DOIUrl":"10.1016/j.ostima.2026.100403","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Subchondral bone plays a key role in load transmission in knee OA, yet existing semi-quantitative (SQ) OARSI attrition scores are subjective and may be limited in detecting early structural changes. A quantitative measure of subchondral bone attrition (qSBA) may provide a more sensitive and objective characterization of these alterations.</div></div><div><h3>OBJECTIVE</h3><div>1) To develop an automated MRI-based method to quantify qSBA. 2) To evaluate the cross-sectional associations of qSBA with radiographic knee OA severity and knee symptoms.</div></div><div><h3>METHODS</h3><div>This study included 91 knees from the OAI baseline visit, a longitudinal cohort of adults aged 45-79 years at risk of or with knee OA. We included all knees with baseline medial compartment SQ attrition score of 1-2 (n=33). A comparison group with SQ attrition score 0 was randomly sampled in each stratum of subcohort (incidence, progression and control) and Kellgren-Lawrence grade (KLG) (n=58). Sagittal knee MRI scans were acquired using a 3D DESS WE sequence (in-plane spatial resolution 0.365 × 0.456 mm; slice thickness 0.7 mm). Tibial masks were obtained using a deep learning segmentation model (nnU-Net) with subsequent manual correction where required. An automated pipeline was developed to quantify qSBA on sagittal slices of the medial tibial plateau. The medial compartment was identified using anatomical subdivision rules consistent with the Whole-organ MRI score (WORMS). Within each medial sagittal slice, a reference line was defined based on tibial axis (see Figure footnote). Attrition depth was computed at each anterior-posterior location as the perpendicular distance between the observed bone surface and the reference line. Slice-level measurements were then aggregated across medial slices to derive summary metrics, including maximum and mean depth (mm), and total attrition area (mm<sup>2</sup>) (Figure). Construct validity was assessed using Spearman correlation between qSBA and SQ medial attrition scores. Associations between qSBA and knee outcomes (KLG, WOMAC and KOOS) were evaluated using multivariable linear regression adjusting for age, sex, and BMI, with additional models further adjusting for KLG and SQ attrition scores. Multiple comparison was adjusted using Benjamini-Hochberg method.</div></div><div><h3>RESULTS</h3><div>In the analytic sample, participants had a mean age of 64.3 (SD 9.3) years and BMI of 28.9 (SD 4.8); 49% were female and 84% were White. Mean qSBA showed a moderate positive correlation with SQ attrition scores (Spearman ρ=0.51, 95% CI 0.32-0.67). In adjusted models, higher KLG was associated with greater qSBA, with KLG3 showing 0.47 mm (p=0.062) and KLG4 showing 0.88 mm (p<0.001) larger mean attrition compared to KLG0. Greater qSBA was associated with worse symptoms across multiple domains (Table). In models adjusted for age, sex, and BMI, higher qSBA was associated with worse WOMAC and KOOS. After a","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100403"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148557880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CLINICAL VALIDATION OF ARTIFICIAL INTELLIGENCE (AI)-BASED CARTILAGE ANALYSIS PREDICTING KNEE REPLACEMENT (KR)","authors":"S. Maschek,&nbsp;W. Wirth,&nbsp;D.J. Hunter,&nbsp;C.K. Kwoh,&nbsp;F. Eckstein","doi":"10.1016/j.ostima.2026.100436","DOIUrl":"10.1016/j.ostima.2026.100436","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Robust biomarkers are essential for developing OA therapies. Ideally, these biomarkers will function as surrogate endpoints that reliably reflect clinically meaningful outcomes in the relevant phenotype. Cartilage loss is central to the pathogenesis of OA. As such, cartilage morphology represents such a scalable endpoint for use in clinical trials, and to this end, morphometry should best be performed automatically, without human interaction.</div></div><div><h3>OBJECTIVE</h3><div>To clinically validate AI- and MRI-based morphometric cartilage analysis as a potential surrogate, by studying cartilage loss prior to knee replacement (KR).</div></div><div><h3>METHODS</h3><div>KRs at 36- to 60-months’ follow-up in the OAI were matched pairwise (1:1) with controls by age, sex and radiographic OA status. Cartilage segmentation was performed using automated, AI-based (sag DESS / cor FLASH), and manual methods (sag DESS). 2-year cartilage thickness change prior to KR was compared between KR cases and controls, using Cohen’s D as an effect size measure. The medial compartment (MFTC) was the primary analytic endpoint.</div></div><div><h3>RESULTS</h3><div>132 case and matched control knees (113 participants; 55% women; age 64.4±8.6 [mean ± SD] years) had DESS, and 35 of those had FLASH in both the case and control knee. In case knees, the MFTC cartilage thickness loss by automated segmentation of the DESS was -263µm (95% CI: -337, -189; SRM -0.63), whereas by manual analysis it was -259µm (95% CI -331, -188; SRM -0.64). In controls, the rate of cartilage thickness change was substantially lower by either analysis method, with the effect size being similar for automated (Cohen’s D -0.40) vs. manual segmentation (-0.35). For automated FLASH the Cohen’s D was -0.59 vs. -0.30 for manual DESS segmentation in the same knees.</div></div><div><h3>CONCLUSION</h3><div>AI-based cartilage analysis performed at least as well as manual expert analysis in differentiating rates of cartilage loss in KR vs. control knees. This was consistent across DESS and FLASH MRI. The current findings support automated MRI-based cartilage analysis as a scalable imaging biomarker linked to a clinically meaningful outcome.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100436"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LONGITUDINAL CHANGES IN PERI-ARTICULAR BMD USING QUANTITATIVE CT IN KNEE OA PATIENTS: DATA FROM THE IMI-APPROACH STUDY","authors":"M. Jarraya,&nbsp;M.P. Jansen,&nbsp;J.D. Laredo,&nbsp;S. Mastbergen,&nbsp;H. Weinans,&nbsp;F.J. Blanco,&nbsp;F. Berenbaum,&nbsp;M. Kloppenburg,&nbsp;I.K. Haugen,&nbsp;F.W. Roemer,&nbsp;K. Engelke","doi":"10.1016/j.ostima.2026.100404","DOIUrl":"10.1016/j.ostima.2026.100404","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;BACKGROUND&lt;/h3&gt;&lt;div&gt;Increasing evidence suggests that peri-articular bone remodeling plays a key role in the development and progression of osteoarthritis (OA). However, a critical knowledge gap remains regarding depth-specific longitudinal changes in peri-articular bone mineral density (BMD) in both the periarticular femur and tibia.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;PURPOSE&lt;/h3&gt;&lt;div&gt;Describe depth-specific longitudinal changes over two years in peri-articular BMD in the femoral and tibial epiphysis using quantitative CT (QCT) among patients with pre-radiographic versus established radiographic OA.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;The IMI-APPROACH study is a multicenter cohort including patients with clinical and/or structural knee OA. The present analysis included participants who had QCT and radiographic examinations at baseline and 2-year follow-up. QCT was used to assess BMD change at the medial and lateral femur and tibia at 4 volumes of interest with increasing distance from the hyaline cartilage: 1. Cortical bone plate (&lt;em&gt;Cort&lt;/em&gt;), 2. Subchondral area (&lt;em&gt;Sub&lt;/em&gt;), 3. Mid-epiphysis (&lt;em&gt;Mid&lt;/em&gt;), and 4. Adjacent to the physis (&lt;em&gt;Juxta&lt;/em&gt;). Baseline radiographs were used to determine the Kellgren and Lawrence grade (KLG) and to categorize patients into pre-radiographic OA (KLG 0–1) and radiographic OA (KLG ≥2). We used linear mixed-effects models (LMMs) to evaluate the anatomical gradient in mean percentage change in BMD from the joint surface to the deep bone. Mean percentage change in BMD was modeled as the continuous dependent variable, with depth rank from cortical to juxta-articular bone included as a fixed effect. A random intercept for patient ID was included to account for repeated measures within patients. To determine if the depth-dependent trajectory of BMD loss differed significantly between pre-radiographic and radiographic OA, an interaction term (depth rank*OA status) was included in the models. Additionally, we reported BMD change as a categorical variable (gain ≥5% increase in BMD over the study time, loss≥5% decrease in BMD, stable= within 5% of baseline BMD) in each region of interest of the tibial plateaus.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;177 knees from 177 participants were included. The mean age was 67 years (SD = 7), 76% of participants were female, and the mean BMI was 27.98 kg/m² (SD = 4.9). Both medial and lateral femoral condyles and tibial plateaus showed progressively greater BMD loss over 24 months from the cortical bone plate to the juxta-physeal region. When BMD change was treated as a continuous variable, the anatomical gradient of BMD loss across tissue depths in the femoral condyles was significantly steeper in patients with established radiographic OA compared to those with pre-radiographic OA (Figure 1). This depth-dependent trajectory was particularly pronounced in the medial condyle (interaction p &lt; 0.001) and was also significant in the lateral condyle (interaction p = 0.016). Conve","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100404"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EVALUATING UNSUPERVISED ANOMALY DETECTION FOR AUTOMATED SHOMRI IN A YOUNG ADULTS POPULATION","authors":"M.A. Kamphuis,&nbsp;D.F. Hanff,&nbsp;E.H.G. Oei,&nbsp;J. Runhaar,&nbsp;S.M.A. Bierma-Zeinstra,&nbsp;S. Klein,&nbsp;J. Hirvasniemi","doi":"10.1016/j.ostima.2026.100432","DOIUrl":"10.1016/j.ostima.2026.100432","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Early structural changes in hip OA are rarely evaluated in young adults, yet identifying such changes may provide insight into disease onset. The Scoring Hip Osteoarthritis with MRI (SHOMRI) system enables detailed assessment of hip joint pathology, but large-scale application is limited by the time and expertise required for manual scoring. Because abnormalities in young adults are rare and subtle, unsupervised anomaly detection (UAD), which learns only from normal examples, may offer a scalable approach for automated screening.</div></div><div><h3>OBJECTIVE</h3><div>The aim of this study was to investigate whether UAD models can prescreen SHOMRI scoring in young adults by automatically identifying potential abnormalities on MRI.</div></div><div><h3>METHODS</h3><div>Hip MRI from 286 participants (572 hips; 145 males, 141 females; mean age 18.3 ± 0.7 years) from the Generation R cohort were manually scored using SHOMRI, which evaluates 8 features across 10 hip joint regions. Scoring was performed on T2-weighted fat-suppressed 3D FSE (CUBE) images (0.89 × 0.89 mm² inplane resolution; 1.2 mm slice thickness). Four reconstruction-based UAD models: an autoencoder (AE), a variational autoencoder (VAE), a generative adversarial network (GAN), and diffusion models were trained using 512 healthy hips and evaluated on 60 hips with and without abnormalities. Model performance was assessed through visual inspection of reconstructions, separation of anomaly score distributions using histograms, and classification performance using AUC.</div></div><div><h3>RESULTS</h3><div>Only 29 hips (5%) showed SHOMRI abnormalities, mostly (n=26) labral changes (Table 1). All evaluated UAD models demonstrated poor discrimination, with overlapping anomaly score distributions. Reconstructions were blurry and lacked fine anatomical detail: abnormalities were suppressed, and normal variation smoothed, producing anomaly maps mostly highlighting intensity differences. AE and VAE models flagged nearly all cases as abnormal, GANs introduced hallucinated edges, and diffusion models suppressed both normal and abnormal features.</div></div><div><h3>CONCLUSION</h3><div>Structural abnormalities were rare in this young adult population. The small size and subtle appearance of SHOMRI findings relative to normal anatomic variability, combined with low‑fidelity reconstructions, limited UAD performance. Methods incorporating anatomical priors, region‑specific modeling, or weak supervision may be needed to improve detection of subtle SHOMRI abnormalities.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100432"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BONE DATA LAKE: A SCALABLE PLATFORM FOR LARGE-SCALE RADIOGRAPHIC TRABECULAR BONE TEXTURE ANALYSIS","authors":"M. Wolski,&nbsp;P. Podsiadlo","doi":"10.1016/j.ostima.2026.100447","DOIUrl":"10.1016/j.ostima.2026.100447","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Trabecular bone (TB) texture analysis of hand and knee X-ray images may serve as a biomarker for detection, prediction and monitoring of OA. However, the scale and heterogeneity of imaging data pose challenges for data storage, standardization, cataloguing, security, and efficient OA analysis.</div></div><div><h3>OBJECTIVE</h3><div>Our objective is to address this problem through the development of a platform, called the Bone Data Lake (BDL) that enables the secure storage of large numbers of X-ray images, TB texture regions and TB texture parameters, independent of their format, size and source.</div></div><div><h3>METHODS</h3><div>The BDL was developed as a cloud-based platform (Wolski <em>et al.</em> 2025; Figure 1) using Amazon Web Services. It comprises three components: a raw data storage, a processed data storage, and data reference system. The first component operates as a repository of data in its raw original format. The second component reformats the data into a standardized format. The last component is the central catalog, which holds metadata information, such as storage locations of original and reformatted data. The BDL’s performance was evaluated using a dataset of 20,000 knee and hand X-ray images of varying formats (DICOM, PNG, JPEG, BMP, and compressed TIFF) and sizes (0.3 MB to 66.7 MB). The dataset represents convenience samples of X-ray images downloaded from the ReadMyXray website (<span><span>https://readmyxray.curtin.edu.au</span><svg><path></path></svg></span>). Images were uploaded into BDL and automatically converted to a standardized TIFF format. TB regions of interest (ROIs) were selected on the standardized images, and their catalog was automatically constructed. Fractal signature texture parameters were calculated for the ROIs and stored in Microsoft Excel files, which were automatically transformed into CSV files and cataloged.</div></div><div><h3>RESULTS</h3><div>The BDL efficiently transforms X-ray images and catalogues TB texture regions and parameters. For 20, 000 images, BDL achieved approximately tenfold reduction in processing time compared with the workstation (8 minutes versus 82 minutes). The data catalog successfully stored metadata including image ID, timestamps, resolutions, image sizes, and ROI orientation. Schema of data in the Excel files was correctly detected and the corresponding CSV file was generated.</div></div><div><h3>CONCLUSION</h3><div>The BDL is a scalable and flexible data storage platform for X-ray and TB bone texture images and parameters. This platform provides a foundation for future OA research infrastructure and supports the development of reliable, secure, and collaborative systems for the detection and prediction of OA from radiographs.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100447"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRI-DERIVED POPLITEAL ARTERY WALL BIOMARKERS AND HIP OSTEOARTHRITIS: DATA FROM THE OSTEOARTHRITIS INITIATIVE","authors":"P. Mirghaderi,&nbsp;N. Eshraghi,&nbsp;F. Pishgar,&nbsp;H.A. Chansky,&nbsp;N. Balu,&nbsp;C. Yuan,&nbsp;D.J. Hunter,&nbsp;F.W. Roemer,&nbsp;A. Guermazi,&nbsp;M. Chalian","doi":"10.1016/j.ostima.2026.100400","DOIUrl":"10.1016/j.ostima.2026.100400","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Vascular health has been implicated in OA pathogenesis, but evidence specific to hip OA remains limited. MRI-derived popliteal artery wall measurements, obtained opportunistically from knee MRI, may provide an imaging marker of systemic vascular burden and help characterize vascular-metabolic phenotypes relevant to hip OA and hip pain.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;To evaluate whether baseline knee MRI-derived popliteal artery wall measurements are associated with prevalent and 4-year incident radiographic hip OA (rHOA), symptomatic hip OA (sHOA), and frequent hip pain in the Osteoarthritis Initiative.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;This observational study included OAI participants with available baseline knee MRI and hip radiographs. Cross-sectional analyses included 8,506 hips, and longitudinal analyses included 6,224 hips with 4-year follow-up radiographs. Popliteal artery wall measurements were quantified from baseline knee MRI using an automated vessel-wall segmentation pipeline and included eccentricity ratio, mean normalized wall index (NWI), maximum NWI, and maximum wall thickness. Outcomes were prevalent and incident rHOA, sHOA, and frequent hip pain. Associations were assessed using multivariable logistic regression with cluster-robust standard errors to account for two hips within participants. Models were adjusted for age, sex, BMI, height, comorbidity score, smoking status, Physical Activity Scale for the Elderly, history of myocardial infarction or stroke, systolic and diastolic blood pressure, diabetes, and lipid-lowering medication use. ORs are reported per 1-SD increase in each vascular measurement.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;At baseline, 521/8,506 hips (6.1%) had rHOA and 264/8,506 hips (3.0%) had sHOA. Mean age and BMI were 60.7 ± 9.1 years and 28.2 ± 4.7 kg/m², respectively. Higher mean NWI was associated with greater odds of rHOA (OR 1.22, 95% CI 1.09–1.36; p&lt;0.001), sHOA (OR 1.22, 95% CI 1.06–1.41; p=0.006), and frequent hip pain (OR 1.12, 95% CI 1.04–1.21; p=0.002). Higher eccentricity ratio was associated with rHOA (OR 1.12, 95% CI 1.02–1.22; p=0.017) and frequent hip pain (OR 1.14, 95% CI 1.07–1.21; p&lt;0.001), but not sHOA (OR 1.12, 95% CI 1.00–1.27; p=0.057). Over 4 years, 96/6,224 hips (1.5%) developed incident rHOA, 46/6,224 hips (0.7%) developed incident sHOA, and 809/6,302 hips (12.8%) developed incident frequent hip pain. Baseline popliteal artery wall measurements were not associated with incident rHOA or sHOA; however, higher mean NWI was associated with incident frequent hip pain (OR 1.16, 95% CI 1.05–1.27; p=0.002).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;CONCLUSION&lt;/h3&gt;&lt;div&gt;Automated knee MRI-derived popliteal artery wall biomarkers were associated with prevalent rHOA, sHOA, and frequent hip pain, and mean NWI was associated with 4-year incident frequent hip pain. These findings support opportunistic vascular assessment on knee MRI as a potential imaging marker of vascu","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100400"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FEMORAL HEAD TRANSLATION IS INCREASED IN PAINFUL HIPS","authors":"H. Zhang,&nbsp;D.R. Wilson","doi":"10.1016/j.ostima.2026.100448","DOIUrl":"10.1016/j.ostima.2026.100448","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Hip pain and osteoarthritis are thought to be largely related to biomechanical disruptions including abnormal stress distributions and altered kinematics. Many of these disruptions are associated with anatomical deformities such as those associated with femoroacetabular impingement (FAI). The normal femoral head has long been assumed to be constrained by the ball-and-socket configuration of the hip joint and therefore not translate during hip rotation. Recent work has measured femoral head translations for a range of postures and explored their relationships to hip morphology and symptoms. These studies have been limited by the postures that can be imaged in conventional MR and CT scanners. We have recently developed and validated an approach using upright open MRI to measure femoral head translation for postures representing a large range of hip motions.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;To determine whether femoral head translation is different between hips with and without pain, and between hips with different FAI deformities, for four postures.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;This study used 79 participants (25 males, 54 females, average age 45.6±6.5) enrolled in the Investigations of Mobility, Physical Activity, and Knowledge Translation in Hip Pain project, a population-based study of participants aged 20-49 with and without hip pain. Cases with hip pain were defined as self-reported pain in the groin or upper thigh in the past 12 months that lasted longer than 6 weeks or occurred on three or more occasions. Each participant was assessed for FAI morphology. Pincer morphology was defined as a lateral center edge (LCE) angle &gt; 40° or a positive crossover sign on an anteroposterior view weight-bearing radiograph. Cam morphology was assessed as alpha angle &gt; 55° in a T1-weighted hip scan on an oblique axial plane using a 3T MRI scanner (Philips Healthcare, Best, The Netherlands). Mixed deformity was defined as positive results for both cam and pincer assessments. Hip translation was measured in 4 postures using an Upright Open MRI scanner (0.5T MROpen, ASG Italy). We scanned the participants in supine FADIR, sitting FADIR, sitting neutral, and sitting cross legged postures (Figure 1), as well as in supine as a reference posture. We used a T1-weighted GFE sequence in the sagittal plane of the hip (Table 1). We scanned the pelvis in the axial plane with the participant in the supine position to establish pelvic coordinates. Femoral head positions were defined as the location of the femoral head center relative to the acetabular cup center in pelvis coordinates. The relative femoral head translation for each tested posture was defined as the relative movement of the femoral head from the supine posture to the tested posture. We tested the hypotheses that hip translation is affected by pain and FAI deformity type using two-way ANOVAs.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;We found increased anterior fem","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100448"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IMAGING-BASED ANALYSIS OF STRUCTURAL PRESERVATION IN EXPERIMENTAL OSTEOARTHRITIS","authors":"V Njoki Kamau,&nbsp;G Lee,&nbsp;JH Kim","doi":"10.1016/j.ostima.2026.100431","DOIUrl":"10.1016/j.ostima.2026.100431","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Preclinical osteoarthritis (OA) studies frequently report structural degeneration; however, comparative evaluation of therapeutic interventions using imaging-based endpoints remains limited. In particular, whether different acupuncture modalities exert distinct effects on joint structure and subchondral bone microarchitecture has not been systematically assessed.</div></div><div><h3>OBJECTIVE</h3><div>1) To compare the effects of multiple acupuncture modalities on joint structural integrity using imaging-based outcomes. 2) To determine whether laser acupuncture provides better preservation of cartilage and subchondral bone. 3) To evaluate modality-specific differences in bone microarchitecture remodeling.</div></div><div><h3>METHODS</h3><div>OA was induced via intra-articular monosodium iodoacetate in male Sprague–Dawley rats (n = 48 per study; n = 8/group). Two experimental arms were conducted:(1) comparative modalities (manual acupuncture, electroacupuncture, bee venom acupuncture, and laser acupuncture), and (2) wavelength-specific invasive laser acupuncture (650 nm and 830 nm). All treatments were applied at GB34 under standardized protocols. Structural outcomes were assessed using micro-computed tomography (Quantum GX2; 90 kV, 88 ìA, voxel size 20 ìm) and histological analysis (Safranin-O/Fast Green; OARSI scoring). Micro-CT parameters included bone volume fraction (BV/TV), trabecular thickness (Tb.Th), trabecular number (Tb.N), and trabecular separation (Tb.Sp), as well as meniscal ossicle formation.</div></div><div><h3>RESULTS</h3><div>Acupuncture modalities demonstrated distinct effects on joint structure. Laser acupuncture and electroacupuncture produced the greatest preservation of cartilage integrity and subchondral bone architecture compared to OA controls (p &lt; 0.001). Micro-CT analysis revealed that laser-treated groups exhibited improved trabecular organization and reduced pathological bone remodeling. Meniscal ossicle formation, a marker of OA progression, was significantly reduced in laser-treated animals (p &lt; 0.006). In wavelength-specific analysis, 830 nm laser acupuncture showed superior structural outcomes, with increased BV/TV (34.85 ± 1.21, p &lt; 0.01) and reduced trabecular separation (p &lt; 0.05), indicating enhanced preservation of bone microarchitecture. Histological findings were consistent with imaging data, demonstrating reduced cartilage degeneration and improved matrix preservation in laser-treated groups.</div></div><div><h3>CONCLUSION</h3><div>Acupuncture modalities differentially modulate structural joint degeneration in experimental OA. Imaging-based analyses demonstrate that laser acupuncture, particularly at 830 nm, provides better preservation of cartilage and subchondral bone microarchitecture.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100431"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}