Osteoarthritis imaging最新文献

Objective

Osteoarthritis affects joints of the hand and is radiographically characterized by joint space narrowing and changes in volumetric bone mineral density (vBMD). High-resolution peripheral computed tomography (HR-pQCT) allows for three-dimensional analysis of joint space width (JSW) changes but has yet to be applied to hand OA (HOA). The purpose of this study was to assess the utility of HR-pQCT in quantifying JSW and vBMD changes in HOA.

Design

The 2nd and 3rd distal interphalangeal (DIP2, DIP3) and trapeziometacarpal (TMC) joints of 20 women (10 controls, 10 HOA) were scanned using HR-pQCT. The Diseases of the Arm, Shoulder, and Hand (DASH) questionnaire and the Jebsen Hand Function Test (JHFT) were obtained. Radiographs were scored using the Eaton-Littler classification. We assessed between-group differences in HR-pQCT outcomes, and relationships between HR-pQCT outcomes and patient and radiographic characteristics.

Results

JSW maximum (JSW.Max) was higher in OA patients in the DIP2 (OA median [interquartile rage]: 2.07 mm [1.90–2.18], controls: 1.88 mm [1.84–1.89]) and DIP3 joints (OA: 2.01 mm [1.89–2.24], controls: 1.86 mm [1.82–1.95]). DIP3 JS volume was higher in OA (30.36 mm³, 19.35–34.57 mm³, controls: 17.05 mm³, 15.53–18.52 mm³). DASH scores were positively associated with DIP2 JSW asymmetry (JSW.AS), and JHFT times were positively associated with DIP3 JSW.Max, JSW.AS, and proximal DIP2 vBMD. Worsening radiographic TMC OA was associated with first metacarpal vBMD, JSW minimum, JSW.Max, and JSW.AS.

Conclusions

These preliminary findings suggest that HR-pQCT may be useful in investigating JS changes in HOA and warrants further investigation.

Objective

Gait alterations in knee osteoarthritis (OA) patients are potentially related to structural progression of joint tissues, of which some are modifiable. The current objective was to determine whether progression in individual OA pathologies is related to gait kinematic parameters in knee OA patients, and whether these changes are influenced by pain.

Design

Range of motion (ROM) during gait, joint tissue pathologies and morphology from index knee radiographs and MRI, and WOMAC pain were collected at baseline and at two-years in the IMI-APPROACH clinical knee OA cohort. Principal component (PC) analysis was performed on two-year change in gait parameters; the resulting (first) PC was compared between progressors and non-progressors for each structural parameter. When the PC indicated differences between groups (p < 0.1), individual gait parameters were compared for that structural outcome. Statistically significant differences in individual gait parameters were corrected for pain change.

Results

191 patients (age 66.5 ± 6.7; BMI 27.5 ± 4.8; 76 % female; 51 % Kellgren-Lawrence grade ≥2) were analyzed. The gait change PC differed between progressors and non-progressors for meniscal extrusion, bone marrow lesions (BMLs), and patellofemoral cartilage lesions. Further, meniscal extrusion progressors showed significantly more knee ROM and calf ROM decrease, BML progressors worsened more in thigh ROM, and patellofemoral cartilage lesion progressors improved in knee ROM. BML results were no longer significant after pain change adjustment (p = 0.054).

Conclusions

Meniscal extrusion and BML progression are associated with gait worsening, though for BMLs the effect might be influenced by pain.

Objective

Meniscal mechanics have been studied widely due to the high prevalence of meniscal injuries and their strong association with knee degeneration. Computational models and cadaver studies have contributed to our understanding of the menisci but require assumptions to extrapolate to living people. Imaging modalities provide the ability to make key measurements in vivo. In particular, magnetic resonance imaging (MRI) provides the three-dimensional (3D) visualization of the menisci required to make important measurements related to mechanical function. This mini review summarizes MR approaches that have been used to make measurements related to meniscal mechanics, including morphology, position, movement, shape, and extrusion.

Design

A literature search was performed using PubMed and Google Scholar, with search terms including “meniscus” and “MRI” in combination with “mechanics”, “position”, “shape”, “movement”, “size”, “loaded”, and “unloaded”. Articles were manually reviewed and selected by consensus between the authors as constituting the most important examples of work required to illustrate the breadth of measurement and imaging approaches used in research on meniscal function.

Results

MRI has been used for quantitative 3D analyses of the morphology and position of the menisci. Morphological analyses included measurements of meniscal volume, thickness, width, and bulging. Positional analyses included measurements of the overlap between the meniscal surface and tibial joint surface, the amount of extrusion, and the percentage of joint surface area and meniscal area that were covered or uncovered. Open MR scanners have been used to measure the movement of the menisci from full extension to deep flexion. MR compatible loading devices have been used to study the effect of loading on meniscal morphology and extrusion. Studies using these methods have found that there are differences in meniscal morphology between healthy and osteoarthritic participants, that the lateral meniscus and anterior horns have greater movement throughout flexion, and that meniscal extrusion increases under load.

Conclusions

MRI has improved our insight into meniscal mechanics. Simulated weightbearing, open imaging through the range of knee flexion, and image processing to yield 3D measurements have all contributed to this progress. These approaches have strong potential to explore clinically motivated research questions related to meniscal mechanics.

Objective

This mini review aims to assess the latest advancements in the field of osteoarthritis (OA) research, particularly focusing on the early detection and prediction of disease progression through the use of advanced imaging technologies utilizing biomechanical modeling and artificial intelligence (AI).

Design

The review consolidates and discusses findings from studies that utilize biomechanical modeling and/or machine learning algorithms to identify pathological changes in joint tissues indicative of OA or prediction of disease progression. It also briefly reviews future research and how these methods could be used as a part of OA management.

Results

AI algorithms have proven highly effective in recognizing the subtle changes in joint tissues associated with OA and in identifying patients at high risk for the disease. While these automated tools facilitate early diagnosis, they typically do not provide personalized intervention strategies to prevent disease progression. AI-enhanced biomechanical modeling has the potential to simulate the effects of various conservative interventions (e.g., weight management, optimal footwear, and gait retraining) on slowing OA progression, which could be pivotal for patient engagement and preventive care.

Conclusions

The integration of AI with biomechanical modeling holds significant promise for enhancing the management of OA by not only predicting disease onset and progression but also by enabling personalized intervention plans. Future research should focus on the development of these models to include personalized, preventive strategies that could effectively engage patients and potentially delay or prevent the onset of OA. This approach could revolutionize patient care by making early, targeted intervention feasible.

MRI-based compositional measurements of cartilage have been developed to characterize the biochemical composition of articular cartilage and the menisci, allowing the assessment of cartilage quality before cartilage loss has occurred and changes are still potentially reversible. They have also been used to investigate the complex relationship between biomechanics and cartilage health. This review focuses on cartilage T_1rho and T₂ cartilage compositional measurements as these techniques have been most frequently used in studies involving joint biomechanics. Recent clinical and in vitro studies are covered, including those investigating the relationship of cartilage composition with physical activity, gait, muscle strength, and biomechanical loading of specimens. These studies clearly illustrate the impact of physical activity on cartilage, how gait alterations after anterior cruciate ligament reconstruction may lead to early degenerative disease, and the complex interplay between muscle strength, gait, and cartilage composition. In vitro work also demonstrates how indentation stiffness correlates with cartilage composition. The review further consolidates the important role of cartilage compositional imaging in understanding the biomechanics and pathophysiology of degenerative joint disease.

Objective

The aim of this study is to present a newly developed automated method to determine radiographic measurements of hip morphology on dual-energy x-ray absorptiometry (DXA) images. The secondary aim was to compare the performance of the automated and manual measurements.

Design

30 DXA scans from 13-year-olds of the prospective population-based cohort study Generation R were randomly selected. The hip shape was outlined automatically using radiographic landmarks from which the acetabular depth-width ratio (ADR), acetabular index (AI), alpha angle (AA), Wiberg and lateral center edge angle (WCEA) (LCEA), extrusion index (EI), neck-shaft angle (NSA), and the triangular index (TI) were determined. Manual assessments were performed twice by two orthopedic surgeons. The agreement within and between observers and methods was visualized using Bland-Altman plots, and the reliability was studied using the intraclass correlation coefficient (ICC) with 95 % confidence intervals (CI).

Results

The automated method was able to perform all radiographic hip morphology measurements. The intermethod reliability between the automated and manual measurements ranged from 0.57 to 0.96 and was comparable to or better than the manual interobserver reliability, except for the AI.

Conclusion

This open-access, automated method allows fast and reproducible calculation of radiographic measurements of hip morphology on right hip DXA images. It is a promising tool for performing automated radiographic measurements of hip morphology in large population studies and clinical practice.

In recent years weight bearing computed tomography (WBCT) has been applied to the knee, delivering 3-D imaging in a physiologically loaded stance. Several quantitative approaches have been used to evaluate this data, with the greatest focus on joint space width (JSW) leading to 3-D JSW being potentially sensitive enough as a useful structural biomarker. Studies of JSW and lower limb alignment have been facilitated by novel technologies and have demonstrated significant differences between the non-weight bearing and weight bearing positions. Further work is needed to look at whole lower limb alignment that involves the interplay of all the joints of the lower limb. Steps are being taken to quantify bone parameters from cone beam technology, while there is an opportunity for contrast arthrography to evaluate articular cartilage and the meniscus in a weight bearing position. The concept of weight bearing imaging has also led to the evolution of different technology solutions, including different types of cone beam set up, upright and supine-loaded multidetector CT techniques. This latter approach has included kinematic CT with gating for acquisitions in different angles of flexion. WBCT has also been combined with MRI for finite element analysis of the meniscus. The increasingly varied applications of WBCT at the knee broadens its definition and hold potential for enhancing our understanding of factors in the onset and progression of osteoarthritis. However a consensus on standardized protocols will be required to facilitate these advances.

The concept that multiple joint tissues are involved in the osteoarthritis (OA) disease process is now widely accepted. There have been significant and important insights over the past two decades in the understanding of bone as a tissue undergoing pathological changes in OA. The specific bony changes of osteophyte growth and “bone attrition” associated with OA have been recognized for many years with several semi-quantitative radiographic and magnetic resonance imaging (MRI) grading systems designed to capture the magnitude of these changes. Over the past decade, there has been significant and important progress in the quantitative measurement of these changes. Manual methods for measuring bone area from 3D MR images have been improved with automation which offers both superior precision and a more responsive measurement that has been applied in several DMOAD randomized controlled trials. Measurement of true 3D bone shape, as opposed to simple geometric measures such as curvature and length, depends on automated methods of segmentation. In this field, important developments have taken place in the statistical parameterization of shape and the construction of OA vs non-OA shape metrics. Work has demonstrated that bone shape may provide an indication of OA status, may predict future OA onset, and is associated with clinical markers of OA such as pain, function and total joint replacement (TKR). Thus, bone shape may be a useful imaging biomarker for OA.

Objective

The objective of this study was to develop a deep-learning-based approach to automatically segment 3D ultrasound images of the synovial tissue in osteoarthritis of the first carpometacarpal (CMC1 OA).

Design

Deep learning predictions on 2D ultrasound slices sampled in the transverse plane were used to view the synovial tissue of the first carpometacarpal in patients with OA, followed by reconstruction into 3D surfaces. A modified 2D U-Net was trained using a dataset of 832 2D US images resliced from 89 3D US images. Segmentation accuracy was evaluated using a testing dataset of 208 2D US images resliced from 15 3D US images. Absolute and signed performance metrics were computed, and segmentation performance was compared between the manual segmentations of raters 1 and 2.

Results

Results of the U-Net-based run were mean 3D DSC 86.9 ± 4.8%, recall 93.7 ± 3.6%, precision 81.1 ± 6.9%, volume percent difference 16.9 ± 10.2%, mean surface distance 0.18 ± 0.04 mm, and Hausdorff distance 1.8 ± 0.8 mm. The algorithm demonstrated an overall increase in performance after 3D segmentation reconstruction compared to 2D predictions, but the difference was not statistically significant.

Conclusion

This study investigated the use of a modified U-Net algorithm to automatically segment the synovial tissue volume (STV) of CMC1 OA patients and demonstrated that the addition of this deep learning method increases the efficiency of STV estimations in clinical trial settings.

Objective

Chronic inflammation and altered walking biomechanics are common after ACL reconstruction (ACLR) and contribute to the development of osteoarthritis. Clinically accessible techniques are needed to monitor inflammation (ultrasound-assessed effusion-synovitis) and walking biomechanics (force-measuring insoles), and they must improve the translation of these assessments and determine whether inflammation and walking biomechanics are related in patients after ACLR. This study aimed to determine the association between ultrasound-detected knee effusion-synovitis and limb loading asymmetries during walking in patients 1–5 years post-ACLR.

Design

15 participants (9 women; age: 26 ± 6yrs; mass: 71 ± 15 kg; height: 173 ± 9 cm; months post-ACLR: 29 ± 13) were included in this cross-sectional study. Knee effusion-synovitis was assessed using a standardized protocol and graded using a validated scoring atlas (0 = absent, 1 = mild, 2 = moderate, 3 = severe) in the ACLR limb. Force-measuring insoles were used to capture the vertical ground reaction force (vGRF) during a one-minute treadmill walking trial. Limb symmetry indices (LSIs) were used to quantify limb loading asymmetry for the peak vGRF and the instantaneous loading rate (vGRF-LR). Spearman correlations determined whether effusion-synovitis grade was associated with peak vGRF and vGRF-LR LSI.

Results

Effusion-synovitis was present in the ACLR limb of 13/15 (87 %) participants (Grade 0: n = 2; Grade 1: n = 8; Grade2: n = 4, Grade3: n = 1). Effusion-synovitis grade was not significantly associated with peak vGRF LSI (mean±sd: 98.0 ± 5.6; ρ = 0.38, p = 0.162), but was significantly associated with vGRF-LR LSI (98.2 ± 11.4; ρ = 0.55, p = 0.035).

Conclusion

Most participants 1–5 years post-ACLR have ultrasound-detected effusion-synovitis. Participants with more severe effusion-synovitis load their ACLR limb more rapidly. This study highlights the utility of clinically accessible techniques in assessing inflammation and walking biomechanics in ACLR patients.