Quantitative 3D imaging parameters improve prediction of hip osteoarthritis outcome

Systematic review of computed tomography parameters used for the assessment of subchondral bone in osteoarthritis

OBJECTIVE

To systematically review the published parameters for the assessment of subchondral bone in human osteoarthritis (OA) using computed tomography (CT) and gain an overview of current practices and standards.

DESIGN

A literature search of Medline, Embase and Cochrane Library databases was performed with search strategies tailored to each database (search from 2010 to January 2023). The search results were screened independently by two reviewers against pre-determined inclusion and exclusion criteria. Studies were deemed eligible if conducted in vivo/ex vivo in human adults (>18 years) using any type of CT to assess subchondral bone in OA. Extracted data from eligible studies were compiled in a qualitative summary and formal narrative synthesis.

RESULTS

This analysis included 202 studies. Four groups of CT modalities were identified to have been used for subchondral bone assessment in OA across nine anatomical locations. Subchondral bone parameters measuring similar features of OA were combined in six categories: (i) microstructure, (ii) bone adaptation, (iii) gross morphology (iv) mineralisation, (v) joint space, and (vi) mechanical properties.

CONCLUSIONS

Clinically meaningful parameter categories were identified as well as categories with the potential to become relevant in the clinical field. Furthermore, we stress the importance of quantification of parameters to improve their sensitivity and reliability for the evaluation of OA disease progression and the need for standardised measurement methods to improve their clinical value.

CartiMorph: A framework for automated knee articular cartilage morphometrics

We introduce CartiMorph, a framework for automated knee articular cartilage morphometrics. It takes an image as input and generates quantitative metrics for cartilage subregions, including the percentage of full-thickness cartilage loss (FCL), mean thickness, surface area, and volume. CartiMorph leverages the power of deep learning models for hierarchical image feature representation. Deep learning models were trained and validated for tissue segmentation, template construction, and template-to-image registration. We established methods for surface-normal-based cartilage thickness mapping, FCL estimation, and rule-based cartilage parcellation. Our cartilage thickness map showed less error in thin and peripheral regions. We evaluated the effectiveness of the adopted segmentation model by comparing the quantitative metrics obtained from model segmentation and those from manual segmentation. The root-mean-squared deviation of the FCL measurements was less than 8%, and strong correlations were observed for the mean thickness (Pearson's correlation coefficient ρ∈[0.82,0.97]), surface area (ρ∈[0.82,0.98]) and volume (ρ∈[0.89,0.98]) measurements. We compared our FCL measurements with those from a previous study and found that our measurements deviated less from the ground truths. We observed superior performance of the proposed rule-based cartilage parcellation method compared with the atlas-based approach. CartiMorph has the potential to promote imaging biomarkers discovery for knee osteoarthritis.

3-D joint space mapping at the ankle from weight-bearing CT: reproducibility, repeatability, and challenges for standardisation

OBJECTIVES

We present a 3-D approach to joint space width (JSW) measurement across the ankle from weight-bearing CT (WBCT) to demonstrate inter-operator reproducibility, test-retest repeatability, and how differences in angulation affect ankle JSW distribution.

METHODS

One side from repeat WBCT imaging of both feet and ankles was analysed from 23 individuals as part of their routine clinical care pathway. Joint space mapping was performed at four facets across the talus: talonavicular, talar dome and medial gutter (dome-medial), lateral gutter, and posterior subtalar. Inter-operator reproducibility was calculated for two users, while test-retest repeatability was calculated by comparing the two visits, both presented as Bland-Altman statistics. Statistical parametric mapping determined any significant relationships between talocrural joint space angulation and 3-D JSW distribution.

RESULTS

The average ± standard deviation interval between imaging was 74.0 ± 29.6 days. Surface averaged bias ± limits of agreement were similar for reproducibility and repeatability, the latter being: talonavicular 0.01 ± 0.26 mm, dome-medial 0.00 ± 0.28 mm, lateral gutter - 0.02 ± 0.40 mm, and posterior subtalar 0.02 ± 0.34 mm. Results are presented as 3-D distribution maps, with optimum test-retest repeatability reaching a smallest detectable difference of ± 0.15 mm.

CONCLUSIONS

Joint space mapping is a robust approach to 3-D quantification of JSW measurement, inter-operator reproducibility, and test-retest repeatability at the ankle, with sensitivity reaching a best value of ± 0.15 mm. Standardised imaging protocols and optimised metal artefact reduction will be needed to further understand the clinical value of these 3-D measures derived from WBCT.

CLINICAL RELEVANCE STATEMENT

Weight-bearing computed tomography is an increasingly important tool in the clinical assessment of orthopaedic ankle disorders. This paper establishes the performance of measuring 3-D joint space width using this technology, which is an important surrogate marker for severity of osteoarthritis.

KEY POINTS

• Joint space width values and error metrics from across the ankle measured from weight-bearing CT can be presented as 3-D maps that show topographic variation. • The best sensitivity for detecting meaningful change in 3-D joint space width at the ankle was ± 0.15 mm, a value less than the isotropic imaging voxel dimensions. • Standardised imaging protocols and optimised metal artefact reduction will be needed to understand the clinical value of 3-D measures from weight-bearing CT.

Application of statistical shape modeling to the human hip joint: a scoping review

OBJECTIVE

The objective of this scoping review was to identify all examples of the application of statistical shape models to the human hip joint, with a focus on applications, population, methodology, and validation.

INTRODUCTION

Clinical radiographs are the most common imaging tool for management of hip conditions, but it is unclear whether radiographs can adequately diagnose or predict outcomes of 3D deformity. Statistical shape modeling, a method of describing the variation of a population of shapes using a small number of variables, has been identified as a useful tool to associate 2D images with 3D anatomy. This could allow clinicians and researchers to validate clinical radiographic measures of hip deformity, develop new ones, or predict 3D morphology directly from radiographs. In identifying all previous examples of statistical shape modeling applied to the human hip joint, this review determined the prevalence, strengths, and weaknesses, and identified gaps in the literature.

INCLUSION CRITERIA

Participants included any human population. The concept included development or application of statistical shape models based on discrete landmarks and principal component analysis. The context included sources that exclusively modeled the hip joint. Only peer-reviewed original research journal articles were eligible for inclusion.

METHODS

We searched MEDLINE, Embase, Cochrane CENTRAL, IEEE Xplore, Web of Science Core Collection, OCLC PapersFirst, OCLC Proceedings, Networked Digital Library of Theses and Dissertations, ProQuest Dissertations and Theses Global, and Google Scholar for sources published in English between 1992 and 2021. Two reviewers screened sources against the inclusion criteria independently and in duplicate. Data were extracted by 2 reviewers using a REDCap form designed to answer the review study questions, and are presented in narrative, tabular, and graphical form.

RESULTS

A total of 104 sources were considered eligible based on the inclusion criteria. From these, 122 unique statistical shape models of the human hip were identified based on 86 unique training populations. Models were most often applied as one-off research tools to describe shape in certain populations or to predict outcomes. The demographics of training populations were skewed toward older patients in high-income countries. A mean age between 60 and 79 years was reported in 29 training populations (34%), more than reported in all other age groups combined, and 73 training populations (85%) were reported or inferred to be from Europe and the Americas. Only 4 studies created models in a pediatric population, although 15 articles considered shape variation over time in some way. There were approximately equal numbers of 2D and 3D models. A variety of methods for labeling the training set was observed. Most articles presented some form of validation such as reporting a model's compactness (n = 71), but in-depth validation was rare.

CONCLUSIONS

Despite the high volume of literature concerning statistical shape models of the human hip, there remains a need for further research in key areas. We identified the lack of models in pediatric populations and low- and middle-income countries as a notable limitation to be addressed in future research.

Hip osteoarthritis: A novel network analysis of subchondral trabecular bone structures

Hip osteoarthritis (HOA) is a degenerative joint disease that leads to the progressive destruction of subchondral bone and cartilage at the hip joint. Development of effective treatments for HOA remains an open problem, primarily due to the lack of knowledge of its pathogenesis and a typically late-stage diagnosis. We describe a novel network analysis methodology for microcomputed tomography (micro-CT) images of human trabecular bone. We explored differences between the trabecular bone microstructure of femoral heads with and without HOA. Large-scale automated extraction of the network formed by trabecular bone revealed significant network properties not previously reported for bone. Profound differences were discovered, particularly in the proximal third of the femoral head, where HOA networks demonstrated elevated numbers of edges, vertices, and graph components. When further differentiating healthy joint and HOA networks, the latter showed fewer small-world network properties, due to decreased clustering coefficient and increased characteristic path length. Furthermore, we found that HOA networks had reduced length of edges, indicating the formation of compressed trabecular structures. In order to assess our network approach, we developed a deep learning model for classifying HOA and control cases, and we fed it with two separate inputs: (i) micro-CT images of the trabecular bone, and (ii) the network extracted from them. The model with plain micro-CT images achieves 74.6% overall accuracy while the trained model with extracted networks attains 96.5% accuracy. We anticipate our findings to be a starting point for a novel description of bone microstructure in HOA, by considering the phenomenon from a graph theory viewpoint.

Subchondral bone changes after joint distraction treatment for end stage knee osteoarthritis

OBJECTIVE

Increased subchondral cortical bone plate thickness and trabecular bone density are characteristic of knee osteoarthritis (OA). Knee joint distraction (KJD) is a joint-preserving knee OA treatment where the joint is temporarily unloaded. It has previously shown clinical improvement and cartilage regeneration, indicating reversal of OA-related changes. The purpose of this research was to explore 3D subchondral bone changes after KJD treatment using CT imaging.

DESIGN

Twenty patients were treated with KJD and included to undergo knee CT imaging before, one, and two years after treatment. Tibia and femur segmentation and registration to canonical surfaces were performed semi-automatically. Cortical bone thickness and trabecular bone density were determined using an automated algorithm. Statistical parametric mapping (SPM) with two-tailed F-tests was used to analyze whole-joint changes.

RESULTS

Data was available of 16 patients. Subchondral cortical bone plate thickness and trabecular bone density were higher in the weight-bearing region of the most affected compartment (MAC; mostly medial). Especially the MAC showed a decrease in thickness and density in the first year after treatment, which was sustained towards the second year.

CONCLUSIONS

KJD treatment results in bone changes that include thinning of the subchondral cortical bone plate and decrease of subchondral trabecular bone density in the first two years after treatment, potentially indicating a partial normalization of subchondral bone.

Osteoarthritis year in review 2021: imaging

PURPOSE

To provide a narrative review of original articles on imaging of osteoarthritis (OA) published between January 1, 2020 and March 31, 2021, with a special focus on imaging of inflammation, imaging of bone, cartilage and bone-cartilage interactions, imaging of peri-articular tissues, imaging scoring methods for OA, and artificial intelligence (AI) applied to OA imaging.

METHODS

The Embase, Pubmed, Medline, Cochrane databases were searched for original research articles in the English language on human, in vivo, imaging of OA published between January 1, 2020 and March 31, 2021. Search terms related to osteoarthritis combined with all imaging modalities and artificial intelligence were applied. A selection of articles reporting on one of the focus topics was discussed further.

RESULTS

The search resulted in 651 articles, of which 214 were deemed relevant to human OA imaging. Among the articles included, the knee joint (69%) and magnetic resonance imaging (MRI) (52%) were the predominant anatomical area and imaging modality studied. There were also a substantial number of papers (n = 46) reporting on AI applications in the field of OA imaging.

CONCLUSION

Imaging continues to play an important role in the assessment of OA. Recent advances in OA imaging include quantitative, non-contrast, and hybrid imaging techniques for improved characterization of multiple tissue processes in OA. In addition, an increasing effort in AI techniques is undertaken to enhance OA imaging acquisition and analysis.

Bone in osteoarthritis: imaging and interventions

PURPOSE OF REVIEW

To review the recent literature on bone in osteoarthritis (OA), with a focus on imaging and intervention studies.

RECENT FINDINGS

Most studies focused on knee OA; hip and hand studies were uncommon. Bone shape studies demonstrated that shape changes precede radiographic OA, predict joint replacement, and have demonstrated high responsiveness. Novel quantitative 3D imaging markers (B-score) have better characterized OA severity, including preradiographic OA status. The addition of computerized tomography-derived 3D metrics has improved the prediction of hip joint replacement when compared to radiographs alone.Recent studies of bisphosphonates for knee OA have reported no benefits on pain or bone marrow lesion (BML) size. A meta-analysis on Vitamin D supplementation in knee OA suggested minimal symptom improvement and no benefits on the structure. Cathepsin K inhibition demonstrated reduction in OA bone change progression, but with no symptom benefit. Studies of injections of bone substitutes into BMLs (subchondroplasty) have generally been small and potential benefits remain unclear.

SUMMARY

Subchondral bone features are associated with pain, incidence and progression of OA. Recent studies have validated quantitative bone shape as a biomarker for OA trials. Trials of bone-targeted OA therapies have been disappointing although cathepsin K inhibition may slow structural progression.

Quantitative Three-dimensional Assessment of Knee Joint Space Width from Weight-bearing CT

Background Imaging of structural disease in osteoarthritis has traditionally relied on MRI and radiography. Joint space mapping (JSM) can be used to quantitatively map joint space width (JSW) in three dimensions from CT images. Purpose To demonstrate the reproducibility, repeatability, and feasibility of JSM of the knee using weight-bearing CT images. Materials and Methods Two convenience samples of weight-bearing CT images of left and right knees with radiographic Kellgren-Lawrence grades (KLGs) less than or equal to 2 were acquired from 2014 to 2018 and were analyzed retrospectively with JSM to deliver three-dimensional JSW maps. For reproducibility, images of three sets of knees were used for novice training, and then the JSM output was compared against an expert's assessment. JSM was also performed on 2-week follow-up images in the second cohort, yielding three-dimensional JSW difference maps for repeatability. Statistical parametric mapping was performed on all knee imaging data (KLG, 0-4) to show the feasibility of a surface-based analysis in three dimensions. Results Reproducibility (in 20 individuals; mean age, 58 years ± 7 [standard deviation]; mean body mass index, 28 kg/m² ± 6; 14 women) and repeatability (in nine individuals; mean age, 53 years ± 6; mean body mass index, 26 kg/m² ± 4; seven women) reached their lowest performance at a smallest detectable difference less than ±0.1 mm in the central medial tibiofemoral joint space for individuals without radiographically demonstrated disease. The average root mean square coefficient of variation was less than 5% across all groups. Statistical parametric mapping (33 individuals; mean age, 57 years ± 7; mean body mass index, 27 kg/m² ± 6; 23 women) showed that the central-to-posterior medial joint space was significantly narrower by 0.5 mm for each incremental increase in the KLG (threshold P < .05). One knee (KLG, 2) demonstrated a baseline versus 24-month change in its three-dimensional JSW distribution that was beyond the smallest detectable difference across the lateral joint space. Conclusion Joint space mapping of the knee using weight-bearing CT images is feasible, demonstrating a relationship between the three-dimensional joint space width distribution and structural joint disease. It is reliably learned by novice users, can be personalized for disease phenotypes, and can be used to achieve a smallest detectable difference that is at least 50% smaller than that reported to be achieved at the highest performance level in radiography. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Roemer in this issue.