Precision, Reliability, and Responsiveness of a Novel Automated Quantification Tool for Cartilage Thickness: Data from the Osteoarthritis Initiative

The design of a sample rapid magnetic resonance imaging (MRI) acquisition protocol supporting assessment of multiple articular tissues and pathologies in knee osteoarthritis

Objective

This expert opinion paper proposes a design for a state-of-the-art magnetic resonance image (MRI) acquisition protocol for knee osteoarthritis clinical trials in early and advanced disease. Semi-quantitative and quantitative imaging endpoints are supported, partly amendable to automated analysis. Several (peri-) articular tissues and pathologies are covered, including synovitis.

Method

A PubMed literature search was conducted, with focus on the past 5 years. Further, osteoarthritis imaging experts provided input. Specific MRI sequences, orientations, spatial resolutions and parameter settings were identified to align with study goals. We strived for implementation on standard clinical scanner hardware, with a net acquisition time ≤30 ​min.

Results

Short- and long-term longitudinal MRIs should be obtained at ≥1.5T, if possible without hardware changes during the study. We suggest a series of gradient- and spin-echo-sequences, supporting MOAKS, quantitative analysis of cartilage morphology and T2, and non-contrast-enhanced depiction of synovitis. These sequences should be properly aligned and positioned using localizer images. One of the sequences may be repeated in each participant (re-test), optimally at baseline and follow-up, to estimate within-study precision. All images should be checked for quality and protocol-adherence as soon as possible after acquisition. Alternative approaches are suggested that expand on the structural endpoints presented.

Conclusions

We aim to bridge the gap between technical MRI acquisition guides and the wealth of imaging literature, proposing a balance between image acquisition efficiency (time), safety, and technical/methodological diversity. This approach may entertain scientific innovation on tissue structure and composition assessment in clinical trials on disease modification of knee osteoarthritis.

Characterizing Osteophyte Formation in Knee Osteoarthritis: Application of Machine Learning Quantification of a Computerized Tomography Cohort: Implications for Treatment

BACKGROUND

Osteophytes are commonly used to diagnose and guide knee osteoarthritis (OA) treatment, but their causes are unclear. Although they are not typically the focus of knee arthroplasty surgeons, they can predict case difficulty and length. Furthermore, their extent and location may yield much information about the knee joint status. The aims of this computed tomography-based study in patients awaiting total or partial knee arthroplasty were to: (1) measure osteophyte volume in anatomical subregions and relative change as total volume increases; (2) determine whether medial and/or lateral OA affects osteophyte distribution; and (3) explore relationships between osteophytes and OA severity.

METHODS

Data were obtained from 4,928 computed tomography scans. Machine-learning-based imaging analyses enabled osteophyte segmentation and quantification, divided into anatomical regions. Mean three-dimensional joint space narrowing was assessed in medial and lateral compartments. A Bayesian model assessed the uniformity of osteophyte distribution. We correlated femoral osteophyte volumes with B-scores, a validated OA status measure.

RESULTS

Total tibial (25%) and femoral osteophyte volumes (75%) within each knee correlated strongly (R2 = 0.85). Medial osteophytes (65.3%) were larger than lateral osteophytes (34.6%), with similar proportions in both the femur and tibia. Osteophyte growth was found in all compartments, and as total osteophyte volume increased, the relative distribution of osteophytes between compartments did not markedly change. No evidence of variation was found in the regional distribution of osteophyte volume between knees with medial, lateral, both, or no three-dimensional joint space narrowing in the femur or tibia. There was a direct relationship between osteophyte volume and OA severity.

CONCLUSIONS

Osteophyte volume increased in both medial and lateral compartments proportionally with total osteophyte volume, regardless of OA location. The peripheral position of femoral osteophytes does not appear to contribute to load-bearing. This suggests that osteophytic growth represents a 'whole-knee'/global response. This work may have broad applications for knee OA, both surgically and nonoperatively.

30 Years of MRI-based cartilage & bone morphometry in knee osteoarthritis: From correlation to clinical trials

OBJECTIVE

The first publication on morphometric analysis of articular cartilage using magnetic resonance imaging (MRI) in 1994 set the scene for a game change in osteoarthritis (OA) research. The current review highlights milestones in cartilage and bone morphometry, summarizing the rapid progress made in imaging, its application to understanding joint (patho-)physiology, and its use in interventional clinical trials.

METHODS

Based on a Pubmed search of articles from 1994 to 2023, the authors subjectively selected representative work illustrating important steps in the development or application of magnetic resonance-based cartilage and bone morphometry, with a focus on studies in humans, and on the knee. Research on OA-pathophysiology is addressed only briefly, given length constraints. Compositional and semi-quantitative assessment are not covered here.

RESULTS

The selected articles are presented in historical order as well as by content. We review progress in the technical aspects of image acquisition, segmentation and analysis, advances in understanding tissue growth, physiology, function, and adaptation, and a selection of clinical trials examining the efficacy of interventions on knee cartilage and bone. A perspective is provided of how lessons learned may be applied to future research and clinical management.

CONCLUSIONS

Over the past 30 years, MRI-based morphometry of cartilage and bone has contributed to a paradigm shift in understanding articular tissue physiology and OA pathophysiology, and to the development of new treatment strategies. It is likely that these technologies will continue to play a key role in the development and (accelerated) approval of therapy, potentially targeted to different OA phenotypes.

Quantification of cartilage and subchondral bone cysts on knee specimens based on a spectral photon-counting computed tomography

Spectral photon-counting computed tomography (SPCCT) is a new technique with the capability to provide mono-energetic (monoE) images with high signal to noise ratio. We demonstrate the feasibility of SPCCT to characterize at the same time cartilage and subchondral bone cysts (SBCs) without contrast agent in osteoarthritis (OA). To achieve this goal, 10 human knee specimens (6 normal and 4 with OA) were imaged with a clinical prototype SPCCT. The monoE images at 60 keV with isotropic voxels of 250 × 250 × 250 µm3 were compared with monoE synchrotron radiation CT (SR micro-CT) images at 55 keV with isotropic voxels of 45 × 45 × 45 µm3 used as benchmark for cartilage segmentation. In the two OA knees with SBCs, the volume and density of SBCs were evaluated in SPCCT images. In 25 compartments (lateral tibial (LT), medial tibial, (MT), lateral femoral (LF), medial femoral and patella), the mean bias between SPCCT and SR micro-CT analyses were 101 ± 272 mm3 for cartilage volume and 0.33 mm ± 0.18 for mean cartilage thickness. Between normal and OA knees, mean cartilage thicknesses were found statistically different (0.005 < p < 0.04) for LT, MT and LF compartments. The 2 OA knees displayed different SBCs profiles in terms of volume, density, and distribution according to size and location. SPCCT with fast acquisitions is able to characterize cartilage morphology and SBCs. SPCCT can be used potentially as a new tool in clinical studies in OA.

Comparison of 3D quantitative osteoarthritis imaging biomarkers from paired CT and MR images: data from the IMI-APPROACH study

INTRODUCTION

MRI bone surface area and femoral bone shape (B-score) measures have been employed as quantitative endpoints in DMOAD clinical trials. Computerized Tomography (CT) imaging is more commonly used for 3D visualization of bony anatomy due to its high bone-soft tissue contrast. We aimed to compare CT and MRI assessments of 3D imaging biomarkers.

METHODS

We used baseline and 24-month image data from the IMI-APPROACH 2-year prospective cohort study. Femur and tibia were automatically segmented using active appearance models, a machine-learning method, to measure 3D bone shape, area and 3D joint space width (3DJSW). Linear regression was used to test for correlation between measures. Limits of agreement and bias were tested using Bland-Altman analysis.

RESULTS

CT-MR pairs of the same knee were available from 434 participants (78% female). B-scores from CT and MR were strongly correlated (CCC = 0.967) with minimal bias of 0.1 (SDD = 0.227). Area measures were also correlated but showed a consistent bias (MR smaller). 3DJSW showed different biases (MR larger) in both lateral and medial compartments.

DISCUSSION

The strong correlation and small B-score bias suggests that B-score may be measured reliably using either modality. It is likely that the bone surface identified using MR and CT will be at slightly different positions within the bone/cartilage boundary. The negative bone area bias suggests the MR bone boundary is inside the CT boundary producing smaller areas for MR, consistent with the positive 3DJSW bias. The lateral-medial 3DJSW difference is possibly due to a difference in knee pose during acquisition (extended for CT, flexed for MR).

TRIAL REGISTRATION

NCT03883568.

Cartilage Topography Assessment With Local-Area Cartilage Segmentation for Knee Magnetic Resonance Imaging

OBJECTIVE

Local-area cartilage segmentation (LACS) software was developed to segment medial femur (MF) cartilage on magnetic resonance imaging (MRI). Our objectives were 1) to extend LACS to the lateral femur (LF), medial tibia (MT), and lateral tibia (LT), 2) to compare LACS to an established manual segmentation method, and 3) to visualize cartilage responsiveness over each cartilage plate.

METHODS

Osteoarthritis Initiative participants with symptomatic knee osteoarthritis (OA) were selected, including knees selected at random (n = 40) and knees identified with loss of cartilage based on manual segmentation (Chondrometrics GmbH), an enriched sample of 126 knees. LACS was used to segment cartilage in the MF, LF, MT, and LT on sagittal 3D double-echo steady-state MRI scans at baseline and at 2-year follow-up. We compared LACS and Chondrometrics average thickness measures by estimating the correlation in each cartilage plate and estimating the standardized response mean (SRM) for 2-year cartilage change. We illustrated cartilage loss topographically with SRM heatmaps.

RESULTS

The estimated correlation between LACS and Chondrometrics measures was r = 0.91 (95% confidence interval [95% CI] 0.86, 0.94) for LF, r = 0.93 (95% CI 0.89, 0.95) for MF, r = 0.97 (95% CI 0.96, 0.98) for LT, and r = 0.87 (95% CI 0.81, 0.91) for MT. Estimated SRMs for LACS and Chondrometrics measures were similar in the random sample, and SRM heatmaps identified subregions of LACS-measured cartilage loss.

CONCLUSION

LACS cartilage thickness measurement in the MF and LF and tibia correlated well with established manual segmentation-based measurement, with similar responsiveness to change, among knees with symptomatic knee OA. LACS measurement of cartilage plate topography enables spatiotemporal analysis of cartilage loss in future knee OA studies.

Imaging in Osteoarthritis

Osteoarthritis (OA) is the most frequent form of arthritis with major implications on both individual and public health care levels. The field of joint imaging, and particularly magnetic resonance imaging (MRI), has evolved rapidly due to the application of technical advances to the field of clinical research. This narrative review will provide an introduction to the different aspects of OA imaging aimed at an audience of scientists, clinicians, students, industry employees, and others who are interested in OA but who do not necessarily focus on OA. The current role of radiography and recent advances in measuring joint space width will be discussed. The status of cartilage morphology assessment and evaluation of cartilage biochemical composition will be presented. Advances in quantitative three-dimensional morphologic cartilage assessment and semi-quantitative whole-organ assessment of OA will be reviewed. Although MRI has evolved as the most important imaging method used in OA research, other modalities such as ultrasound, computed tomography, and metabolic imaging play a complementary role and will also be discussed.

Deep Learning-Based Image Reconstruction for Different Medical Imaging Modalities

Image reconstruction in magnetic resonance imaging (MRI) and computed tomography (CT) is a mathematical process that generates images at many different angles around the patient. Image reconstruction has a fundamental impact on image quality. In recent years, the literature has focused on deep learning and its applications in medical imaging, particularly image reconstruction. Due to the performance of deep learning models in a wide variety of vision applications, a considerable amount of work has recently been carried out using image reconstruction in medical images. MRI and CT appear as the ultimate scientifically appropriate imaging mode for identifying and diagnosing different diseases in this ascension age of technology. This study demonstrates a number of deep learning image reconstruction approaches and a comprehensive review of the most widely used different databases. We also give the challenges and promising future directions for medical image reconstruction.

Studying osteoarthritis with artificial intelligence applied to magnetic resonance imaging

The 3D nature and soft-tissue contrast of MRI makes it an invaluable tool for osteoarthritis research, by facilitating the elucidation of disease pathogenesis and progression. The recent increasing employment of MRI has certainly been stimulated by major advances that are due to considerable investment in research, particularly related to artificial intelligence (AI). These AI-related advances are revolutionizing the use of MRI in clinical research by augmenting activities ranging from image acquisition to post-processing. Automation is key to reducing the long acquisition times of MRI, conducting large-scale longitudinal studies and quantitatively defining morphometric and other important clinical features of both soft and hard tissues in various anatomical joints. Deep learning methods have been used recently for multiple applications in the musculoskeletal field to improve understanding of osteoarthritis. Compared with labour-intensive human efforts, AI-based methods have advantages and potential in all stages of imaging, as well as post-processing steps, including aiding diagnosis and prognosis. However, AI-based methods also have limitations, including the arguably limited interpretability of AI models. Given that the AI community is highly invested in uncovering uncertainties associated with model predictions and improving their interpretability, we envision future clinical translation and progressive increase in the use of AI algorithms to support clinicians in optimizing patient care.

Towards understanding mechanistic subgroups of osteoarthritis: 8-year cartilage thickness trajectory analysis

Many studies have validated cartilage thickness as a biomarker for knee osteoarthritis (OA); however, few studies investigate beyond cross-sectional observations or comparisons across two timepoints. By characterizing the trajectory of cartilage thickness changes over 8 years in healthy individuals from the OA initiative data set, this study discovers associations between the dynamics of cartilage changes and OA incidence. A fully automated cartilage segmentation and thickness measurement method were developed and validated against manual measurements: mean absolute error = 0.11-0.14 mm (n = 4129 knees) and automatic reproducibility = 0.04-0.07 mm (n = 316 knees). The mean thickness for the medial and lateral tibia (MT, LT), central weight-bearing medial and lateral femur (cMF, cLF), and patella (P) cartilage compartments were quantified for 1453 knees at seven timepoints. Trajectory subgroups were defined per cartilage compartment such as stable, thinning to thickening, accelerated thickening, plateaued thickening, thickening to thinning, accelerated thinning, or plateaued thinning. For tibiofemoral compartments, the stable (22%-36%) and plateaued thinning (22%-37%) trajectories were the most common, with an average initial velocity (μm/month), acceleration (μm/month² ) for the plateaued thinning trajectories LT: -2.66, 0.0326; MT: -2.49, 0.0365; cMF: -3.51, 0.0509; and cLF: -2.68, 0.041. In the patella compartment, the plateaued thinning (35%) and thickening to thinning (24%) trajectories were the most common, with an average initial velocity, acceleration for each -4.17, 0.0424; 1.95, -0.0835. Knees with nonstable trajectories had higher adjusted odds of OA incidence than stable trajectories: accelerated thickening, accelerated thinning, and plateaued thinning trajectories of the MT had adjusted odds ratio (OR) of 18.9, 5.48, and 1.47, respectively; in the cMF, adjusted OR of 8.55, 10.1, and 2.61, respectively.

Osteoarthritis year in review 2020: imaging

This narrative "Year in Review" highlights a selection of articles published between January 2019 and April 2020, to be presented at the OARSI World Congress 2020 within the field of osteoarthritis (OA) imaging. Articles were obtained from a PubMed search covering the above period, utilizing a variety of relevant search terms. We then selected original and review studies on OA-related imaging in humans, particularly those with direct clinical relevance, with a focus on the knee. Topics selected encompassed clinically relevant models of early OA, particularly imaging applications on cruciate ligament rupture, as these are of direct clinical interest and provide potential opportunity to evaluate preventive therapy. Further, imaging applications on structural modification of articular tissues in patients with established OA, by non-pharmacological, pharmacological and surgical interventions are summarized. Finally, novel deep learning approaches to imaging are reviewed, as these facilitate implementation and scaling of quantitative imaging application in clinical trials and clinical practice. Methodological or observational studies outside these key focus areas were not included. Studies focused on biology, biomechanics, biomarkers, genetics and epigenetics, and clinical studies that did not contain an imaging component are covered in other articles within the OARSI "Year in Review" series. In conclusion, exciting progress has been made in clinically validating human models of early OA, and the field of automated articular tissue segmentation. Most importantly though, it has been shown that structure modification of articular cartilage is possible, and future research should focus on the translation of these structural findings to clinical benefit.

A Deep Learning Automated Segmentation Algorithm Accurately Detects Differences in Longitudinal Cartilage Thickness Loss - Data from the FNIH Biomarkers Study of the Osteoarthritis Initiative

Objective

To study the longitudinal performance of fully automated cartilage segmentation in knees with radiographic osteoarthritis (OA), we evaluated the sensitivity to change in progressor knees from the Foundation for the National Institutes of Health OA Biomarkers Consortium between the automated and previously reported manual expert segmentation, and we determined whether differences in progression rates between predefined cohorts can be detected by the fully automated approach.

Methods

The OA Initiative Biomarker Consortium was a nested case–control study. Progressor knees had both medial tibiofemoral radiographic joint space width loss (≥0.7 mm) and a persistent increase in Western Ontario and McMaster Universities Osteoarthritis Index pain scores (≥9 on a 0–100 scale) after 2 years from baseline (n = 194), whereas non‐progressor knees did not have either of both (n = 200). Deep‐learning automated algorithms trained on radiographic OA knees or knees of a healthy reference cohort (HRC) were used to automatically segment medial femorotibial compartment (MFTC) and lateral femorotibial cartilage on baseline and 2‐year follow‐up magnetic resonance imaging. Findings were compared with previously published manual expert segmentation.

Results

The mean ± SD MFTC cartilage loss in the progressor cohort was –181 ± 245 μm by manual segmentation (standardized response mean [SRM] –0.74), –144 ± 200 μm by the radiographic OA–based model (SRM –0.72), and –69 ± 231 μm by HRC‐based model segmentation (SRM –0.30). Cohen's d for rates of progression between progressor versus the non‐progressor cohort was –0.84 (P < 0.001) for manual, –0.68 (P < 0.001) for the automated radiographic OA model, and –0.14 (P = 0.18) for automated HRC model segmentation.

Conclusion

A fully automated deep‐learning segmentation approach not only displays similar sensitivity to change of longitudinal cartilage thickness loss in knee OA as did manual expert segmentation but also effectively differentiates longitudinal rates of loss of cartilage thickness between cohorts with different progression profiles.

Automated MRI assessment confirms cartilage thickness modification in patients with knee osteoarthritis: post-hoc analysis from a phase II sprifermin study

BACKGROUND

Sprifermin is under investigation as a potential disease-modifying osteoarthritis drug. Previously, 2-year results from the FORWARD study showed significant dose-dependent modification of cartilage thickness in the total femorotibial joint (TFTJ), medial and lateral femorotibial compartments (MFTC, LFTC), and central medial and lateral TFTJ subregions, by quantitative magnetic resonance imaging (qMRI) using manual segmentation.

OBJECTIVE

To determine whether qMRI findings from FORWARD could be reproduced by an independent method of automated segmentation using an identical dataset and similar anatomical regions in a post-hoc analysis.

METHOD

Cartilage thickness was assessed at baseline and 6, 12, 18 and 24 months, using automated cartilage segmentation with active appearance models, a supervised machine learning method. Images were blinded for treatment and timepoint. Treatment effect was assessed by observed and adjusted changes using a linear mixed model for repeated measures.

RESULTS

Based on automated segmentation, statistically significant, dose-dependent structural modification of cartilage thickness was observed over 2 years with sprifermin vs placebo for TFTJ (overall treatment effect and dose response, both P < 0.001), MFTC (P = 0.004 and P = 0.044), and LFTC (both P < 0.001) regions. For highest dose, in the central medial tibial (P = 0.008), central lateral tibial (P < 0.001) and central lateral femoral (P < 0.001) regions.

CONCLUSIONS

Cartilage thickness assessed by automated segmentation provided a consistent dose response in structural modification compared with manual segmentation. This is the first time that two independent quantification methods of image analysis have reached the same conclusions in an interventional trial, strengthening the conclusions that sprifermin modifies structural progression in knee osteoarthritis.

Three-Dimensional Surface-Based Analysis of Cartilage MRI Data in Knee Osteoarthritis: Validation and Initial Clinical Application

BACKGROUND

Traditional quantitative analysis of cartilage with MRI averages measurements (eg, thickness) across regions-of-interest (ROIs) which may reduce responsiveness.

PURPOSE

To validate and describe clinical application of a semiautomated surface-based method for analyzing cartilage relaxation times ("composition") and morphology on MRI, 3D cartilage surface mapping (3D-CaSM).

STUDY TYPE

Validation study in cadaveric knees and prospective observational (cohort) study in human participants.

POPULATION

Four cadaveric knees and 14 participants aged 40-60 with mild-moderate knee osteoarthritis (OA) and 6 age-matched healthy volunteers, imaged at baseline, 1, and 6 months.

FIELD STRENGTH/SEQUENCE

3D spoiled gradient echo, T1 rho/T2 magnetization-prepared 3D fast spin echo for mapping of T1 rho/T2 relaxation times and delayed gadolinium enhanced MRI of cartilage (dGEMRIC) using variable flip angle T1 relaxation time mapping at 3T.

ASSESSMENT

3D-CaSM was validated against high-resolution peripheral quantitative computed tomography (HRpQCT) in cadaveric knees, with comparison to expert manual segmentation. The clinical study assessed test-retest repeatability and sensitivity to change over 6 months for cartilage thickness and relaxation times.

STATISTICAL TESTS

Bland-Altman analysis was performed for the validation study and evaluation of test-retest repeatability. Six-month changes were assessed via calculation of the percentage of each cartilage surface affected by areas of significant change (%SC), defined using thresholds based on area and smallest detectable difference (SDD).

RESULTS

Bias and precision (0.06 ± 0.25 mm) of 3D-CaSM against reference HRpQCT data were comparable to expert manual segmentation (-0.13 ± 0.26 mm). 3D-CaSM demonstrated significant (>SDD) 6-month changes in cartilage thickness and relaxation times in both OA participants and healthy controls. The parameter demonstrating the greatest 6-month change was T2 relaxation time (OA median %SC [IQR] = 8.8% [5.5 to 12.6]).

DATA CONCLUSION

This study demonstrates the construct validity and potential clinical utility of 3D-CaSM, which may offer advantages to conventional ROI-based methods.

LEVEL OF EVIDENCE

2.

TECHNICAL EFFICACY STAGE

2. J. Magn. Reson. Imaging 2020;52:1139-1151.